Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …

Malware has been one of the most damaging threats to computers that span across multiple
operating systems and various file formats. To defend against ever-increasing and ever …

Machine learning (ML) systems are rapidly increasing in size, are acquiring new
capabilities, and are increasingly deployed in high-stakes settings. As with other powerful …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

Recent work has shown that adversarial Windows malware samples—referred to as
adversarial EXE mples in this article—can bypass machine learning-based detection relying …

Code autocompletion is an integral feature of modern code editors and IDEs. The latest
generation of autocompleters uses neural language models, trained on public open-source …

The incremental diffusion of machine learning algorithms in supporting cybersecurity is
creating novel defensive opportunities but also new types of risks. Multiple researches have …

Training pipelines for machine learning (ML) based malware classification often rely on
crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we study …