Classification of Abnormal Patterns in Traffic Analysis Based on a Fusion Approach

Classification of Abnormal Patterns in Traffic Analysis Based on a Fusion Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Classification of Abnormal Patterns in Traffic Analysis Based on a Fusion Approach Fathi E. Abd El-Samie, Nabil A. Ismail, Adel S. El-Fishawy, Khalil F. Ramadan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8630088/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Optical signal processing plays a crucial role in Intrusion Detection System (IDS) for optical networks, as optical techniques provide more bandwidth and more security for the communication channel. It involves various techniques and operations performed on the optical signals to extract relevant information and identify potential security threats or intrusions. The IDS is a very important component which detect unauthorized access to computer networks and systems. IDS analyzes network traffic and system logs to identify potential security threats and alert system administrators to take appropriate action. There are different types of IDS, including signature-based, anomaly-based, and hybrid approaches. IDS uses a variety of detection methods to identify potential security threats, including statistical analysis, machine learning, and rule-based methods. However, IDS faces several challenges, including the need to balance detection accuracy with false positives and false negatives while keeping up with the ever-changing threat landscape. There are an evaluation metrics, such as detection rate, false positive rate, and accuracy. In this paper, we use accuracy of the detection to evaluate the effectiveness of IDS. Data preprocessing and feature selection are also important to improve search capabilities in IDS. In general. Ongoing research in this area, including the use of deep learning models such as convolution neural network (CNN), long-short term memory (LSTM) and hybrid models with CIC-IDS 2018 dataset, will continue to improve IDS ability to detect and prevent security breaches. This research will enhance accuracy detection of the IDS. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Optical signal processing Intrusion detection systems Machine learning Deep learning CNN LSTM Anomaly based detection KDD Cup 99 NSL-KDD CSE-CIC IDS 2018 Full Text 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8630088","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":595325389,"identity":"21d93a50-78fb-4b4b-8b22-8c010ebaacd4","order_by":0,"name":"Fathi E. Abd El-Samie","email":"","orcid":"","institution":"Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Fathi","middleName":"E. 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