Advances in Malware and Data-Driven Network Security - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Advances in Malware and Data-Driven Network Security write by Gupta, Brij B.. This book was released on 2021-11-12. Advances in Malware and Data-Driven Network Security available in PDF, EPUB and Kindle. Every day approximately three-hundred thousand to four-hundred thousand new malware are registered, many of them being adware and variants of previously known malware. Anti-virus companies and researchers cannot deal with such a deluge of malware – to analyze and build patches. The only way to scale the efforts is to build algorithms to enable machines to analyze malware and classify and cluster them to such a level of granularity that it will enable humans (or machines) to gain critical insights about them and build solutions that are specific enough to detect and thwart existing malware and generic-enough to thwart future variants. Advances in Malware and Data-Driven Network Security comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning, and AI as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats. Providing information on advances in malware and data-driven network security as well as future research directions, it is ideal for graduate students, academicians, faculty members, scientists, software developers, security analysts, computer engineers, programmers, IT specialists, and researchers who are seeking to learn and carry out research in the area of malware and data-driven network security.
Android Malware Detection using Machine Learning
Android Malware Detection using Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Android Malware Detection using Machine Learning write by ElMouatez Billah Karbab. This book was released on 2021-07-10. Android Malware Detection using Machine Learning available in PDF, EPUB and Kindle. The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
Cybersecurity in Intelligent Networking Systems
Cybersecurity in Intelligent Networking Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Cybersecurity in Intelligent Networking Systems write by Shengjie Xu. This book was released on 2023-01-24. Cybersecurity in Intelligent Networking Systems available in PDF, EPUB and Kindle. CYBERSECURITY IN INTELLIGENT NETWORKING SYSTEMS Help protect your network system with this important reference work on cybersecurity Cybersecurity and privacy are critical to modern network systems. As various malicious threats have been launched that target critical online services—such as e-commerce, e-health, social networks, and other major cyber applications—it has become more critical to protect important information from being accessed. Data-driven network intelligence is a crucial development in protecting the security of modern network systems and ensuring information privacy. Cybersecurity in Intelligent Networking Systems provides a background introduction to data-driven cybersecurity, privacy preservation, and adversarial machine learning. It offers a comprehensive introduction to exploring technologies, applications, and issues in data-driven cyber infrastructure. It describes a proposed novel, data-driven network intelligence system that helps provide robust and trustworthy safeguards with edge-enabled cyber infrastructure, edge-enabled artificial intelligence (AI) engines, and threat intelligence. Focusing on encryption-based security protocol, this book also highlights the capability of a network intelligence system in helping target and identify unauthorized access, malicious interactions, and the destruction of critical information and communication technology. Cybersecurity in Intelligent Networking Systems readers will also find: Fundamentals in AI for cybersecurity, including artificial intelligence, machine learning, and security threats Latest technologies in data-driven privacy preservation, including differential privacy, federated learning, and homomorphic encryption Key areas in adversarial machine learning, from both offense and defense perspectives Descriptions of network anomalies and cyber threats Background information on data-driven network intelligence for cybersecurity Robust and secure edge intelligence for network anomaly detection against cyber intrusions Detailed descriptions of the design of privacy-preserving security protocols Cybersecurity in Intelligent Networking Systems is an essential reference for all professional computer engineers and researchers in cybersecurity and artificial intelligence, as well as graduate students in these fields.
Game Theory and Machine Learning for Cyber Security
Game Theory and Machine Learning for Cyber Security - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Game Theory and Machine Learning for Cyber Security write by Charles A. Kamhoua. This book was released on 2021-09-08. Game Theory and Machine Learning for Cyber Security available in PDF, EPUB and Kindle. GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.
Data Analytics and Decision Support for Cybersecurity
Data Analytics and Decision Support for Cybersecurity - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Analytics and Decision Support for Cybersecurity write by Iván Palomares Carrascosa. This book was released on 2017-08-01. Data Analytics and Decision Support for Cybersecurity available in PDF, EPUB and Kindle. The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detection, E-Learning, Online Examinations, Cybersecurity noisy data removal, Secure Smart Power Systems, Security Visualization and Monitoring. Researchers and professionals alike will find the chapters an essential read for further research on the topic.
