Anomaly Detection

The Internet of Things (IoT) and embedded systems are rapidly evolving, driven by the need for sustainability and robust security measures. The Eclipse Foundation's 2024 IoT & Embedded Developer Survey sheds light on these trends, revealing how developers are navigating the complexities of

Adversarial Machine Learning (AML) in cybersecurity represents a critical field of study aimed at understanding and mitigating the risks associated with malicious manipulation of machine learning (ML) models. As ML becomes more prevalent in enhancing security protocols, adversaries are increasingly

The proliferation of Internet of Things (IoT) devices has revolutionized connectivity and accessibility, yet this surge also exposes critical vulnerabilities to zero-day attacks that evade traditional security measures. As IoT continues to integrate into everyday life, safeguarding network

The constant evolution of cyber threats requires innovative approaches to safeguard information networks. Intrusion Detection Systems (IDS) play a pivotal role in securing networks by monitoring and analyzing network traffic to identify suspicious activity. With the growing reliance on network

In the complex world of banking, fraud prevention remains an ever-evolving challenge for financial institutions. Traditional fraud detection methods, often relying on static, rule-based systems, struggle to keep pace with increasingly sophisticated fraud tactics. In this landscape, machine learning

In the rapidly evolving landscape of software systems, ensuring that these systems remain reliable and issue-free is paramount. Traditional deep learning methods, although effective in many domains, have encountered significant challenges when interpreting the semantic details embedded within log