Introduces the concept of intrusion detection, discusses various approaches for intrusion detection systems (IDS), and presents the architecture and implementation of IDS. This title also includes the performance comparison of various IDS via simulation.
Detection of anomalies in data is one of the fundamental machine learning tasks. Anomaly detection provides the core technology for a broad spectrum of security-centric applications. In this dissertation, we examine various aspects of anomaly based intrusion detection in computer security. First, we present a new approach to learn program behavior for intrusion detection. Text categorization techniques are adopted to convert each process to a vector and calculate the similarity between two program activities. Then the k-nearest neighbor classifier is employed to classify program behavior as normal or intrusive. We demonstrate that our approach is able to effectively detect intrusive program behavior while a low false positive rate is achieved. Second, we describe an adaptive anomaly detection framework that is de- signed to handle concept drift and online learning for dynamic, changing environments. Through the use of unsupervised evolving connectionist systems, normal behavior changes are efficiently accommodated while anomalous activities can still be recognized. We demonstrate the performance of our adaptive anomaly detection systems and show that the false positive rate can be significantly reduced.
The Internet of Things (IoT) is a complex paradigm where billions of devices are connected to a network. These connected devices form an intelligent system of systems that share the data without human-to-computer or human-to-human interaction. These systems extract meaningful data that can transform human lives, businesses, and the world in significant ways. However, the reality of IoT is prone to countless cyber-attacks in the extremely hostile environment like the internet. The recent hack of 2014 Jeep Cherokee, iStan pacemaker, and a German steel plant are a few notable security breaches. To secure an IoT system, the traditional high-end security solutions are not suitable, as IoT devices are of low storage capacity and less processing power. Moreover, the IoT devices are connected for longer time periods without human intervention. This raises a need to develop smart security solutions which are light-weight, distributed and have a high longevity of service. Rather than per-device security for numerous IoT devices, it is more feasible to implement security solutions for network data. The artificial intelligence theories like Machine Learning and Deep Learning have already proven their significance when dealing with heterogeneous data of various sizes. To substantiate this, in this research, we have applied concepts of Deep Learning and Transmission Control Protocol/Internet Protocol (TCP/IP) to build a light-weight distributed security solution with high durability for IoT network security. First, we have examined the ways of improving IoT architecture and proposed a light-weight and multi-layered design for an IoT network. Second, we have analyzed the existingapplications of Machine Learning and Deep Learning to the IoT and Cyber-Security. Third, we have evaluated deep learning's Gated Recurrent Neural Networks (LSTM and GRU) on the DARPA/KDD Cup '99 intrusion detection data set for each layer in the designed architecture. Finally, from the evaluated metrics, we have proposed the best neural network design suitable for the IoT Intrusion Detection System. With an accuracy of 98.91% and False Alarm Rate of 0.76 %, this unique research outperformed the performance results of existing methods over the KDD Cup '99 dataset. For this first time in the IoT research, the concepts of Gated Recurrent Neural Networks are applied for the IoT security.
This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity.
As the advancement of technology continues, cyber security continues to play a significant role in todays world. With society becoming more dependent on the internet, new opportunities for virtual attacks can lead to the exposure of critical information. Machine and deep learning techniques to prevent this exposure of information are being applied to address mounting concerns in computer security. The Handbook of Research on Machine and Deep Learning Applications for Cyber Security is a pivotal reference source that provides vital research on the application of machine learning techniques for network security research. While highlighting topics such as web security, malware detection, and secure information sharing, this publication explores recent research findings in the area of electronic security as well as challenges and countermeasures in cyber security research. It is ideally designed for software engineers, IT specialists, cybersecurity analysts, industrial experts, academicians, researchers, and post-graduate students.
With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents machine learning techniques in depth to help you more effectively detect and counter network intrusion. In this book, you’ll learn about: Network anomalies and vulnerabilities at various layers The pros and cons of various machine learning techniques and algorithms A taxonomy of attacks based on their characteristics and behavior Feature selection algorithms How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance Important unresolved issues and research challenges that need to be overcome to provide better protection for networks Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.
Network management becomes difficult when the size of the network grows. An ill-managed network opens several ways for the adversaries to exploit the security vulnerabilities for intrusions. Also, low-priced Internet subscriptions and publicly available attack tools enable the attackers to launch undiscovered or zero-day attacks in a network. Machine learning based approaches are well-suited to detect such kinds of undiscovered attacks. However, the hand-engineering involved in machine learning approaches for the proper selection of features from the network traffic puts a constraint on the accuracy of attack detection. The recently emerged networking paradigm named as software-defined networks (SDN) and the reincarnation of the neural network as deep learning (DL) promise to revolutionize the relevant industries. The SDN centralizes the network management and controls the network from a logically single point. The DL-based approach significantly improves the selection of features for the classification or prediction in an unsupervised manner. In our work, we utilize the benefits offered by the SDN and DL for the design and implementation of a network intrusion detection system (NIDS). The NIDS, implemented as an SDN application, can monitor the entire network for intrusions from a single point. Using the DL-based approach for the implementation helps in proper feature selection from a large traffic feature set and produces high accuracy with very low false alarms in intrusion detection. Before a real-world implementation of the NIDS, we develop a DL-based NIDS using a benchmark intrusion dataset (NSL-KDD) to explore the applicability of a DL-based approach for the NIDS implementation. An evaluation of the attack impact on network services running in the SDN environment is also performed. We analyze the response time and loss of service delivery in different attack scenarios. Finally, we discuss the implementation of a light-weight testbed for network security experiments developed with the tools used in an SDN infrastructure.
The State of the Art in Intrusion Prevention and Detection analyzes the latest trends and issues surrounding intrusion detection systems in computer networks, especially in communications networks. Its broad scope of coverage includes wired, wireless, and mobile networks; next-generation converged networks; and intrusion in social networks. Presenting cutting-edge research, the book presents novel schemes for intrusion detection and prevention. It discusses tracing back mobile attackers, secure routing with intrusion prevention, anomaly detection, and AI-based techniques. It also includes information on physical intrusion in wired and wireless networks and agent-based intrusion surveillance, detection, and prevention. The book contains 19 chapters written by experts from 12 different countries that provide a truly global perspective. The text begins by examining traffic analysis and management for intrusion detection systems. It explores honeypots, honeynets, network traffic analysis, and the basics of outlier detection. It talks about different kinds of IDSs for different infrastructures and considers new and emerging technologies such as smart grids, cyber physical systems, cloud computing, and hardware techniques for high performance intrusion detection. The book covers artificial intelligence-related intrusion detection techniques and explores intrusion tackling mechanisms for various wireless systems and networks, including wireless sensor networks, WiFi, and wireless automation systems. Containing some chapters written in a tutorial style, this book is an ideal reference for graduate students, professionals, and researchers working in the field of computer and network security.
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.
In today's world, a computer is highly exposed to attacks. In here, I try to build a predictive model to identify if the connection coming is an attack or genuine. Machine learning is that part of computer science in which instead of programming a machine we provide the ability to learn. Knowingly or unknowingly machine learning has become a part of our day to day lives. It could be in many ways like predicting stock market or image recognition while uploading a picture in Facebook and so on. Deep learning is a new concept which is trending these days, which moves a step towards the main aim of Machine Learning which is artificial intelligence. This machine learning/artificial intelligence can be used to make intrusion detection in a network more intelligent. We use different machine learning techniques including deep learning to figure out which approach is best for intrusion detection. To do this, we take a network intrusion dataset by Lincoln Labs who created an artificial set up to imitate U.S. Air Force LAN and get the TCP dumps generated. This also includes simulations of various types of attacks. We apply different machine learning algorithms on this data. And choose the machine learning algorithm which is most efficient to build a predictive model for intrusion detection. Now to the same dataset, we will apply Deep Learning mechanisms to build a predictive model with the algorithm that works the best for this data, after comparing the results generated by various deep learning algorithms. We build tool for each of the models (i.e. machine learning and deep learning). Now, the two tools one generated by machine learning and other by deep learning will be compared for accuracy.
This book presents state-of-the-art research on intrusion detection using reinforcement learning, fuzzy and rough set theories, and genetic algorithm. Reinforcement learning is employed to incrementally learn the computer network behavior, while rough and fuzzy sets are utilized to handle the uncertainty involved in the detection of traffic anomaly to secure data resources from possible attack. Genetic algorithms make it possible to optimally select the network traffic parameters to reduce the risk of network intrusion. The book is unique in terms of its content, organization, and writing style. Primarily intended for graduate electrical and computer engineering students, it is also useful for doctoral students pursuing research in intrusion detection and practitioners interested in network security and administration. The book covers a wide range of applications, from general computer security to server, network, and cloud security.
Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems is a fertile ground where many tasks can be formulated as learning problems and approached in terms of machine learning algorithms. This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the reliability, security, performance, and privacy issues of cyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state-of-the-practice in this significant area, and giving a classification of existing work. Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks.
"This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets"--Provided by publishe
"Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This book has a strong focus on information processing and combines and extends results from computer security. The first part of the book surveys the data sources, the learning and mining methods, evaluation methodologies, and past work relevant for computer security. The second part of the book consists of articles written by the top researchers working in this area. These articles deals with topics of host-based intrusion detection through the analysis of audit trails, of command sequences and of system calls as well as network intrusion detection through the analysis of TCP packets and the detection of malicious executables. This book fills the great need for a book that collects and frames work on developing and applying methods from machine learning and data mining to problems in computer security.
Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key Features Manage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineering Automate your daily workflow by addressing various security challenges using the recipes covered in the book Book Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learn Learn how to build malware classifiers to detect suspicious activities Apply ML to generate custom malware to pentest your security Use ML algorithms with complex datasets to implement cybersecurity concepts Create neural networks to identify fake videos and images Secure your organization from one of the most popular threats – insider threats Defend against zero-day threats by constructing an anomaly detection system Detect web vulnerabilities effectively by combining Metasploit and ML Understand how to train a model without exposing the training data Who this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.
This book constitutes the refereed proceedings of the 18th Australian Joint Conference on Artificial Intelligence, AI 2005, held in Sydney, Australia in December 2005. The 77 revised full papers and 119 revised short papers presented together with the abstracts of 3 keynote speeches were carefully reviewed and selected from 535 submissions. The papers are catgorized in three broad sections, namely: AI foundations and technologies, computational intelligence, and AI in specialized domains. Particular topics addressed by the papers are logic and reasoning, machine learning, game theory, robotic technology, data mining, neural networks, fuzzy theory and algorithms, evolutionary computing, Web intelligence, decision making, pattern recognition, agent technology, and AI applications.
The two-volume set LNCS 3644 and LNCS 3645 constitute the refereed proceedings of the International Conference on Intelligent Computing, ICIC 2005, held in Hefei, China, in August 2005. The program committee selected 215 carefully revised full papers for presentation in two volumes from over 2000 submissions, based on rigorous peer reviews. The first volume includes all the contributions related with perceptual and pattern recognition, informatics theories and applications computational neuroscience and bioscience, models and methods, and learning systems. The second volume collects the papers related with genomics and proteomics, adaptation and decision making, applications and hardware, and other applications.
The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.
This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites – which can determine satellites’ current status and predict their failure based on telemetry data – is one of the most important current issues in aerospace engineering. This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data.