Malware Analysis Using Artificial Intelligence and Deep Learning

by Mark Stamp

Author	: Mark Stamp
Publisher	: Springer Nature
Total Pages	: 651
Release	: 2020-12-20
Genre	: Computers
ISBN	: 3030625826

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This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.

Malware Detection

by Mihai Christodorescu

Author	: Mihai Christodorescu
Publisher	: Springer Science & Business Media
Total Pages	: 307
Release	: 2007-03-06
Genre	: Computers
ISBN	: 0387445994

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This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.

Malware Data Science

by Joshua Saxe

Author	: Joshua Saxe
Publisher	: No Starch Press
Total Pages	: 274
Release	: 2018-09-25
Genre	: Computers
ISBN	: 1593278594

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Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You'll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.

Android Malware Detection using Machine Learning

by ElMouatez Billah Karbab

Author	: ElMouatez Billah Karbab
Publisher	: Springer Nature
Total Pages	: 212
Release	: 2021-07-10
Genre	: Computers
ISBN	: 303074664X

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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.

Advances in Malware and Data-Driven Network Security

by Gupta, Brij B.

Author	: Gupta, Brij B.
Publisher	: IGI Global
Total Pages	: 304
Release	: 2021-11-12
Genre	: Computers
ISBN	: 1799877914

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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.

Security of Information and Networks

by Atilla Eli

Author	: Atilla Eli
Publisher	: Trafford Publishing
Total Pages	: 388
Release	: 2008
Genre	: Computers
ISBN	: 1425141099

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This book is a select collection of edited papers from the International Conference on Security of Information and Networks (SIN 2007) on the main theme of Information Assurance, Security, and Public Policy. SIN 2007 was hosted by the Eastern Mediterranean University in Gazimagusa, North Cyprus and co-organized by the Istanbul Technical University, Turkey. While SIN 2007 covered all areas of information and network security, the papers included here focused on the following topics: - cryptology: design and analysis of cryptographic algorithms, hardware and software implementations of cryptographic algorithms, and steganography; - network security: authentication, authorization and access control, privacy, intrusion detection, grid security, and mobile and personal area networks; - IT governance: information security management systems, risk and threat analysis, and information security policies. They represent an interesting mix of innovative academic research and experience reports from practitioners. This is further complemented by a number of invited papers providing excellent overviews: - Elisabeth Oswald, University of Bristol, Bristol, UK: Power Analysis Attack: A Very Brief Introduction; - Marc Joye, Thomson R&D, France: On White-Box Cryptography; - Bart Preneel, Katholieke Universiteit Leuven, Leuven, Belgium: Research Challenges in Cryptology; - Mehmet Ufuk Caglayan, Bogazici University, Turkey: Secure Routing in Ad Hoc Networks and Model Checking. The papers are organized in a logical sequence covering Ciphers; Mobile Agents & Networks; Access Control and Security Assurance; Attacks, Intrusion Detection, and Security Recommendations; and, Security Software, Performance, and Experience.

Machine Learning and Security

by Clarence Chio

Author	: Clarence Chio
Publisher	: "O'Reilly Media, Inc."
Total Pages	: 394
Release	: 2018-01-26
Genre	: Computers
ISBN	: 1491979852

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Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions

Handbook of Research on Machine and Deep Learning Applications for Cyber Security

by Ganapathi, Padmavathi

Author	: Ganapathi, Padmavathi
Publisher	: IGI Global
Total Pages	: 506
Release	: 2019-07-26
Genre	: Computers
ISBN	: 1522596135

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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.

Implications of Artificial Intelligence for Cybersecurity

by National Academies of Sciences, Engineering, and Medicine

Author	: National Academies of Sciences, Engineering, and Medicine
Publisher	: National Academies Press
Total Pages	: 99
Release	: 2020-01-27
Genre	: Computers
ISBN	: 0309494508

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In recent years, interest and progress in the area of artificial intelligence (AI) and machine learning (ML) have boomed, with new applications vigorously pursued across many sectors. At the same time, the computing and communications technologies on which we have come to rely present serious security concerns: cyberattacks have escalated in number, frequency, and impact, drawing increased attention to the vulnerabilities of cyber systems and the need to increase their security. In the face of this changing landscape, there is significant concern and interest among policymakers, security practitioners, technologists, researchers, and the public about the potential implications of AI and ML for cybersecurity. The National Academies of Sciences, Engineering, and Medicine convened a workshop on March 12-13, 2019 to discuss and explore these concerns. This publication summarizes the presentations and discussions from the workshop.