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This dissertation by Chih-Yuan Lin explores network-based anomaly detection for SCADA (Supervisory Control and Data Acquisition) systems, which are crucial for monitoring critical infrastructure like electricity grids. The work addresses the challenges of detecting zero-day attacks through modeling normal traffic behaviors and identifying deviations. It proposes statistical and machine learning approaches to reduce false positives in anomaly detection and develops a traffic generation framework for evaluation. The research provides insights into traffic modeling, categorizing SCADA traffic, and analyzing network traces to improve SCADA security. The intended audience includes researchers and professionals in cybersecurity and network systems.

Anomaly detection (Computer security) --- Intrusion detection systems (Computer security)

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Go on a journey through the threat detection engineering lifecycle while enriching your skill set and protecting your organization Key Features Gain a comprehensive understanding of threat validation Leverage open source tools to test security detections Harness open source content to supplement detection and testing Book Description Threat validation is an indispensable component of every security detection program, ensuring a healthy detection pipeline. This comprehensive detection engineering guide will serve as an introduction for those who are new to detection validation, providing valuable guidelines to swiftly bring you up to speed. The book will show you how to apply the supplied frameworks to assess, test, and validate your detection program. It covers the entire life cycle of a detection, from creation to validation, with the help of real-world examples. Featuring hands-on tutorials, projects, and self-assessment questions, this guide will enable you to confidently validate the detections in your security program. By the end of this book, you'll have developed the skills necessary to test your security detection program and strengthen your organization's security measures. What you will learn Understand the detection engineering process Build a detection engineering test lab Learn how to maintain detections as code Understand how threat intelligence can be used to drive detection development Prove the effectiveness of detection capabilities to business leadership Learn how to limit attackers' ability to inflict damage by detecting any malicious activity early Who this book is for This book is for security analysts and engineers seeking to improve their organization's security posture by mastering the detection engineering lifecycle. To get started with this book, you'll need a basic understanding of cybersecurity concepts, along with some experience with detection and alert capabilities.

Anomaly detection (Computer security) --- Computer networks --- Security measures.

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This licentiate thesis by Massimiliano Raciti explores the application of anomaly detection in cyber-physical systems (CPS) as a security measure. It focuses on using machine learning and data mining algorithms to model normal system behavior and detect anomalies, providing a second line of defense against cyber-attacks. The research examines implementations in water management systems and smart metering devices, demonstrating the effectiveness of anomaly detection in identifying contamination events and electricity theft. Additionally, the thesis investigates parameter adaptation in Mobile Ad hoc Networks (MANETs) to enhance network survivability during attacks while managing energy consumption. The work is intended for professionals and researchers in computer science and information technology, aiming to improve security measures in critical systems.

Anomaly detection (Computer security) --- Computer security. --- Computer security

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This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. You will: Understand what anomaly detection is, why it it is important, and how it is applied Grasp the core concepts of machine learning. Master traditional machine learning approaches to anomaly detection using scikit-kearn. Understand deep learning in Python using Keras and PyTorch Process data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications.

Anomaly detection (Computer security) --- Python (Computer program language) --- Machine learning. --- Artificial intelligence. --- Python (Computer program language). --- Open source software. --- Artificial Intelligence. --- Machine Learning. --- Python. --- Open Source.

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Discover key information buried in the noise of data by learning a variety of anomaly detection techniques and using the Python programming language to build a robust service for anomaly detection against a variety of data types. The book starts with an overview of what anomalies and outliers are and uses the Gestalt school of psychology to explain just why it is that humans are naturally great at detecting anomalies. From there, you will move into technical definitions of anomalies, moving beyond "I know it when I see it" to defining things in a way that computers can understand. The core of the book involves building a robust, deployable anomaly detection service in Python. You will start with a simple anomaly detection service, which will expand over the course of the book to include a variety of valuable anomaly detection techniques, covering descriptive statistics, clustering, and time series scenarios. Finally, you will compare your anomaly detection service head-to-head with a publicly available cloud offering and see how they perform. The anomaly detection techniques and examples in this book combine psychology, statistics, mathematics, and Python programming in a way that is easily accessible to software developers. They give you an understanding of what anomalies are and why you are naturally a gifted anomaly detector. Then, they help you to translate your human techniques into algorithms that can be used to program computers to automate the process. You'll develop your own anomaly detection service, extend it using a variety of techniques such as including clustering techniques for multivariate analysis and time series techniques for observing data over time, and compare your service head-on against a commercial service. What You Will Learn Understand the intuition behind anomalies Convert your intuition into technical descriptions of anomalous data Detect anomalies using statistical tools, such as distributions, variance and standard deviation, robust statistics, and interquartile range Apply state-of-the-art anomaly detection techniques in the realms of clustering and time series analysis Work with common Python packages for outlier detection and time series analysis, such as scikit-learn, PyOD, and tslearn Develop a project from the ground up which finds anomalies in data, starting with simple arrays of numeric data and expanding to include multivariate inputs and even time series data Who This Book Is For For software developers with at least some familiarity with the Python programming language, and who would like to understand the science and some of the statistics behind anomaly detection techniques. Readers are not required to have any formal knowledge of statistics as the book introduces relevant concepts along the way.

Anomaly detection (Computer security) --- Computer programming. --- Python (Computer program language) --- Scripting languages (Computer science) --- Detection, Anomaly (Computer security) --- Computer security --- Computers --- Electronic computer programming --- Electronic data processing --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Programming