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Network Anomaly Detection

Network Anomaly Detection

Dhruba Kumar Bhattacharyya; Jugal Kumar Kalita

CRC Press Inc
2013
sidottu
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 layersThe pros and cons of various machine learning techniques and algorithmsA taxonomy of attacks based on their characteristics and behaviorFeature selection algorithmsHow to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection systemPractical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performanceImportant unresolved issues and research challenges that need to be overcome to provide better protection for networksExamining 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.
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DDoS Attacks

DDoS Attacks

Dhruba Kumar Bhattacharyya; Jugal Kumar Kalita

Productivity Press
2016
sidottu
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.
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Economic & Social Issues in India

Economic & Social Issues in India

Dhrub Kumar

Ramesh Publishing House
2020
nidottu
'Economic & Social Issues in India' is a subject which is becoming very important in many exams like Civil Services, Bank Officers Exam etc. The present book 'Economic & Social Issues in India' has been specially developed keeping in mind the requirements of students, exam-aspirants and other readers with academic as well as competitions' point of view. The book is especially useful for the aspirants of various competitive exams where this subject forms an essential part of the examination. The main aim of the book is to present this specialised subject in a reader-friendly manner to make the readers grasp its various topics thoroughly. The book comprises a wide spectrum of chapters and topics on which questions are frequently asked in various exams. The book will act as an efficient tool to enhance your knowledge of the subject and do better preparation of your respective exam. The book will definitely prove to be a boon to the inquisitive students, competitive exam-aspirants, and other readers in improving and enhancing their knowledge of the subject and will immensely help them perform better in their respective exams and competitions.
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Gene Expression Data Analysis

Gene Expression Data Analysis

Pankaj Barah; Dhruba Kumar Bhattacharyya; Jugal Kumar Kalita

CRC Press
2021
sidottu
Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge.Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data.Key Features: 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 multidisciplinary researchers and practitioners in computer science and biological sciences
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Gene Expression Data Analysis

Gene Expression Data Analysis

Pankaj Barah; Dhruba Kumar Bhattacharyya; Jugal Kumar Kalita

TAYLOR FRANCIS LTD
2024
nidottu
Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge.Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data.Key Features: 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 multidisciplinary researchers and practitioners in computer science and biological sciences
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AI-Driven Security

AI-Driven Security

Rajesh Daruvuri; Kiran Kumar Patibandla

Lap Lambert Academic Publishing
2025
pokkari
In an era of rapid advancements in artificial intelligence, cybersecurity threats continue to evolve, particularly in the domain of large language models, financial systems, and password security. This book delves into the critical aspects of AI-driven security, offering insights into risk management, defensive mechanisms, and ethical AI deployment. Through a structured approach, this book explores:1.The vulnerabilities of financial large language models and prompt injection attack defenses.2.Ownership protection mechanisms through watermarking and fingerprinting techniques.3.AI-powered security policy automation for operating systems.4.Password security analysis leveraging hybrid rule-based and ML techniques. With extensive research and case studies, "AI-Driven Security" provides a comprehensive guide to fortifying AI applications against cyber threats, making it a must-read for cybersecurity professionals, AI researchers, and industry practitioners.
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