An optimization-based approach to identifying and detecting malicious activity in the dark web

Abstract

Dark web emerged as an encrypted and closed network that can only be accessed by computers using specific software and allows users to apply for membership and you have to provide your IDs to the admin of that network, which creates severe challenges in passing cybersecurity; With advanced Internet facilities, the origin and possibility of network invasions or attacks are increasing, and it is becoming very difficult for traditional anomaly detection systems to analyze objectively and pass Functional Purpose: Our ultimate goal by applying machine learning algorithms and network analysis methods is to identify a certain set of attributes that May indicate a cyber threat. Like many other studies, the traffic in the current paper has also been generalized to represent darknet traffic with a classification of unique features between attacks and attacks. As is known, based on the results of the experimental analysis of the proposed approach, it is possible to conclude about the high efficiency of the developed method in identifying types of cyber threats . This project contributes, by utilizing and improving machine learning techniques, to finding reliable measures to mitigate threats in cyberspace,  and improving threat detection programs.