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040 _aEBZ
_beng
_cEBZ
020 _a9781000824971
_qelectronic book
020 _a1000824977
_qelectronic book
020 _a9781003121510
_qelectronic book
020 _a1003121519
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020 _a9781000824988
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020 _z0367638711
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024 7 _a10.1201/9781003121510
_2doi
037 _a9781003121510
_bTaylor & Francis
037 _a9781000824988
_bO'Reilly Media
050 4 _aTK5105.59
_b.T46 2023
072 7 _aCOM
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072 7 _aCOM
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072 7 _aUT
_2bicssc
049 _aMAIN
100 1 _aThomas, Tony,
_eauthor.
_964674
245 1 0 _aIntelligent mobile malware detection /
_cTony Thomas, Roopak Surendran and Teenu S. John, Mamoun Alazab.
250 _aFirst edition.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c2023.
264 4 _c©2023
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
341 0 _atextual
_2sapdv
_3EBSCOhost
490 0 _aSecurity, privacy, and trust in mobile communications
504 _aIncludes bibliographical references and index.
520 _aThe popularity of Android mobile phones has caused more cybercriminalstocreate malware applications that carry out various malicious activities. The attacks, whichescalatedafter the COVID-19 pandemic, proved there is great importance in protecting Android mobile devices from malware attacks. Intelligent Mobile Malware Detection will teach users how to develop intelligent Android malware detection mechanisms by using various graph and stochastic models. The book begins with an introduction to the Android operating system accompanied by the limitations of the state-of-the-art static malware detection mechanisms as well as a detailed presentation of a hybrid malware detection mechanism. The text then presents four different system call-based dynamic Android malware detection mechanisms using graph centrality measures, graph signal processing and graph convolutional networks. Further, the text shows how most of the Android malware can be detected by checking the presence of a unique subsequence of system calls in its system call sequence. All the malware detection mechanisms presented in the book are based on the authors' recent research. The experiments are conducted with the latest Android malware samples, andthe malware samples are collected from public repositories. The source codes are also provided for easy implementation of the mechanisms. This book will be highly useful to Android malware researchers, developers, students and cyber security professionals to explore and build defense mechanisms against the ever-evolving Android malware.
532 0 _3EBSCOhost
_a"EBSCO evaluates our products based on the Web Content Accessibility Guidelines (WCAG) and the related Section 508 and EN 301 549 regulations in the US and EU. Most EBSCO products are substantially conformant with WCAG 2.2 level AA." Source: https://connect.ebsco.com/s/article/EBSCO-VPATs?language=en_US. Last accessed April 22, 2025.
545 0 _aDr. Tony Thomas is currently associate professor in the School of Computer Science and Engineering, Kerala University of Digital Sciences, Innovation and Technology, India (formerly IIITM-K). He completed his master's and PhD degrees from IIT Kanpur. After completing his PhD, he carried out his post-doctoral research at the Korea Advanced Institute of Science and Technology. After that, he joined as a researcher at the General Motors Research Lab, Bangalore, India. He later moved to the School of Computer Engineering, Nanyang Technological University, Singapore as a research fellow. In 2011, he joined as an assistant professor at Indian Institute of Information Technology and Management-Kerala (IIITM-K). He is an associate editor and reviewer of several journals. He is a member of the Board of Studies of several universities. His current research interests include: malware analysis, biometrics, cryptography, quantum computation and machine learning applications in cyber security. He has published many research papers, book chapters and books in these domains. He is an author of the book Machine Learning Approaches in Cyber Security Analytics published by Springer. Dr. Roopak Surendran is currently working as a penetration tester at the Kerala Security Audit and Assurance Centre (K-SAAC) of the Kerala University of Digital Sciences Innovation and Technology. He has done his PhD research in Android malware analysis, which was funded by the Kerala state planning board. Before joining the PhD program, he completed his MPhil degree in computer science with a specialization in cyber security from Indian Institute of Information Technology and Management-Kerala. He published many research papers related to malware analysis and phishing detection. Also, he has developed Python-based tools and sandboxes to protect devices from phishing and malware attacks. His interests include: web application security, mobile application security, malware analysis and phishing detection. Ms. Teenu S. John holds an MTech degree in computer science with specialization in data security from TocH Institute of Science and Technology under Cochin University of Science and Technology, Kerala, India, and a BTech degree in information technology from the College of Engineering Perumon, under Cochin University of Science and Technology-Kerala, India. She is currently doing her PhD on adversarial malware detection at the Kerala University of Digital Sciences Innovation and Technology, formerly Indian Institute of Information Technology and Management-Kerala (IIITM-K). Her research interests include: malware analysis, machine learning for cyber security, data analytics and cyber threat detection. Dr. Mamoun Alazab is associate professor at the College of Engineering, IT and Environment, and is the director of the NT Academic Centre for Cyber Security and Innovation (ACCI) at Charles Darwin University, Australia. He received his PhD in computer science from the Federation University of Australia, School of Science, Information Technology and Engineering. He is a cyber security researcher and practitioner with industry and academic experience. Dr. Alazab's research is multidisciplinary focusing on cyber security including current and emerging issues in the cyber environment like cyber-physical systems and Internet of Things, with a focus on cybercrime detection and prevention. He has more than 300 research papers, 11 authored and edited books, as well as 3 patents. As of March 2022, 9256 citations appear on Google. His research over the years has contributed to the development of several successful secure commercial systems. His book, Malware Analysis Using Artificial Intelligence and Deep Learning, reached 40k downloads in about 1 year and was referred to by Microsoft research and Google research. He is the recipient of several prestigious awards including the NT Young Tall Poppy of the Year (2021) from the Australian Institute of Policy and Science (AIPS) and the Japan Society for the Promotion of Science (JSPS) fellowship through the Australian Academy of Science. He worked previously as a senior lecturer (Australian National University) and lecturer (Macquarie University). He is a senior member of the IEEE, and the founding chair of the IEEE Northern Territory (NT) Subsection. He serves as the associate editor of IEEE Transactions on Computational Social Systems, IEEE Transactions on Network and Service Management (TNSM), ACM Digital Threats: Research and Practice, and Complex & Intelligent Systems.
588 _aDescription based on online resource; title from digital title page (viewed on February 07, 2023).
650 0 _aMobile communication systems
_xSecurity measures.
_964675
650 0 _aMobile computing
_xSecurity measures.
_964676
650 0 _aComputer crimes
_xPrevention.
_964385
650 0 _aMalware (Computer software)
_964677
650 7 _aCOMPUTERS / Security / Viruses
_2bisacsh
_964678
650 7 _aCOMPUTERS / Networking / Security
_2bisacsh
700 1 _aJohn, Teenu S.,
_eauthor.
_964679
700 1 _aSurendran, Roopak,
_eauthor.
_964680
700 1 _aAlazab, Mamoun,
_d1980-
_eauthor.
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_964681
776 0 8 _iPrint version:
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856 4 0 _3EBSCOhost
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