Ep. 9: Winning the Cybersecurity Cat and Mouse Game with AI
Feb 8, 2017
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Dr. Eli David, CTO of Deep Instinct, discusses using AI for cybersecurity, bringing deep learning to combat malware. Topics include evolution of neural networks, AI in cybersecurity, layered defense strategies, and AI-enhanced behavioral analysis for proactive threat prevention.
Deep learning in cybersecurity boosts detection rates by 20-30%, outperforming traditional methods.
Multi-layered defense strategy aims to make cyber attacks more challenging through AI-driven malware detection.
Deep dives
The Role of AI in Cybersecurity
Dr. Eli David, an expert in computational intelligence and CTO of deep instinct, explains how they apply deep learning to revolutionize cybersecurity. Traditional methods struggle to keep up with the ever-evolving threats, while deep learning can adapt to detect malware mutations and new threats rapidly. By training their deep learning models on vast amounts of data, they achieve significant improvements in detection rates, making it more challenging for attackers to evade detection.
Advancements in Deep Learning and Cybersecurity
Deep learning has transformed computer vision, speech recognition, and text understanding by operating directly on raw data without the need for feature extraction. Similarly, applying deep learning to cybersecurity enables the detection of new threats without prior exposure by analyzing the raw bytes of malicious and legitimate files. This approach results in a substantial 20-30% increase in detection rates compared to traditional machine learning methods. The increased reliance on deep learning models for cybersecurity aims to enhance defense mechanisms against evolving cyber threats.
The Future of Cybersecurity and AI
The cybersecurity industry is evolving towards a multi-layered defense approach, leveraging various solutions and technologies to create barriers against cyber attacks. While deep learning provides advanced detection capabilities, it is acknowledged that achieving 100% protection is unrealistic. The focus shifts towards making attacks more difficult by applying deep learning for malware detection and prevention. Deep learning applications in normal behavior detection also show promising results, indicating a future trend of utilizing AI for enhancing cybersecurity resilience.
Cybersecurity is a cat-and-mouse game where the mouse always has long had the upper hand because it’s so easy for new malware to go undetected. Dr. Eli David, an expert in computational intelligence and CTO of Deep Instinct, wants to use AI to change that, bringing the GPU-powered deep learning techniques underpinning modern speech and image recognition to the vexing world of cybersecurity.
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