Darknets are probes listening to traffic reaching IP addresses that host no services. Traffic
reaching a darknet results from the actions of internet scanners, botnets, and possibly …

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

Neural networks (NNs) are increasingly popular in developing NIDS, yet can prove
vulnerable to adversarial examples. Through these, attackers that may be oblivious to the …

Security breaches may be difficult to detect because attackers are continually tweaking
methods to evade detection and utilize legitimate credentials that have already been …

Artificial intelligence (AI) technology ruling people is still the scene in the science fiction film,
but hackers using AI technology against existing security measures is an inescapable trend …

This paper proposes a threat detection system based on Machine Learning classifiers that
are trained using darknet traffic. Traffic destined to Darknet is either malicious or by …

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage
legitimate credentials with trusted tools already deployed in a network environment, making …

Most machine learning algorithms only have a good recognition rate on balanced datasets.
However, in the field of malicious traffic identification, benign traffic on the network is far …

Network intrusion detection systems (NIDS) are an essential defense for computer networks
and the hosts within them. Machine learning (ML) nowadays predominantly serves as the …

With the booming of malware-based cyber-security incidents and the sophistication of
attacks, previous detections based on malware sample analysis appear powerless due to …