In spam and malware detection, attackers exploit randomization to obfuscate malicious data
and increase their chances of evading detection at test time, eg, malware code is typically …

To explore the advantages of adversarial learning and deep learning, we propose a novel
network intrusion detection model called SAVAER-DNN, which can not only detect known …

Despite the growing popularity of modern machine learning techniques (eg, Deep Neural
Networks) in cyber-security applications, most of these models are perceived as a black-box …

Since network intrusion detectors for the Internet of Things (IoT) increasingly rely on
machine learning models, attacks against these detectors are also escalating. Machine …

With the rapid advancement in machine learning (ML), ML-based Intrusion Detection
Systems (IDSs) are widely deployed to protect networks from various attacks. One of the …

Intrusion detection is a crucial service in today's data networks, and the search for new fast
and robust algorithms that are capable of detecting and classifying dangerous traffic is …

Intrusion Detection Systems (IDS) have a long history as an effective network defensive
mechanism. The systems alert defenders of suspicious and/or malicious behavior detected …

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 …

As the internet continues to be populated with new devices and emerging technologies, the
attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of …

Intrusion Detection systems are important tools in preventing malicious traffic from
penetrating into networks and systems. Recently, Intrusion Detection Systems are rapidly …