The anonymous nature of darknets is commonly exploited for illegal activities. Previous
research has employed machine learning and deep learning techniques to automate the …

Encrypted traffic tunnelled by Tor or VPN is referred to as darknet traffic. The ability to detect,
identify, and characterize darknet traffic is critical for detecting network traffic generated by a …

Darknet traffic classification is crucial for identifying anonymous network applications and
defensing cyber crimes. Although notable research efforts have been dedicated to …

Darknet is commonly known as the epicenter of illegal online activities. An analysis of
darknet traffic is essential to monitor real-time applications and activities running over the …

Darknet traffic classification is significantly important to categorize real-time applications.
Although there are notable efforts to classify darknet traffic which rely heavily on existing …

Deep machine learning techniques have shown promising results in network traffic
classification, however, the robustness of these techniques under adversarial threats is still …

With the ever-growing occurrence of networking attacks, robust network security systems are
essential to prevent and mitigate their harming effects. In recent years, machine learning …

Botnets are one of predominant threats to Internet security. To date, machine learning
technology has wide application in botnet detection because that it is able to summarize the …

Neural networks are increasingly important in the development of Network Intrusion
Detection Systems (NIDS), as they have the potential to achieve high detection accuracy …

State of the art deep learning techniques are known to be vulnerable to evasion attacks
where an adversarial sample is generated from a malign sample and misclassified as …