In the last decade, the number of computer malware has grown rapidly. Currently,
cybercriminals typically use malicious software (malware) as a means of attacking industrial …

The effectiveness of deep-learning-based malicious traffic detection systems relies on high-
quality labeled traffic datasets. However, malicious traffic labeling approaches can easily …

Cobalt Strike is the most prevalent attack tool abused by cyber-criminals to achieve
command and control on victim hosts over HTTPS traffics. It appears in many ransomware …

Internet traffic classification plays a key role in network visibility, Quality of Services (QoS),
intrusion detection, Quality of Experience (QoE) and traffic-trend analyses. In order to …

The accurate identification of internet traffic is crucial for network management. However, the
use of encryption techniques and constant changes in network protocols make it difficult to …

Current security solutions face significant challenges in dealing with the ever-increasing
complexity and sophistication of cyber-attacks. This is particularly true for the solutions that …

Due to the exponential increase of internet applications and network users, network traffic
classification (NTC) is a crucial study subject. It successfully improves network service …

Using deep learning models to deal with the classification tasks in network traffic offers a
new approach to address the imbalanced Internet of Things malicious traffic classification …

Malicious encrypted traffic detection is a critical component of network security management.
Previous detection methods can be categorized into two classes as follows: one is to use the …

One of the challenges of network traffic classification and mobile app identification is model
generalization. The accuracy and efficiency of classification models are strongly influenced …