Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in the AI and machine learning research community. The most common reason is to cause a ...

AI hackers don’t sleep — so our defenses can’t either. Digital twins could finally help us hunt threats 24/7, before they hunt us. I recently gave a presentation at SecTor on proactive threat hunting, ...

TOKYO--(BUSINESS WIRE)--Mitsubishi Electric Corporation (TOKYO: 6503) announced today that it has developed the manufacturing industry’s first multi-agent AI technology that leverages an argumentation ...

The Computer Weekly Security Think Tank considers if Anthropic’s Claude Mythos frontier AI model is a benefit or barrier to ...

Cyber security is under intense scrutiny these days, especially as more adversarial AI-based attacks such as Scattered Spider can use a variety of living-off-the-land methods to spread and speed their ...

Cisco's AI Security and Safety Framework includes a unified taxonomy that aims to classify a range of AI safety threats, such as content safety failures, agentic risks, and supply chain threats. Cisco ...

IFAP generates adversarial perturbations using model gradients and then shapes them in the discrete cosine transform (DCT) domain. Unlike existing frequency-aware methods that apply a fixed frequency ...

Supply chains are a primary target for cybercriminals and provide the foundation of global commerce in the hyper-connected digital ecosystem of today ...

Effective network management is critical for ensuring reliable system performance and safeguarding the flow of information that powers nearly every business operation. AI has quickly become the ...

Anyone making predictions about IT and networking will inevitably come up against a major problem – the pace of development is so quick that it is difficult to make accurate estimations. There is also ...