An introduction to the Central Dogma of molecular biology and information flow in biological systems. A systematic overview of the methods for generating gene expression data. Background knowledge on statistical modeling and machine learning techniques. Detailed methodology of analyzing gene expression data with an example case study. Clustering methods for finding co-expression patterns from microarray, bulkRNA and scRNA data. A large number of practical tools, systems and repositories that are useful for computational biologists to create, analyze and validate biologically relevant gene expression patterns. Suitable for multi-disciplinary researchers and practitioners in computer science and biological sciences.

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.

DDoS Attacks: Evolution, Detection, Prevention, Reaction, and Tolerance discusses the evolution of distributed denial-of-service (DDoS) attacks, how to detect a DDoS attack when one is mounted, how to prevent such attacks from taking place, and how to react when a DDoS attack is in progress, with the goal of tolerating the attack. It introduces types and characteristics of DDoS attacks, reasons why such attacks are often successful, what aspects of the network infrastructure are usual targets, and methods used to launch attacks. The book elaborates upon the emerging botnet technology, current trends in the evolution and use of botnet technology, its role in facilitating the launching of DDoS attacks, and challenges in countering the role of botnets in the proliferation of DDoS attacks. It introduces statistical and machine learning methods applied in the detection and prevention of DDoS attacks in order to provide a clear understanding of the state of the art. It presents DDoS reaction and tolerance mechanisms with a view to studying their effectiveness in protecting network resources without compromising the quality of services. To practically understand how attackers plan and mount DDoS attacks, the authors discuss the development of a testbed that can be used to perform experiments such as attack launching, monitoring of network traffic, and detection of attacks, as well as for testing strategies for prevention, reaction, and mitigation. Finally, the authors address current issues and challenges that need to be overcome to provide even better defense against DDoS attacks.

The two-volume set of LNCS 11941 and 11942 constitutes the refereed proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, held in Tezpur, India, in December 2019. The 131 revised full papers presented were carefully reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning; Soft and Evolutionary Computing; Image Processing; Medical Image Processing; Bioinformatics and Biomedical Signal Processing; Information Retrieval; Remote Sensing; Signal and Video Processing; and Smart and Intelligent Sensors.

The two-volume set of LNCS 11941 and 11942 constitutes the refereed proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, held in Tezpur, India, in December 2019. The 131 revised full papers presented were carefully reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning; Soft and Evolutionary Computing; Image Processing; Medical Image Processing; Bioinformatics and Biomedical Signal Processing; Information Retrieval; Remote Sensing; Signal and Video Processing; and Smart and Intelligent Sensors.