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IEEE15h
Generating Adversarial Malware Examples Against Multiple Machine Learning Detectors - IEEE Xplore
Malware poses a significant threat to network and information system security, particularly in industrial Internet of Things (IIoT) environments, where embedded systems and edge devices often rely on ...
IEEE16h
An Efficient Machine Learning based technique for Android Malware Detection - IEEE Xplore
The increase of digital application in today’s era has attracted the attention of cybercriminals seeking to exploit vulnerabilities in the Android ecosystem. The lack of security and detection ...
Insurance Business America1d
AI fuels malware surge targeting encrypted systems and endpoints
Malicious activity via email remains a prominent delivery mechanism, as generative AI enables attackers to develop increasingly convincing phishing content. WatchGuard reported higher detection rates ...
AI–based test detects early signs of osteoporosis from X-ray images
Investigators have developed an artificial intelligence-assisted diagnostic system that can estimate bone mineral density in ...
Your employee logins are more valuable to criminals than ever - here's how to keep them protected
Hackers are increasingly going after employee login credentials, helped by advanced tools that are both cheap and easy to obtain, experts have warned. This is the sentiment echoed in a new report from ...
North Korean Hackers Deploy Rare Nim-Based Malware on macOS to Target Web3 Startups
A new Nim-based malware campaign targets macOS users in crypto and Web3 firms, using novel persistence tricks and encrypted ...
North Korean hackers target Mac users with devious new malware
Security researchers from SentinelLabs discovered NimDoor, a unique backdoor malware written in a lesser-known programming language called Nim, which they attributed to North Korea state-sponsored ...