Genetic algorithms are meta-heuristic algorithms used in constrained and unconstrained optimization problems. They mimic the physiological evolution process of humans and use these insights to solve problems. Genetic algorithms are known to outperform some traditional machine learning and search algorithms because they can withstand noise or changes in input pattern.
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Basics of Machine Learning in Cybersecurity
- What is machine learning?
- Problems that machine learning solves
- Why use machine learning in cybersecurity?
- Current cybersecurity solutions
- Data in machine learning
- Different types of machine learning algorithm
- Algorithms in machine learning
- The machine learning architecture
- Hands-on machine learning
- Summary
- What is machine learning?
- Time Series Analysis and Ensemble Modeling
- Segregating Legitimate and Lousy URLs
- Knocking Down CAPTCHAs
- Using Data Science to Catch Email Fraud and Spam
- Efficient Network Anomaly Detection Using k-means
- Decision Tree and Context-Based Malicious Event Detection
- Catching Impersonators and Hackers Red Handed
- Changing the Game with TensorFlow
- Financial Fraud and How Deep Learning Can Mitigate It
- Case Studies
- Other Books You May Enjoy
Genetic algorithms
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