A SURVEY ON DEEP LEARNING TECHNIQUES FOR DARKNET TRAFFIC MALWARE DETECTION

Encrypted data that is sent over Tor or a VPN referred to as "darknet traffic." In order identify network activity brought on by cyberattack, it crucial be able recognise, find, and explain darknet activities. Cyberattacks pose serious danger security management can effectively observed through monitoring classification. Despite the fact many surveys were conducted traffic classification using machine learning techniques, only small number of researchers have means review their work classifying deep techniques. this article, we introduce novel idea for research malware used in uses Deep Learning which will aid improving surveys. First, based survey, chose few including Neural Network (DNN), Convolution (CNN), Recurrent (RNN), AutoEncoder (AE) proven more effective recent study. Second, dataset CIC-DarkNet-2020, contains variety activities TOR traffic, develop models. Finally, after analysing information, discovered best Model Darknet has potential enhance efficiency variant detection constrained system resources. Index Terms – Darknet, DNN, CNN, RNN, AE, Learning, Classifying Darknet.