Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

Security Operation Centers (SOC) continuously monitor system logs to detect suspicious activities such as brute-force attacks, unauthorized access, or privilege abuse. However, the large volume and ...

Abstract: The rapid expansion of data from diverse sources has made anomaly detection (AD) increasingly essential for identifying unexpected observations that may signal system failures, security ...

Melanoma remains one of the hardest skin cancers to diagnose because it often mimics harmless moles or lesions. While most artificial intelligence (AI) tools rely on dermoscopic images alone, they ...

ABSTRACT: The accelerating sophistication of cyberattacks poses unprecedented challenges for national security, critical infrastructures, and global digital resilience. Traditional signature-based ...

5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?

ABSTRACT: Healthcare cyberattacks rise as attackers leverage system and human vulnerabilities. The existing security systems are technology-driven as they focus on system resilience, but they neglect ...

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.