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by Allan Liska, Laurent Gil
Security with AI and Machine Learning
Preface
1. The Role of ML and AI in Security
Where Rules-Based, Signature-Based, and Firewall Solutions Fall Short
Preparing for Unexpected Attacks
2. Understanding AI, ML, and Automation
AI and ML
Automation
Challenges in Adopting AI and ML
The Way Forward
3. Focusing on the Threat of Malicious Bots
Bots and Botnets
Bots and Remote Code Execution
More Flexible Malicious Bots, More Risks to Your Business
4. The Evolution of the Botnet
A Thriving Underground Market
The Bot Marketplace
AI and ML Adoption in Botnets
Staying Ahead of the Next Attack with Threat Intelligence
5. AI and ML on the Security Front: A Focus on Web Applications
Finding Anomalies
Bringing ML to Bot Attack Remediation
Using Supervised ML-Based Defenses for Security Events and Log Analysis
Deploying Increasingly Sophisticated Malware Detection
Using AI to Identify Bots
6. AI and ML on the Security Front: Beyond Bots
Identifying the Insider Threat
Tracking Attacker Dwell Time
Orchestrating Protection
ML and AI in Security Solutions Today
7. ML and AI Case Studies
Case Study: Global Media Company Fights Scraping Bots
The Problem
Human versus Bot Behavior
Bot Management
Human Interaction Analysis: 90% Were Bots
When Nothing Else Works: Using Very Sophisticated ML Engines with a Data Science Team
Correlated elements
The Results
8. Looking Ahead: AI, ML, and Managed Security Service Providers
The MSSP as an AI and ML Source
Cloud-Based WAFs Using AI and ML
Addressing the Application Security Challenge
9. Conclusion: Why AI and ML Are Pivotal to the Future of Enterprise Security
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