Attention Data Scientists and Cybersecurity professionals: the long awaited Machine Learning for Cybersecurity Cookbook is finally out!!
We’ve worked for months to deliver you what is the most up-to-date most cutting-edge most practical guide out there for applying ML/data science to security! Grab a copy to learn
1. Smart malware detection and evasion techniques using ML, deep learning for catching zero-day threats, overcoming obfuscation/packing, automating malware analysis…
2. Social engineering using ML including voice impersonation, Deepfake and fake review generation and *phishing on steroids* via ML…
3. Next-gen pentesting including NN-based fuzzing, metasploit made DANGEROUS with an RL agent, software vulnerability detection with AI and Tor deanonymizing!
4. Upgraded intrusion detection with AI including Insider Threat detection, network anomaly detection, catching DDoS and financial fraud.
5. Securing and attacking data with ML including Deep Learning for password cracking (#scary), steganalysis via AI, ML attacks on hardware, and encryption with NNs.
6. Secure and Private AI to preserve customer privacy and confidentiality, including federated learning, differential privacy, and even adversarial robustness.
Grab your copy to equip yourself with the best skills in the field of cybersecurity!
Dr. Tsukerman graduated from Stanford University and UC Berkeley. His achievements include designing a machine-learning-based anti-ransomware product that won Top 10 Ransomware Products by PC Magazine as well as implementing a machine-learning-based malware detection system for Palo Alto Network’s WildFire service serving over 30,000 enterprise customers. Dr. Tsukerman is the author of the Machine Learning for Cybersecurity Cookbook, Cybersecurity Data Science and Machine Learning for Red Team Hackers Courses.