Blockchain-Enhanced Network Scanning and Monitoring (BENSAM) Framework

In recent years, the convergence of advanced technologies has enabled real-time data access and sharing across diverse devices and networks, significantly amplifying cybersecurity risks. For organizations with digital infrastructures, network security is crucial for mitigating potential cyber-attacks. They establish security policies to protect systems and data, but employees may intentionally or unintentionally bypass these policies, rendering the network vulnerable to internal and external threats. Detecting these policy violations is challenging, requiring frequent manual system checks for compliance. This paper addresses key challenges in safeguarding digital assets against evolving threats, including rogue access points, man-in-the-middle attacks, denial-of-service (DoS) incidents, unpatched vulnerabilities, and AI-driven automated exploits. We propose the Blockchain-Enhanced Network Scanning and Monitoring (BENSAM) Framework, a multi-layered system that integrates advanced network scanning with a structured database for asset management, policy driven vulnerability detection, and remediation planning. Key enhancements include device profiling, user activity monitoring, network forensics, intrusion detection capabilities, and multi-format report generation. By incorporating blockchain technology, leveraging immutable ledgers and smart contracts, the framework ensures tamper proof audit trails, decentralized verification of policy compliance, and automated real-time responses to violations, such as alerts or device isolation. The research provides a detailed literature review on blockchain applications in domains like IoT, healthcare, and vehicular networks. It analyzes common network vulnerabilities (e.g. open ports, remote access, disabled firewalls), attacks (including spoofing, flooding, DDoS), and outlines policy enforcement methods. Moreover, the framework anticipates emerging challenges from AI-driven attacks such as adversarial evasion, data poisoning, and transformer-based threats, positioning the system for future integration of adaptive mechanisms to counter these advanced intrusions. This blockchain enhanced approach streamlines security analysis, extends framework for AI threat detection with improved accuracy, reduces administrative overhead by integrating multiple security tools into a cohesive, trustworthy, reliable solution.