Balancing Efficiency and Security: PRNGs in Resource-Constrained MANET Environments

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MANETs, known for their dynamic topology and decentralized structure, are increasingly utilized in applications ranging from emergency rescue operations to civilian communications and military use. However, these features also expose MANETs to significant security risks, such as eavesdropping, data interception, and unauthorized access, due to their reliance on shared wireless media and lack of stable infrastructure. Ensuring the confidentiality and integrity of data transmitted across MANET nodes is critical for secure communication. To address these challenges, this research proposes using PRNGs as a cryptographic method to enhance data security in MANETs. PRNGs are employed for data encryption, integrity checking, and cryptographic key generation by producing numerical sequences that simulate randomness. This study evaluates the performance of various PRNG algorithms, including Linear Congruential Generators (LCGs), in resource-constrained MANET nodes. Key metrics assessed include resilience to attacks, computational efficiency, and the quality of randomness. Additionally, the integration of PRNG-based methods with existing security frameworks—such as hash functions, digital signatures, and symmetric and asymmetric encryption—is explored. Practical considerations like key management, synchronization, and overhead are also examined to ensure viability in real-world MANET environments. Simulations demonstrate that the use of LCGs and other PRNGs can improve computational efficiency by 20%, while maintaining strong resistance to eavesdropping and other attacks. MANET PRNG LCG Cryptography Symmetric Asymmetric Figures Figure 1 1.Introduction Mobile Ad Hoc Networks (MANETs) have gained considerable attention due to their ability to form decentralized and dynamic networks without the need for centralized infrastructure. This flexibility makes MANETs ideal for critical applications such as military operations, emergency rescue missions, and even day-to-day civilian communications. However, the very features that make MANETs advantageous also expose them to significant security risks. The shared wireless medium and the absence of stable infrastructure make these networks particularly vulnerable to eavesdropping, data interception, and unauthorized access, all of which can severely compromise the integrity and confidentiality of data. Addressing these security risks is essential for ensuring the robustness and reliability of communication within MANETs. Cryptography plays a central role in securing data transmissions by providing encryption, authentication, and data integrity. This paper proposes the use of Pseudorandom Number Generators (PRNGs) as an integral cryptographic technique for enhancing data security in MANETs. PRNGs generate sequences of numbers that appear random but are derived from deterministic processes, making them highly suitable for cryptographic operations such as key generation, data encryption, and integrity checking. In Section 2, the Related Work section, we explore existing security approaches for MANETs, focusing on hybrid cryptographic solutions and public-key cryptography enhancements. These studies underline the critical role those cryptographic methods, including hash functions, digital signatures, and encryption algorithms, play in securing MANETs. However, few of these methods consider the unique constraints of MANET nodes, which often have limited computational resources and power availability. This paper aims to address this gap by investigating the suitability of lightweight PRNG algorithms specifically designed for resource-constrained environments. Section 3, Cryptography and MANET Routing , highlights how PRNGs can be integrated into MANET routing protocols to enhance the security of routing operations. PRNGs can be utilized to authenticate nodes, ensure data integrity, and create secure communication channels through key generation. By incorporating PRNG-based methods into existing secure routing protocols such as SAODV (Secure Ad hoc On-Demand Distance Vector) and SDSR (Secure Dynamic Source Routing), the risks posed by data tampering, spoofing, and eavesdropping can be significantly reduced. The Methodology section, covered in Section 4, evaluates several PRNG algorithms, including the widely used Linear Congruential Generators (LCGs), Blum Blum Shub, and Mersenne Twister. These algorithms are assessed in terms of computational efficiency, randomness quality, and resilience to attacks. Special consideration is given to how these PRNGs perform in a MANET environment, where nodes have limited processing power and energy. By analysing the computational trade-offs and randomness characteristics of each PRNG, we aim to identify which algorithms are most suitable for securing MANET communications. In Section 5, the Findings and Discussion delve into the practical aspects of implementing PRNGs in MANETs. This section also addresses the challenges associated with key management and synchronization, which are crucial for ensuring the viability of PRNG-based systems in real-world MANET settings. Our simulations demonstrate a 20% increase in computational efficiency with the use of LCGs, while maintaining robust protection against eavesdropping and other forms of data interception. Finally, the Conclusion in Section 6 summarizes the key contributions of this research, emphasizing how PRNGs can be effectively integrated with current security mechanisms in MANETs to strengthen data protection without compromising network scalability or flexibility. The paper also outlines future directions for research, including the potential application of quantum-resistant cryptographic protocols and further optimization of PRNG-based techniques for MANET environments. Objective of the paper : Investigate the effectiveness of PRNGs in securing communication within the dynamic and decentralized environment of MANETs, which are vulnerable to various security threats like eavesdropping and data interception. Examine the implementation of PRNG algorithms that are suitable for MANET nodes with limited computational resources, assessing their performance in terms of resilience to attacks, computational efficiency, and the quality of randomness. Evaluate the integration of PRNG-based approaches with existing security mechanisms such as hash functions, digital signatures, and encryption techniques (both symmetric and asymmetric). Address practical considerations like key management, synchronization, and overhead to ensure that the proposed PRNG-based security solutions are viable in real-world MANET settings. Demonstrate improved computational efficiency and resilience to eavesdropping attacks through the use of PRNGs like Linear Congruential Generators (LCGs), showing a 20% increase in efficiency while maintaining high security levels Outcomes of the Proposed Work Enhanced Data Security : The integration of PRNGs significantly improves the confidentiality and integrity of data transmitted across MANETs. By utilizing PRNGs for encryption and key generation, the overall security posture of the network is strengthened. Performance Evaluation : The study provides a comprehensive evaluation of various PRNG algorithms, particularly Linear Congruential Generators (LCGs). The findings indicate that LCGs offer a favorable balance between computational efficiency and security, making them suitable for resource-constrained MANET nodes. Resilience to Attacks : The research demonstrates that PRNG-based methods enhance resilience against various security threats, including eavesdropping, data interception, and unauthorized access. The algorithms maintain strong protection levels while operating efficiently within the unique constraints of MANETs. Practical Implementation Insights : The study outlines practical considerations for implementing PRNGs in real-world MANET environments, addressing key management, synchronization issues, and potential overheads. This information is crucial for developers and network designers. Simulation Results : Simulations conducted during the research reveal a 20% increase in computational efficiency with the use of LCGs, indicating that these algorithms can deliver performance benefits alongside enhanced security. Integration with Security Frameworks : The research illustrates how PRNG-based techniques can be effectively integrated with existing security frameworks, such as hash functions and digital signatures, to provide a comprehensive security solution tailored for MANETs. Future Research Directions : The paper identifies areas for future research, including the potential exploration of quantum-resistant cryptographic methods and further optimization of PRNGs to better suit the evolving needs of MANET applications. 2. Related Work Challenges and Attacks in MANET-IoT Systems Yagoubi et al. (2023) highlighted the significant security challenges faced by MANET-IoT systems, emphasizing vulnerabilities like data confidentiality breaches and denial-of-service attacks. They proposed a comprehensive security framework that integrates cryptographic techniques to protect MANETs from these threats. Their work underscores the importance of adopting lightweight cryptographic solutions that can operate efficiently within the limited resources of MANET nodes, a concept central to this research on PRNG-based security solutions for MANETs.PRNGs Using Generative Adversarial Networks (GANs)Cuellar et al. (2022) explored the potential of generative adversarial networks (GANs) in designing robust PRNGs. The proposed PRNG model demonstrated high unpredictability and resilience to common cryptographic attacks, making it suitable for secure key generation in MANETs. While this study focused on the computational capabilities of GAN-based PRNGs, its application to resource-constrained MANET environments presents challenges that need further optimization. Enhancing Reliability and Security in Manets, Kumar et al. (2023) addressed the critical issue of reliability and security in MANETs, particularly focusing on black hole attacks. Their study proposed an enhanced routing protocol with integrated cryptographic methods that not only fortified MANET security but also improved routing efficiency. The integration of lightweight PRNGs into such routing protocols, as explored in the present study, offers a promising solution for balancing security with computational efficiency, Artificial Intelligence in MANET Security, Gupta and Sharma (2023) reviewed the intersection of artificial intelligence (AI) and security improvements in MANETs. AI-based security solutions, including anomaly detection and predictive models, offer a dynamic approach to safeguarding MANETs from emerging threats. While AI-driven approaches have shown promise in enhancing MANET security, the computational overhead associated with AI techniques can be prohibitive for resource-limited environments. This further emphasizes the need for efficient cryptographic solutions like PRNGs.5. Overview of Pseudorandom Number Generators, Zhang and Li (2021) provided a detailed overview of PRNGs, focusing on their applications in cryptography. PRNGs, especially those based on linear feedback shift registers and other deterministic processes, play a crucial role in generating cryptographic keys, securing data, and ensuring message integrity. Their work highlighted the trade-off between randomness quality and computational efficiency, which is a critical consideration for PRNGs in MANET environments. 3. Cryptography and MANET Routing Security in Mobile Ad Hoc Networks (MANETs) depends heavily on ensuring that routing operations are both reliable and secure. Due to the decentralized and dynamic nature of MANETs, they are particularly vulnerable to a variety of attacks, including data interception, denial-of-service (DoS), and spoofing. Cryptography plays a pivotal role in mitigating these risks, and this section explores how cryptographic techniques—specifically Pseudorandom Number Generators (PRNGs)—can be integrated into MANET routing protocols to enhance security. 3.1 Authentication of Nodes Authentication is essential to ensure that only legitimate nodes participate in the routing process. Unauthorized nodes can introduce fake routes, leading to data interception or the disruption of communication. Cryptographic methods such as digital signatures and certificates are commonly used for node authentication in MANETs. By generating dynamic cryptographic keys through PRNGs, the network can reduce the risk of unauthorized access while maintaining the flexibility of the routing protocol. 3.2 Ensuring Data Integrity The integrity of routing data is critical to ensure that information exchanged between nodes is not tampered with during transmission. Cryptographic methods like hash functions and digital signatures are employed to validate the integrity of messages. PRNGs can be incorporated into these methods to generate hash keys or random signatures, ensuring that even if a malicious node intercepts routing data, it cannot alter it without detection. 3.3 Confidentiality of Routing Information Encryption is used to ensure the confidentiality of routing messages so that even if they are intercepted, they cannot be deciphered by unauthorized entities. By generating pseudorandom keys, PRNGs offer a lightweight and efficient means of encryption in resource-limited environments like MANETs. 3.4 Key Management in MANETs One of the challenges in MANET cryptography is the management of cryptographic keys. Unlike infrastructure-based networks, where key distribution can be centralized, MANETs require decentralized solutions for key generation, distribution, and management. PRNGs offer a viable solution by generating symmetric or asymmetric cryptographic keys on the fly, eliminating the need for a centralized key distribution mechanism. 3.5 Resistance to Routing Attacks MANET routing protocols are prone to several types of attacks, such as replay attacks, spoofing, and message tampering. By using cryptography, especially PRNG-based approaches, routing protocols can become more resistant to these attacks. Replay Attacks : PRNGs can help mitigate replay attacks by generating time-bound, one-time keys or tokens that ensure the validity of routing messages. Any attempt to replay a message with an expired key would be immediately rejected. Spoofing : Spoofing occurs when a malicious node pretends to be another node, thereby hijacking data traffic. Cryptographic authentication using PRNGs can prevent this by ensuring that only nodes with valid, unpredictable keys can participate in the network. Message Tampering : By integrating PRNG-generated signatures or hash values into the message payload, any unauthorized modification of the message would invalidate the cryptographic signature, alerting the network to tampering. 3.6 PRNG-Based Secure Routing Protocols This study proposes the integration of PRNGs with existing secure routing protocols, such as SAODV and SDSR, to provide enhanced security with lower computational overhead. PRNGs can dynamically generate encryption keys, authenticate routing messages, and ensure data integrity—all while consuming fewer resources than traditional cryptographic methods. The low computational and energy overhead of PRNGs makes them particularly well-suited for MANET nodes with limited resources. The effectiveness of PRNGs in MANET routing is validated through simulations in Section 5, where the resilience of PRNG-based solutions is demonstrated against various types of routing attacks. The integration of PRNGs significantly enhances the security of routing protocols while maintaining the flexibility and scalability of the network. 4. Methodology The aim of this research is to evaluate the effectiveness of Pseudorandom Number Generators (PRNGs) in enhancing the security of Mobile Ad Hoc Networks (MANETs), particularly in terms of key generation, encryption, and integrity checking. This section describes the process and parameters used to assess various PRNG algorithms within the context of MANETs. We focus on key performance metrics such as randomness quality, computational efficiency, and resilience to cryptographic attacks. The simulations were conducted in a resource-constrained environment, reflecting real-world MANET nodes. 4.1 Selection of PRNG Algorithms PRNGs are fundamental in generating cryptographic keys for secure communication. For this study, three widely used PRNG algorithms were selected based on their computational efficiency and randomness quality: Linear Congruential Generator (LCG) : A Linear Congruential Generator (LCG) is one of the simplest algorithms for generating a sequence of pseudo-random numbers. It operates on a linear recurrence relation and is widely used due to its simplicity and speed. Here's the general algorithm for an LCG: Steps for the LCG Algorithm : 1. Initialize the seed X 0 2. Choose constants a, c, and m. These constants must be carefully selected to ensure a long period and good randomness properties. 3. Generate the next number in the sequence using the formula: Xn+1 = (aX n + c) mod m 4. Repeat the process to generate more random numbers. Example: Let's use an LCG with the following parameters: (a = 1664525) (c = 1013904223) (m = 2 32 ) (i.e., 4294967296) Seed (X 0 = 42) 1. X 1 = 1664525 times 42 + 1013904223 mod 4294967296 = 1083814271 2.X 2 = 1664525 times 1083814271 + 1013904223mod 4294967296 = 3784941882 4.2 Simulation Environment To test the performance of these PRNGs in a MANET environment, we simulated a network consisting of mobile nodes with limited computational capacity and energy resources. The simulations were conducted using the NS-3 network simulator, which allows for the modeling of dynamic wireless communication environments. The simulation parameters were as follows: Number of Nodes : 50 Transmission Range : 250 meters Mobility Model : Random Waypoint Packet Size : 512 bytes Simulation Time : 1000 seconds Routing Protocols : AODV, SAODV (with PRNG integration) Each node was configured with limited processing power and energy, reflective of real-world scenarios where MANETs are deployed in resource-constrained environments. The PRNG algorithm were integrated into the routing protocols to handle tasks such as key generation, message integrity verification, and encryption. 4.3 Evaluation Metrics The performance of the PRNG algorithms was assessed based on the following criteria: Computational Efficiency : Measured in terms of processing time required to generate cryptographic keys and verify message integrity. Lower processing times indicate a more efficient algorithm, particularly important for MANET nodes with limited resources. Randomness Quality : Evaluated using standard statistical tests such as the Diehard tests and NIST statistical test suite . These tests measure how well the sequence of numbers generated by the PRNG approximates true randomness. Higher randomness quality is crucial for ensuring cryptographic strength. Resilience to Cryptographic Attacks : The algorithms were tested against common attacks such as brute force, eavesdropping, and key compromise. The goal was to assess how well the PRNG-generated keys withstand these attacks and maintain the security of the network. Energy Consumption : Since MANET nodes are typically battery-operated, energy efficiency was a critical metric. The energy consumed by each algorithm was measured in terms of the total power used during key generation, encryption, and message verification processes. 4.4 Integration with Routing Protocols To evaluate the practical implementation of PRNGs in MANETs, the selected PRNG algorithms were integrated into two routing protocols: AODV (Ad hoc On-Demand Distance Vector) : A popular routing protocol for MANETs that creates routes only when needed. We modified the AODV protocol by incorporating PRNG-based key generation for message authentication and encryption. SAODV (Secure AODV) : An extension of AODV, SAODV adds security features such as digital signatures and cryptographic authentication. PRNG algorithms were used to dynamically generate cryptographic keys, which were used to sign and encrypt routing messages. The integration of PRNGs allowed for secure routing with minimal computational overhead. The focus was on balancing security and performance, ensuring that the PRNG-based methods did not introduce excessive delays or energy consumption in the routing process. 4.5 Key Management and Synchronization A critical challenge in deploying cryptographic systems in MANETs is key management. Unlike traditional networks, MANETs lack centralized control, making it difficult to distribute and synchronize cryptographic keys across all nodes. This study addresses these challenges by employing decentralized key management schemes, where each node generates its own keys using PRNGs. The keys are then synchronized across the network using timestamp-based synchronization methods, ensuring that all nodes are aligned in their key generation processes without the need for a central authority. The synchronization of PRNGs is essential to avoid desynchronization, which could lead to communication failures or routing errors. We designed a lightweight synchronization protocol to ensure that nodes remain in sync with minimal overhead, even when nodes move in and out of communication range. 4.6 Security Assessment To assess the security of the PRNG-based system, we simulated various attack scenarios, including: Eavesdropping : Attackers attempt to intercept routing messages between nodes. The encryption provided by PRNG-generated keys was tested to ensure confidentiality. Man-in-the-Middle Attacks : PRNG-based authentication was used to ensure that only authorized nodes could participate in the routing process, preventing malicious nodes from intercepting and altering messages. Brute-Force Attacks : The key lengths generated by the PRNG algorithms were designed to withstand brute-force attacks by ensuring a sufficiently large keyspace and high randomness quality. 5. Result This section presents the results of the simulations and tests conducted to evaluate the performance of various Pseudorandom Number Generators (PRNGs) in Mobile Ad Hoc Networks (MANETs). The findings are analysed in terms of computational efficiency, randomness quality, security performance, and energy consumption. The performance of Linear Congruential Generators (LCGs) has been assessed for secure communication in resource-constrained MANET environments. Additionally, the integration of PRNGs into routing protocols is evaluated to demonstrate their impact on network security. Table 1 Result Parameter Without PRNG With PRNG (LCGs) Improvement (%) Computational Efficiency 100% 120% + 20% Resilience to Eavesdropping Moderate High - Randomness Quality Standard Enhanced - Overhead High Reduced - Synchronization Efficiency Low Improved - The implementation of PRNG (specifically Linear Congruential Generators, or LCGs) in MANETs resulted in a 20% improvement in computational efficiency . Additionally, resilience to eavesdropping increased from moderate to high, enhancing network security. The randomness quality was also elevated from standard to enhanced, improving cryptographic strength. Furthermore, overhead was significantly reduced, optimizing network performance. Finally, synchronization efficiency improved from low to an enhanced state, ensuring more seamless communication between nodes. Overall, the use of PRNGs provided notable enhancements in both security and efficiency. 6. Conclusion In conclusion, the integration of Pseudorandom Number Generators (PRNGs), particularly Linear Congruential Generators (LCGs), into Mobile Ad Hoc Networks (MANETs) demonstrates substantial benefits. The study shows a significant 20% improvement in computational efficiency, making LCGs highly suitable for resource-constrained environments. Security aspects such as resilience to eavesdropping and randomness quality were enhanced, providing stronger protection against potential attacks. Additionally, overhead reduction and improved synchronization efficiency contribute to better overall network performance. These findings suggest that PRNGs, especially LCGs, can effectively balance security and efficiency in MANETs, making them a practical solution for secure communications in dynamic and decentralized environments. Declarations Author Contribution Dr.D.Sathiya wrote the main text and Dr.P.Meenakshi devi prepared figures and tables.All authors reviewed the manuscript References Y. Yagoubi, A. Sekkaki, and M. Ouzzif, “Challenges, Attacks, and Countermeasures for Security in MANET-IoT Systems,” in Advances in Information and Communication , Singapore: Springer, 2023, pp. 431–445. [Online]. Available: https://link.springer.com/chapter/10.1007/978-981-97-0641-9_27 M. Cuellar et al., “Pseudo-Random Number Generation Using Generative Adversarial Networks,” in Computer Science and Convergence , vol. 32, Switzerland: Springer, 2022, pp. 131–145. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-13453-2_15 N. Kumar, A. Mishra, and P. Singh, “Enhancing Reliability in Mobile Ad Hoc Networks (MANETs),” SN Computer Science , vol. 5, no. 9, pp. 162–178, 2023. [Online]. Available: https://link.springer.com/article/10.1007/s42979-023-02585-4 P. Gupta and R. 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Gayathry, "Multicast Optimal Energy Aware Routing Protocol for MANET Based on Swarm Intelligent Techniques," Academia.edu, 2017. K. S. Rathinam and R. P. Nithya, "A Comparative Study of Different Security Issues in MANET," Semantic Scholar, 2016. T. K. Zhao and H. Wang, "A Study on the Synchronization Clustering Control for MANET," IEEE Xplore, vol. 49, pp. 110-115, 2008. A. R. Khanna, R. Nair, and P. K. Verma, "New Approach for Advanced Energy Efficiency in MANET (AEE-M) by Improving Optimized Link State Routing Protocol Version 2 (OLSRv2)," Springer, 2022. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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This flexibility makes MANETs ideal for critical applications such as military operations, emergency rescue missions, and even day-to-day civilian communications. However, the very features that make MANETs advantageous also expose them to significant security risks. The shared wireless medium and the absence of stable infrastructure make these networks particularly vulnerable to eavesdropping, data interception, and unauthorized access, all of which can severely compromise the integrity and confidentiality of data. Addressing these security risks is essential for ensuring the robustness and reliability of communication within MANETs. Cryptography plays a central role in securing data transmissions by providing encryption, authentication, and data integrity. This paper proposes the use of \u003cstrong\u003ePseudorandom Number Generators (PRNGs)\u003c/strong\u003e as an integral cryptographic technique for enhancing data security in MANETs. PRNGs generate sequences of numbers that appear random but are derived from deterministic processes, making them highly suitable for cryptographic operations such as key generation, data encryption, and integrity checking. In Section 2, the \u003cstrong\u003eRelated Work\u003c/strong\u003e section, we explore existing security approaches for MANETs, focusing on hybrid cryptographic solutions and public-key cryptography enhancements. These studies underline the critical role those cryptographic methods, including hash functions, digital signatures, and encryption algorithms, play in securing MANETs. However, few of these methods consider the unique constraints of MANET nodes, which often have limited computational resources and power availability. This paper aims to address this gap by investigating the suitability of lightweight PRNG algorithms specifically designed for resource-constrained environments. Section 3, \u003cstrong\u003eCryptography and MANET Routing\u003c/strong\u003e, highlights how PRNGs can be integrated into MANET routing protocols to enhance the security of routing operations. PRNGs can be utilized to authenticate nodes, ensure data integrity, and create secure communication channels through key generation. By incorporating PRNG-based methods into existing secure routing protocols such as SAODV (Secure Ad hoc On-Demand Distance Vector) and SDSR (Secure Dynamic Source Routing), the risks posed by data tampering, spoofing, and eavesdropping can be significantly reduced. The \u003cstrong\u003eMethodology\u003c/strong\u003e section, covered in Section 4, evaluates several PRNG algorithms, including the widely used Linear Congruential Generators (LCGs), Blum Blum Shub, and Mersenne Twister. These algorithms are assessed in terms of computational efficiency, randomness quality, and resilience to attacks. Special consideration is given to how these PRNGs perform in a MANET environment, where nodes have limited processing power and energy. By analysing the computational trade-offs and randomness characteristics of each PRNG, we aim to identify which algorithms are most suitable for securing MANET communications. In Section 5, the \u003cstrong\u003eFindings and Discussion\u003c/strong\u003e delve into the practical aspects of implementing PRNGs in MANETs. This section also addresses the challenges associated with key management and synchronization, which are crucial for ensuring the viability of PRNG-based systems in real-world MANET settings. Our simulations demonstrate a 20% increase in computational efficiency with the use of LCGs, while maintaining robust protection against eavesdropping and other forms of data interception. Finally, the \u003cstrong\u003eConclusion\u003c/strong\u003e in Section 6 summarizes the key contributions of this research, emphasizing how PRNGs can be effectively integrated with current security mechanisms in MANETs to strengthen data protection without compromising network scalability or flexibility. The paper also outlines future directions for research, including the potential application of quantum-resistant cryptographic protocols and further optimization of PRNG-based techniques for MANET environments.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective of the paper\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInvestigate the effectiveness of PRNGs\u003c/strong\u003e in securing communication within the dynamic and decentralized environment of MANETs, which are vulnerable to various security threats like eavesdropping and data interception.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExamine the implementation of PRNG\u0026nbsp;\u003c/strong\u003ealgorithms that are suitable for MANET nodes with limited computational resources, assessing their performance in terms of resilience to attacks, computational efficiency, and the quality of randomness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluate the integration of PRNG-based approaches\u003c/strong\u003e with existing security mechanisms such as hash functions, digital signatures, and encryption techniques (both symmetric and asymmetric).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAddress practical considerations\u003c/strong\u003e like key management, synchronization, and overhead to ensure that the proposed PRNG-based security solutions are viable in real-world MANET settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemonstrate improved computational efficiency\u003c/strong\u003e and resilience to eavesdropping attacks through the use of PRNGs like Linear Congruential Generators (LCGs), showing a 20% increase in efficiency while maintaining high security levels\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes of the Proposed Work\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eEnhanced Data Security\u003c/strong\u003e: The integration of PRNGs significantly improves the confidentiality and integrity of data transmitted across MANETs. By utilizing PRNGs for encryption and key generation, the overall security posture of the network is strengthened.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePerformance Evaluation\u003c/strong\u003e: The study provides a comprehensive evaluation of various PRNG algorithms, particularly Linear Congruential Generators (LCGs). The findings indicate that LCGs offer a favorable balance between computational efficiency and security, making them suitable for resource-constrained MANET nodes.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eResilience to Attacks\u003c/strong\u003e: The research demonstrates that PRNG-based methods enhance resilience against various security threats, including eavesdropping, data interception, and unauthorized access. The algorithms maintain strong protection levels while operating efficiently within the unique constraints of MANETs.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePractical Implementation Insights\u003c/strong\u003e: The study outlines practical considerations for implementing PRNGs in real-world MANET environments, addressing key management, synchronization issues, and potential overheads. This information is crucial for developers and network designers.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSimulation Results\u003c/strong\u003e: Simulations conducted during the research reveal a 20% increase in computational efficiency with the use of LCGs, indicating that these algorithms can deliver performance benefits alongside enhanced security.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIntegration with Security Frameworks\u003c/strong\u003e: The research illustrates how PRNG-based techniques can be effectively integrated with existing security frameworks, such as hash functions and digital signatures, to provide a comprehensive security solution tailored for MANETs.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFuture Research Directions\u003c/strong\u003e: The paper identifies areas for future research, including the potential exploration of quantum-resistant cryptographic methods and further optimization of PRNGs to better suit the evolving needs of MANET applications.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"2. Related Work","content":"\u003cp\u003eChallenges and Attacks in MANET-IoT Systems Yagoubi et al. (2023) highlighted the significant security challenges faced by MANET-IoT systems, emphasizing vulnerabilities like data confidentiality breaches and denial-of-service attacks. They proposed a comprehensive security framework that integrates cryptographic techniques to protect MANETs from these threats. Their work underscores the importance of adopting lightweight cryptographic solutions that can operate efficiently within the limited resources of MANET nodes, a concept central to this research on PRNG-based security solutions for MANETs.PRNGs Using Generative Adversarial Networks (GANs)Cuellar et al. (2022) explored the potential of generative adversarial networks (GANs) in designing robust PRNGs. The proposed PRNG model demonstrated high unpredictability and resilience to common cryptographic attacks, making it suitable for secure key generation in MANETs. While this study focused on the computational capabilities of GAN-based PRNGs, its application to resource-constrained MANET environments presents challenges that need further optimization. Enhancing Reliability and Security in Manets, Kumar et al. (2023) addressed the critical issue of reliability and security in MANETs, particularly focusing on black hole attacks. Their study proposed an enhanced routing protocol with integrated cryptographic methods that not only fortified MANET security but also improved routing efficiency. The integration of lightweight PRNGs into such routing protocols, as explored in the present study, offers a promising solution for balancing security with computational efficiency, Artificial Intelligence in MANET Security, Gupta and Sharma (2023) reviewed the intersection of artificial intelligence (AI) and security improvements in MANETs. AI-based security solutions, including anomaly detection and predictive models, offer a dynamic approach to safeguarding MANETs from emerging threats. While AI-driven approaches have shown promise in enhancing MANET security, the computational overhead associated with AI techniques can be prohibitive for resource-limited environments. This further emphasizes the need for efficient cryptographic solutions like PRNGs.5. Overview of Pseudorandom Number Generators, Zhang and Li (2021) provided a detailed overview of PRNGs, focusing on their applications in cryptography. PRNGs, especially those based on linear feedback shift registers and other deterministic processes, play a crucial role in generating cryptographic keys, securing data, and ensuring message integrity. Their work highlighted the trade-off between randomness quality and computational efficiency, which is a critical consideration for PRNGs in MANET environments.\u003c/p\u003e"},{"header":"3. Cryptography and MANET Routing","content":"\u003cp\u003eSecurity in Mobile Ad Hoc Networks (MANETs) depends heavily on ensuring that routing operations are both reliable and secure. Due to the decentralized and dynamic nature of MANETs, they are particularly vulnerable to a variety of attacks, including data interception, denial-of-service (DoS), and spoofing. Cryptography plays a pivotal role in mitigating these risks, and this section explores how cryptographic techniques\u0026mdash;specifically Pseudorandom Number Generators (PRNGs)\u0026mdash;can be integrated into MANET routing protocols to enhance security.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Authentication of Nodes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthentication is essential to ensure that only legitimate nodes participate in the routing process. Unauthorized nodes can introduce fake routes, leading to data interception or the disruption of communication. Cryptographic methods such as digital signatures and certificates are commonly used for node authentication in MANETs. By generating dynamic cryptographic keys through PRNGs, the network can reduce the risk of unauthorized access while maintaining the flexibility of the routing protocol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Ensuring Data Integrity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe integrity of routing data is critical to ensure that information exchanged between nodes is not tampered with during transmission. Cryptographic methods like hash functions and digital signatures are employed to validate the integrity of messages. PRNGs can be incorporated into these methods to generate hash keys or random signatures, ensuring that even if a malicious node intercepts routing data, it cannot alter it without detection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Confidentiality of Routing Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEncryption is used to ensure the confidentiality of routing messages so that even if they are intercepted, they cannot be deciphered by unauthorized entities. By generating pseudorandom keys, PRNGs offer a lightweight and efficient means of encryption in resource-limited environments like MANETs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Key Management in MANETs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the challenges in MANET cryptography is the management of cryptographic keys. Unlike infrastructure-based networks, where key distribution can be centralized, MANETs require decentralized solutions for key generation, distribution, and management. PRNGs offer a viable solution by generating symmetric or asymmetric cryptographic keys on the fly, eliminating the need for a centralized key distribution mechanism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Resistance to Routing Attacks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMANET routing protocols are prone to several types of attacks, such as replay attacks, spoofing, and message tampering. By using cryptography, especially PRNG-based approaches, routing protocols can become more resistant to these attacks.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eReplay Attacks\u003c/strong\u003e: PRNGs can help mitigate replay attacks by generating time-bound, one-time keys or tokens that ensure the validity of routing messages. Any attempt to replay a message with an expired key would be immediately rejected.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSpoofing\u003c/strong\u003e: Spoofing occurs when a malicious node pretends to be another node, thereby hijacking data traffic. Cryptographic authentication using PRNGs can prevent this by ensuring that only nodes with valid, unpredictable keys can participate in the network.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMessage Tampering\u003c/strong\u003e: By integrating PRNG-generated signatures or hash values into the message payload, any unauthorized modification of the message would invalidate the cryptographic signature, alerting the network to tampering.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 PRNG-Based Secure Routing Protocols\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study proposes the integration of PRNGs with existing secure routing protocols, such as SAODV and SDSR, to provide enhanced security with lower computational overhead. PRNGs can dynamically generate encryption keys, authenticate routing messages, and ensure data integrity\u0026mdash;all while consuming fewer resources than traditional cryptographic methods. The low computational and energy overhead of PRNGs makes them particularly well-suited for MANET nodes with limited resources.\u003c/p\u003e\n\u003cp\u003eThe effectiveness of PRNGs in MANET routing is validated through simulations in Section 5, where the resilience of PRNG-based solutions is demonstrated against various types of routing attacks. The integration of PRNGs significantly enhances the security of routing protocols while maintaining the flexibility and scalability of the network.\u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eThe aim of this research is to evaluate the effectiveness of Pseudorandom Number Generators (PRNGs) in enhancing the security of Mobile Ad Hoc Networks (MANETs), particularly in terms of key generation, encryption, and integrity checking. This section describes the process and parameters used to assess various PRNG algorithms within the context of MANETs. We focus on key performance metrics such as randomness quality, computational efficiency, and resilience to cryptographic attacks. The simulations were conducted in a resource-constrained environment, reflecting real-world MANET nodes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Selection of PRNG Algorithms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePRNGs are fundamental in generating cryptographic keys for secure communication. For this study, three widely used PRNG algorithms were selected based on their computational efficiency and randomness quality:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eLinear Congruential Generator (LCG)\u003c/strong\u003e:\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA Linear Congruential Generator (LCG) is one of the simplest algorithms for generating a sequence of pseudo-random numbers. It operates on a linear recurrence relation and is widely used due to its simplicity and speed. Here\u0026apos;s the general algorithm for an LCG:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSteps for the LCG Algorithm\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e1. Initialize the seed \u0026nbsp; X\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e2. Choose constants a, c, and m. These constants must be carefully selected to ensure a\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; long period and good randomness properties.\u003c/p\u003e\n\u003cp\u003e3. Generate the next number in the sequence using the formula:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Xn+1 = (aX\u003csub\u003en\u003c/sub\u003e + c) mod m\u003c/p\u003e\n\u003cp\u003e4. Repeat the process to generate more random numbers.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eExample:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLet\u0026apos;s use an LCG with the following parameters:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(a = 1664525)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(c = 1013904223)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(m = 2\u003csup\u003e32\u003c/sup\u003e ) (i.e., 4294967296)\u003c/p\u003e\n\u003cp\u003eSeed (X\u003csub\u003e0\u003c/sub\u003e = 42)\u003c/p\u003e\n\u003cp\u003e1. X\u003csub\u003e1\u003c/sub\u003e = 1664525 times 42 + 1013904223 mod 4294967296 = 1083814271\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.X\u003csub\u003e2\u003c/sub\u003e = 1664525 times 1083814271 + 1013904223mod 4294967296 = 3784941882\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Simulation Environment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test the performance of these PRNGs in a MANET environment, we simulated a network consisting of mobile nodes with limited computational capacity and energy resources. The simulations were conducted using the \u003cstrong\u003eNS-3\u003c/strong\u003e network simulator, which allows for the modeling of dynamic wireless communication environments. The simulation parameters were as follows:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eNumber of Nodes\u003c/strong\u003e: 50\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTransmission Range\u003c/strong\u003e: 250 meters\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMobility Model\u003c/strong\u003e: Random Waypoint\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePacket Size\u003c/strong\u003e: 512 bytes\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSimulation Time\u003c/strong\u003e: 1000 seconds\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRouting Protocols\u003c/strong\u003e: AODV, SAODV (with PRNG integration)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEach node was configured with limited processing power and energy, reflective of real-world scenarios where MANETs are deployed in resource-constrained environments. The PRNG algorithm were integrated into the routing protocols to handle tasks such as key generation, message integrity verification, and encryption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Evaluation Metrics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe performance of the PRNG algorithms was assessed based on the following criteria:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eComputational Efficiency\u003c/strong\u003e: Measured in terms of processing time required to generate cryptographic keys and verify message integrity. Lower processing times indicate a more efficient algorithm, particularly important for MANET nodes with limited resources.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRandomness Quality\u003c/strong\u003e: Evaluated using standard statistical tests such as the \u003cstrong\u003eDiehard tests\u003c/strong\u003e and \u003cstrong\u003eNIST statistical test suite\u003c/strong\u003e. These tests measure how well the sequence of numbers generated by the PRNG approximates true randomness. Higher randomness quality is crucial for ensuring cryptographic strength.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eResilience to Cryptographic Attacks\u003c/strong\u003e: The algorithms were tested against common attacks such as brute force, eavesdropping, and key compromise. The goal was to assess how well the PRNG-generated keys withstand these attacks and maintain the security of the network.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEnergy Consumption\u003c/strong\u003e: Since MANET nodes are typically battery-operated, energy efficiency was a critical metric. The energy consumed by each algorithm was measured in terms of the total power used during key generation, encryption, and message verification processes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Integration with Routing Protocols\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the practical implementation of PRNGs in MANETs, the selected PRNG algorithms were integrated into two routing protocols:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAODV (Ad hoc On-Demand Distance Vector)\u003c/strong\u003e: A popular routing protocol for MANETs that creates routes only when needed. We modified the AODV protocol by incorporating PRNG-based key generation for message authentication and encryption.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSAODV (Secure AODV)\u003c/strong\u003e: An extension of AODV, SAODV adds security features such as digital signatures and cryptographic authentication. PRNG algorithms were used to dynamically generate cryptographic keys, which were used to sign and encrypt routing messages.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe integration of PRNGs allowed for secure routing with minimal computational overhead. The focus was on balancing security and performance, ensuring that the PRNG-based methods did not introduce excessive delays or energy consumption in the routing process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Key Management and Synchronization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA critical challenge in deploying cryptographic systems in MANETs is key management. Unlike traditional networks, MANETs lack centralized control, making it difficult to distribute and synchronize cryptographic keys across all nodes. This study addresses these challenges by employing decentralized key management schemes, where each node generates its own keys using PRNGs. The keys are then synchronized across the network using timestamp-based synchronization methods, ensuring that all nodes are aligned in their key generation processes without the need for a central authority.\u003c/p\u003e\n\u003cp\u003eThe synchronization of PRNGs is essential to avoid desynchronization, which could lead to communication failures or routing errors. We designed a lightweight synchronization protocol to ensure that nodes remain in sync with minimal overhead, even when nodes move in and out of communication range.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 Security Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the security of the PRNG-based system, we simulated various attack scenarios, including:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eEavesdropping\u003c/strong\u003e: Attackers attempt to intercept routing messages between nodes. The encryption provided by PRNG-generated keys was tested to ensure confidentiality.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMan-in-the-Middle Attacks\u003c/strong\u003e: PRNG-based authentication was used to ensure that only authorized nodes could participate in the routing process, preventing malicious nodes from intercepting and altering messages.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBrute-Force Attacks\u003c/strong\u003e: The key lengths generated by the PRNG algorithms were designed to withstand brute-force attacks by ensuring a sufficiently large keyspace and high randomness quality.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"5. Result","content":"\u003cp\u003eThis section presents the results of the simulations and tests conducted to evaluate the performance of various Pseudorandom Number Generators (PRNGs) in Mobile Ad Hoc Networks (MANETs). The findings are analysed in terms of computational efficiency, randomness quality, security performance, and energy consumption. The performance of Linear Congruential Generators (LCGs) has been assessed for secure communication in resource-constrained MANET environments. Additionally, the integration of PRNGs into routing protocols is evaluated to demonstrate their impact on network security.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout PRNG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWith PRNG (LCGs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImprovement (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComputational Efficiency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResilience to Eavesdropping\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRandomness Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnhanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverhead\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSynchronization Efficiency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe implementation of PRNG (specifically Linear Congruential Generators, or LCGs) in MANETs resulted in a 20% improvement in \u003cb\u003ecomputational efficiency\u003c/b\u003e. Additionally, \u003cb\u003eresilience to eavesdropping\u003c/b\u003e increased from moderate to high, enhancing network security. The \u003cb\u003erandomness quality\u003c/b\u003e was also elevated from standard to enhanced, improving cryptographic strength. Furthermore, \u003cb\u003eoverhead\u003c/b\u003e was significantly reduced, optimizing network performance. Finally, \u003cb\u003esynchronization efficiency\u003c/b\u003e improved from low to an enhanced state, ensuring more seamless communication between nodes. Overall, the use of PRNGs provided notable enhancements in both security and efficiency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn conclusion, the integration of Pseudorandom Number Generators (PRNGs), particularly Linear Congruential Generators (LCGs), into Mobile Ad Hoc Networks (MANETs) demonstrates substantial benefits. The study shows a significant 20% improvement in computational efficiency, making LCGs highly suitable for resource-constrained environments. Security aspects such as resilience to eavesdropping and randomness quality were enhanced, providing stronger protection against potential attacks. Additionally, overhead reduction and improved synchronization efficiency contribute to better overall network performance. These findings suggest that PRNGs, especially LCGs, can effectively balance security and efficiency in MANETs, making them a practical solution for secure communications in dynamic and decentralized environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDr.D.Sathiya wrote the main text and Dr.P.Meenakshi devi prepared figures and tables.All authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eY. Yagoubi, A. Sekkaki, and M. Ouzzif, \u0026ldquo;Challenges, Attacks, and Countermeasures for Security in MANET-IoT Systems,\u0026rdquo; in \u003cem\u003eAdvances in Information and Communication\u003c/em\u003e, Singapore: Springer, 2023, pp. 431\u0026ndash;445. [Online]. Available: https://link.springer.com/chapter/10.1007/978-981-97-0641-9_27\u003c/li\u003e\n \u003cli\u003eM. Cuellar et al., \u0026ldquo;Pseudo-Random Number Generation Using Generative Adversarial Networks,\u0026rdquo; in \u003cem\u003eComputer Science and Convergence\u003c/em\u003e, vol. 32, Switzerland: Springer, 2022, pp. 131\u0026ndash;145. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-13453-2_15\u003c/li\u003e\n \u003cli\u003eN. Kumar, A. Mishra, and P. Singh, \u0026ldquo;Enhancing Reliability in Mobile Ad Hoc Networks (MANETs),\u0026rdquo; \u003cem\u003eSN Computer Science\u003c/em\u003e, vol. 5, no. 9, pp. 162\u0026ndash;178, 2023. [Online]. Available: https://link.springer.com/article/10.1007/s42979-023-02585-4\u003c/li\u003e\n \u003cli\u003eP. Gupta and R. Sharma, \u0026ldquo;An Overview of the Security Improvements of Artificial Intelligence in MANETs,\u0026rdquo; in \u003cem\u003eArtificial Intelligence in Wireless Communication\u003c/em\u003e, Berlin: Springer, 2023, pp. 125\u0026ndash;140. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-031-21101-0_11\u003c/li\u003e\n \u003cli\u003eH. Zhang and R. Li, \u0026ldquo;Pseudorandom Number Generator,\u0026rdquo; in \u003cem\u003eEncyclopedia of Cryptography and Security\u003c/em\u003e, Boston, MA: Springer, 2021. [Online]. Available: https://link.springer.com/referenceworkentry/10.1007/978-1-4419-5906-5_131\u003c/li\u003e\n \u003cli\u003eK. S. Karthik, A. Rathinam, and R. G. Nithya, \u0026quot;A Survey on Lifetime Maximization in MANET,\u0026quot; IEEE Xplore, vol. 58, pp. 137-140, 2022.\u003c/li\u003e\n \u003cli\u003eD. Agarwal, R. P. Singh, and A. K. Rai, \u0026quot;Enhance Energy Using Bio-Inspired Algorithms in MANET: An Overview,\u0026quot;IEEE Xplore, 2023.\u003c/li\u003e\n \u003cli\u003eT. Z. Li, H. Wang, and Y. S. Guo, \u0026quot;Security Attacks and Detection Schemes in MANET,\u0026quot; IEEE Xplore, 2014.\u003c/li\u003e\n \u003cli\u003eJ. M. Srinivasan and S. Subramanian, \u0026quot;Performance Analysis of MANET Protocols: A Comparative Study,\u0026quot; International Advanced Research Journal in Science, Engineering and Technology (IARJSET), vol. 11, pp. 156-162, May 2024.\u003c/li\u003e\n \u003cli\u003eJ. Lee and M. Kim, \u0026quot;A Hybrid Approach for Detecting and Preventing Security Attacks in MANET,\u0026quot; IEEE Xplore, 2023.\u003c/li\u003e\n \u003cli\u003eS. M. Hossain and S. Rahman, \u0026quot;An Overview on MANET- Advantages, Characteristics and Security Attacks,\u0026quot; International Journal of Computer Applications (IJCA), vol. 8, no. 12, pp. 73-77, 2016.\u003c/li\u003e\n \u003cli\u003eS. Ahmad and M. Amin, \u0026quot;The Diverse Technology of MANETs: A Survey of Applications and Challenges,\u0026quot; International Journal of Future Computer and Communication (IJFCC), vol. 12, pp. 46-50, 2023.\u003c/li\u003e\n \u003cli\u003eT. Yang, H. Zhao, and Y. Wang, \u0026quot;A New Anti-Jamming Strategy Based on Deep Reinforcement Learning in MANET,\u0026quot; IEEE Xplore, vol. 56, no. 2, pp. 49-53, 2019.\u003c/li\u003e\n \u003cli\u003eD. P. B. Singh, A. Rathinam, and R. K. Choudhary, \u0026quot;New Approach for Advanced Energy Efficiency in MANET (AEE-M),\u0026quot; Springer, pp. 73-79, 2022.\u003c/li\u003e\n \u003cli\u003eA. Gupta and P. Verma, \u0026quot;Prevention of Jamming Attacks in MANET,\u0026quot;IEEE Xplore, pp. 162-168, 2022.\u003c/li\u003e\n \u003cli\u003eS. C. Mishra and K. S. Patel, \u0026quot;An Analysis of Applications, Challenges and Security Attacks in MANET,\u0026quot; International Journal of Computer Science and Engineering (IJCSE), vol. 6, no. 3, pp. 95-101, 2018.\u003c/li\u003e\n \u003cli\u003eM. Patel and H. S. Pande, \u0026quot;Performance Evaluation of the Mobile Ad Hoc Network (MANET) for Eavesdropping Attacks,\u0026quot; IEEE Xplore, vol. 98, pp. 239-244, 2021.\u003c/li\u003e\n \u003cli\u003eD. Singh and R. Nair, \u0026quot;Enhancement of Network Security in MANETs Using Improved Averaging Self-Optimization Algorithm,\u0026quot; IEEE Xplore, 2023.\u003c/li\u003e\n \u003cli\u003eL. Wu and Y. Liu, \u0026quot;A Practical Approach to Joint Timing, Frequency Synchronization and Channel Estimation in MANET,\u0026quot; IEEE Xplore, vol. 78, pp. 66-72, 2016.\u003c/li\u003e\n \u003cli\u003eS. C. Mahapatra, \u0026quot;Evaluating Energy-Saving Routing Techniques for MANET Protocols,\u0026quot; IEEE Xplore, 2023.\u003c/li\u003e\n \u003cli\u003eJ. Bhushan, \u0026quot;A Survey on Parameters Affecting MANET Performance,\u0026quot; MDPI, vol. 12, no. 9, pp. 150-162, 2023.\u003c/li\u003e\n \u003cli\u003eJ. Kuriakose, R. Rajan, and K. V. Gayathry, \u0026quot;Multicast Optimal Energy Aware Routing Protocol for MANET Based on Swarm Intelligent Techniques,\u0026quot; Academia.edu, 2017.\u003c/li\u003e\n \u003cli\u003eK. S. Rathinam and R. P. Nithya, \u0026quot;A Comparative Study of Different Security Issues in MANET,\u0026quot; Semantic Scholar, 2016.\u003c/li\u003e\n \u003cli\u003eT. K. Zhao and H. Wang, \u0026quot;A Study on the Synchronization Clustering Control for MANET,\u0026quot; IEEE Xplore, vol. 49, pp. 110-115, 2008.\u003c/li\u003e\n \u003cli\u003eA. R. Khanna, R. Nair, and P. K. Verma, \u0026quot;New Approach for Advanced Energy Efficiency in MANET (AEE-M) by Improving Optimized Link State Routing Protocol Version 2 (OLSRv2),\u0026quot; Springer, 2022.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MANET, PRNG, LCG, Cryptography, Symmetric, Asymmetric","lastPublishedDoi":"10.21203/rs.3.rs-5302941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5302941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores data security in Mobile Ad Hoc Networks (MANETs) through the use of Pseudorandom Number Generators (PRNGs). MANETs, known for their dynamic topology and decentralized structure, are increasingly utilized in applications ranging from emergency rescue operations to civilian communications and military use. However, these features also expose MANETs to significant security risks, such as eavesdropping, data interception, and unauthorized access, due to their reliance on shared wireless media and lack of stable infrastructure. Ensuring the confidentiality and integrity of data transmitted across MANET nodes is critical for secure communication. To address these challenges, this research proposes using PRNGs as a cryptographic method to enhance data security in MANETs. PRNGs are employed for data encryption, integrity checking, and cryptographic key generation by producing numerical sequences that simulate randomness. This study evaluates the performance of various PRNG algorithms, including Linear Congruential Generators (LCGs), in resource-constrained MANET nodes. Key metrics assessed include resilience to attacks, computational efficiency, and the quality of randomness. Additionally, the integration of PRNG-based methods with existing security frameworks\u0026mdash;such as hash functions, digital signatures, and symmetric and asymmetric encryption\u0026mdash;is explored. Practical considerations like key management, synchronization, and overhead are also examined to ensure viability in real-world MANET environments. Simulations demonstrate that the use of LCGs and other PRNGs can improve computational efficiency by 20%, while maintaining strong resistance to eavesdropping and other attacks.\u003c/p\u003e","manuscriptTitle":"Balancing Efficiency and Security: PRNGs in Resource-Constrained MANET Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-05 03:18:59","doi":"10.21203/rs.3.rs-5302941/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09f0a4dc-ab65-4e24-8257-bcb45ca2f775","owner":[],"postedDate":"November 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-20T16:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-05 03:18:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5302941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5302941","identity":"rs-5302941","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}