TUM Library

Description based upon print version of record.

Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Acknowledgments -- Notes on Contributors -- Chapter 1 Artificial Intelligence -- The Chronological Evolution of Cybersecurity -- An Introduction to Artificial Intelligence -- The Sub-Fields of Artificial Intelligence -- Machine Learning -- Neural Networks -- Computer Vision -- A Brief Overview of This Book -- The History of Artificial Intelligence -- The Origin Story -- The Golden Age for Artificial Intelligence -- The Evolution of Expert Systems -- The Importance of Data in Artificial Intelligence

The Fundamentals of Data Basics -- The Types of Data that are Available -- Big Data -- Understanding Preparation of Data -- Other Relevant Data Concepts that are Important to Artificial Intelligence -- Resources -- Chapter 2 Machine Learning -- The High Level Overview -- The Machine Learning Process -- Data Order -- Picking the Algorithm -- Training the Model -- Model Evaluation -- Fine Tune the Model -- The Machine Learning Algorithm Classifications -- The Machine Learning Algorithms -- Key Statistical Concepts -- The Deep Dive into the Theoretical Aspects of Machine Learning

Understanding Probability -- The Bayesian Theorem -- The Probability Distributions for Machine Learning -- The Normal Distribution -- Supervised Learning -- The Decision Tree -- The Problem of Overfitting the Decision Tree -- The Random Forest -- Bagging -- The Naïve Bayes Method -- The KNN Algorithm -- Unsupervised Learning -- Generative Models -- Data Compression -- Association -- The Density Estimation -- The Kernel Density Function -- Latent Variables -- Gaussian Mixture Models -- The Perceptron -- Training a Perceptron -- The Boolean Functions -- The Multiple Layer Perceptrons

The Multi-Layer Perceptron (MLP): A Statistical Approximator -- The Backpropagation Algorithm -- The Nonlinear Regression -- The Statistical Class Descriptions in Machine Learning -- Two Class Statistical Discrimination -- Multiclass Distribution -- Multilabel Discrimination -- Overtraining -- How a Machine Learning System can Train from Hidden, Statistical Representation -- Autoencoders -- The Word2vec Architecture -- Application of Machine Learning to Endpoint Protection -- Feature Selection and Feature Engineering for Detecting Malware -- Common Vulnerabilities and Exposures (CVE)

Text Strings -- Byte Sequences -- Opcodes -- API, System Calls, and DLLs -- Entropy -- Feature Selection Process for Malware Detection -- Feature Selection Process for Malware Classification -- Training Data -- Tuning of Malware Classification Models Using a Receiver Operating Characteristic Curve -- Detecting Malware after Detonation -- Summary -- Applications of Machine Learning Using Python -- The Use of Python Programming in the Healthcare Sector -- How Machine Learning is Used with a Chatbot -- The Strategic Advantages of Machine Learning In Chatbots

The world of cybersecurity and the landscape that it possesses is changing on a dynamic basis. It seems like that hardly one threat vector is launched, new variants of it are already on the way. IT Security teams in businesses and corporations are struggling daily to fight off any cyberthreats that they are experiencing. On top of this, they are also asked by their CIO or CISO to model what future Cyberattacks could potentially look like, and ways as to how the lines of defenses can be further enhanced. IT Security teams are overburdened and are struggling to find ways in order to keep up with what they are being asked to do. Trying to model the cyberthreat landscape is a very laborious process, because it takes a lot of time to analyze datasets from many intelligence feeds. What can be done to accomplish this Herculean task? The answer lies in Artificial Intelligence (AI). With AI, an IT Security team can model what the future Cyberthreat landscape could potentially look like in just a matter of minutes. As a result, this gives valuable time for them not only to fight off the threats that they are facing, but to also come up with solutions for the variants that will come out later. Practical AI for Cybersecurity explores the ways and methods as to how AI can be used in cybersecurity, with an emphasis upon its subcomponents of machine learning, computer vision, and neural networks. The book shows how AI can be used to help automate the routine and ordinary tasks that are encountered by both penetration testing and threat hunting teams. The result is that security professionals can spend more time finding and discovering unknown vulnerabilities and weaknesses that their systems are facing, as well as be able to come up with solid recommendations as to how the systems can be patched up quickly.

OCLC-licensed vendor bibliographic record.