Название: Machine Learning Approaches in Cyber Security Analytics Автор: Tony Thomas, Athira P. Vijayaraghavan Издательство: Springer Год: 2020 Страниц: 217 Язык: английский Формат: pdf (true) Размер: 10.1 MB
This book introduces various machine learning methods for cyber security analytics. With an overwhelming amount of data being generated and transferred over various networks, monitoring everything that is exchanged and identifying potential cyber threats and attacks poses a serious challenge for cyber experts. Further, as cyber attacks become more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify them more rapidly. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyber attacks.
The main emphasis will be on the discussion of machine learning algorithms which have potential applications in cybersecurity analytics. There will be discussions on how cybersecurity analytics complements machine learning research. The potential applications include malware detection, biometrics, anomaly detection, cyberattack prediction, and so on.
The proposed book is a research monograph on cybersecurity analytics using various machine intelligence approaches. Most of the contents of the book are out of the original research by the authors. The cybersecurity and machine learning researchers, graduate students, and developers in cybersecurity will be benefited from this book. The prerequisites needed to understand the book are undergraduate-level knowledge mathematics, statistics, and computer science.
Introduction Introduction to Machine Learning Machine Learning and Cybersecurity 3.2 Spam Detection 3.3 Phishing Page Detection 3.4 Malware Detection 3.5 DoS and DDoS Attack Detection 3.6 Anomaly Detection 3.7 Biometric Recognition 3.8 Software Vulnerabilities Support Vector Machines and Malware Detection 4.2 Malware Detection 4.3 Maximizing the Margin and Hyperplane Optimization 4.4 Lagrange Multiplier 4.5 Kernel Methods Clustering and Malware Classification Nearest Neighbor and Fingerprint Classification 6.6 Algorithms to Compute Nearest Neighbors 6.6.1 Brute Force 6.6.2 KD Tree 6.6.3 Ball Tree Dimensionality Reduction and Face Recognition Neural Networks and Face Recognition 8.2 Artificial Neural Networks (ANN) 8.3 Convolutional Neural Networks (CNN) 8.4.2 Building a Keras Model Applications of Decision Trees Adversarial Machine Learning in Cybersecurity Bibliography . . . . 201
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