Abstract: Network Intrusion Detection Systems (NIDS) are widely used to secure modern networks, but deploying accurate and scalable Machine Learning (ML)-based detection in high-speed environments ...

Abstract: We present Poligraph, an intrusion-tolerant and decentralized fake news detection system. Poligraph aims to address architectural, system, technical, and social challenges of building a ...

Australia's enterprise sector faces rising risks from AI-driven identity attacks. Learn what Australian challenged ...

Three heads are better than one. Versions of this proverb are found worldwide and throughout history. Yet in the race to ...

As UK police embrace the AI revolution, a WIRED investigation reveals the messy inside story of one region’s experiment with predictive analytics.

This repository is part of IPAL - an Industrial Protocol Abstraction Layer. IPAL aims to establish an abstract representation of industrial network traffic for subsequent unified and ...

This week’s threat list looks painfully familiar: abused integrations, fake tools, poisoned websites, ransomware crews trying to shut down security tools, and mobile malware asking for way too much ...

Infostealers continue to be some of the most pervasive and impactful threats across the cybercrime ecosystem. They play a central role in intrusions, silently harvesting passwords, cookies, and ...

Learn about cybersecurity and how to defend your people, data, and applications against today’s growing number of cybersecurity threats. Cybersecurity is a set of processes, best practices, and ...

To achieve this goal, we have proposed an intrusion detection system using multiscale attention 1D convolutional neural networks for efficient detection of all major attacks. Our proposed mechanism ...

Internet of Things (IoT) botnet detection faces significant challenges by the growing intricacy and decreased transparency of Machine Learning (ML) models. In this work, we provide an ensemble-based ...