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?

Only machine learning can address the increasing number of challenges in cybersecurity: scaling up security solutions, detecting unknown attacks and detecting advanced attacks, including ...

Furthermore, AI and ML algorithms power automated defence responses, enabling organisations to respond rapidly to cyber threats. Security Orchestration, Automation and Response (SOAR) platforms ...

Organizations should consider adopting AI and ML-powered prevention, detection, and response strategies to keep up with the volume, sophistication, and speed of today’s cyber threats.

Machine learning algorithms enable systems to learn and improve from experience; systems do not need to be explicitly programmed. Malware Detection: ML algorithms detect anomalies in code behavior ...

WIRED Brand Lab | With automated attacks on the rise, a point-solution approach to security is no longer sufficient. Here’s how AI and machine learning can help deliver end-to-end solutions to ...

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 ...

Using ML for Cyber Defence: On the flip side, machine learning can also be used to improve cybersecurity. ML can detect patterns in cyberattacks, helping security teams predict and prevent future ...