Network-based intrusion detections become more difficult as Internet traffic is mostly
encrypted. This paper introduces a method to detect encrypted malicious traffic based on the …

Clustering algorithms play a crucial role in early warning cybersecurity systems. They allow
for the detection of new attack patterns and anomalies and enhance system performance …

The surge in global digitalization triggered by COVID-19 has led to a significant increase in
internet traffic and has precipitated a rapid transformation of the network security landscape …

Users and businesses in the network frequently suffer from attacks by malware like privacy
breach. While encrypted traffic protects users and businesses, it also provides convenience …

As the Internet evolves from TLS 1.2 to TLS 1.3, it offers enhanced security against network
eavesdrop** for online communications. However, this advancement also enables …

With the development of artificial intelligence, malicious traffic detection technology based
on deep learning has become mainstream with its powerful detection performance. Most …

Feature extraction is critical for TLS traffic analysis using machine learning techniques,
which it is also very difficult and time-consuming requiring huge engineering efforts. We …

Malicious URLs are a serious threat to cybersecurity because they can compromise user
security and inflict large financial losses. The extensiveness and adaptability of traditional …

As encryption technology has become more widely used, attackers have begun to use
techniques that increase the stealth nature of their attacks based on the assumption that …

Traditional methods for detecting malicious encrypted traffic are constrained by the rigidity of
rules and the limitations of manual intervention, making it difficult to cope with the …