2. Intrusion detection: With the help of machine learning security teams can quickly detect anomalous behavior in network traffic and flag potential security breaches. 3. Fraud detection: Machine ...

By Dr. May Wang, CTO of IoT Security at Palo Alto Networks and the Co-founder, Chief Technology Officer (CTO), and board member of Zingbox - Why has machine learning become so vital in cybersecurity?

As artificial intelligence (AI) continues to evolve, its applications are increasingly permeating various sectors. While AI provides numerous benefits across ...

Machine learning is helping create stronger, more efficient encryption methods. By analyzing huge amounts of data, ML can design encryption algorithms that are tougher to crack.

AI and machine learning (ML) are urgently needed in endpoint protection to identify the weakest endpoints, update their patches and harden detection and response beyond what’s available today.

A critical privilege escalation vulnerability affecting Azure Machine Learning (AML) has been discovered by cybersecurity ...

A new technical paper titled “A Survey on Machine Learning in Hardware Security” was published by researchers at TU Delft. Abstract “Hardware security is currently a very influential domain, where ...

A new report out today from software supply chain company JFrog Ltd. reveals a surge in security vulnerabilities in machine learning platforms, highlighting the relative immaturity of the field comp ...

The 10 hottest data science and machine learning tools include MLflow 3.0, PyTorch, Snowflake Data Science Agent and ...