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 ...

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Surveillance has come a long way from the watchful eyes of security guards to the all-seeing lenses of today's cameras.

Devon Rollins, vice president of cyber engineering and machine learning at Capital One, adds, “Securing business-critical applications requires a level of differentiated protection.

Other applications of machine learning Apart from DGAs, other attack techniques can be used and, in the same efficient way, tackled by ML. Phishing is an excellent use case for machine learning.

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 ...

Last, but most certainly not least, quantum-enhanced machine learning could also aid in identifying and mitigating cyber threats. If the current applications of machine learning seem daunting ...

Real-World Applications of Machine Learning: ... ML models can be manipulated by adversarial inputs, posing security risks in applications like facial recognition or autonomous driving.