This book shows how machine learning (ML) methods can be used to enhance cyber security operations, including detection, modeling, monitoring as well as defense against threats to sensitive data and security systems. Filling an important gap between ML and cyber security communities, it discusses topics covering a wide range of modern and practical ML techniques, frameworks and tools.

Algorithms play a central role both in the theory and in the practice of computing. The goal of the authors was to write a textbook that would not trivialize the subject but would still be readable by most students on their own. The book contains over 120 exercises. Some of them are drills; others make important points about the material covered in the text or introduce new algorithms not covered there. The book also provides programming projects. From the Table of Contents: Chapter 1: Basic knowledge of Mathematics, Relations, Recurrence relation and Solution techniques, Function and Growth of functions. Chapter 2: Different Sorting Techniques and their analysis. Chapter 3: Greedy approach, Dynamic Programming, Branch and Bound techniques, Backtracking and Problems, Amortized analysis, and Order Statics. Chapter 4: Graph algorithms, BFS, DFS, Spanning Tree, Flow Maximization Algorithms. Shortest Path Algorithms. Chapter 5: Binary search tree, Red black Tree, Binomial heap, B-Tree and Fibonacci Heap. Chapter 6: Approximation Algorithms, Sorting Networks, Matrix operations, Fast Fourier Transformation, Number theoretic Algorithm, Computational geometry Randomized Algorithms, String matching, NP-Hard, NP-Completeness, Cooks theorem.