Android Malware Detection using Machine Learning - Karbab, Elmouatez Billah

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Présentation Android Malware Detection Using Machine Learning Format Broché
- Livre Informatique

Livre Informatique - Karbab, Elmouatez Billah - 01/07/2022 - Broché - Langue : Anglais

Auteur(s) : Karbab, Elmouatez Billah - Mouheb, Djedjiga - Derhab, Abdelouahid - Debbabi, Mourad

Editeur : Springer International Publishing

Langue : Anglais

Parution : 01/07/2022

Format : Moyen, de 350g à 1kg

Nombre de pages : 216

Expédition : 335

Dimensions : 23.5 x 15.5 x 1.2

ISBN : 9783030746667

Résumé :

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.

Biographie:
Associate Professor at the Computer Science Department of Laval University, Canada...

Sommaire:

Dr. ElMouatez Billah Karbab is a researcher at Concordia University, Montreal, Canada. His research focuses on applied machine learning techniques on malware fingerprinting and mobile & IoT security. He is a research scientist at the National Cyber Forensic and Training Alliance (NCFTA) of Canada, an international organization which focuses on the investigation of cyber-crimes. He is also serving as a data scientist and cyber-security specialist at NCFTA Canada. He served as an associate researcher at Research Centre for Scientific and Technical Information (CERIST), Algeria, where he worked on international projects in collaboration with the university of Cape Town, South Africa, and Heudiasyc Lab, France. ElMouatez has published many peer-reviewed research articles in international journals and conferences on malware fingerprinting using machine learning techniques, cyber security, and embedded systems.

Mourad Debbabi is Professor at the Concordia Institute for Information Systems Engineering and Interim Dean of the Gina Cody School of Engineering and Computer Science. He holds the NSERC/Hydro-Quebec Thales Senior Industrial Research Chair in Smart Grid Security. He is a member of the Cybersecurity Advisory Board to the Minister of Digital Transformation, and a member of the Advisory Board of the Cybercrime Council. He serves/served on the boards of Canadian Police College, PROMPT Qu?bec and Calcul Qu?bec. He is the founder and Director of the Security Research Centre at Concordia University. Dr. Debbabi holds Ph.D. and M.Sc. degrees in computer science from Universit? Paris-XI Orsay, France, and an Engineering degree from Universit? de Constantine. He has published 6 books and more than 300 peer-reviewed research articles in international journals and conferences on cyber security, cyber forensics, smart grids, privacy, cryptographic protocols, threat intelligence generation, malware analysis, reverse engineering, specification andverification of safety-critical systems, programming languages and type theory. He has supervised to successful completion 33 Ph.D. students, 76 Master students and 14 Postdoctoral Fellows. He served as a Senior Scientist at the Panasonic Information and Network Technologies Laboratory, Princeton, New Jersey, USA...

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Android Malware Detection using Machine Learning - Karbab, Elmouatez Billah

Avis sur Android Malware Detection Using Machine Learning Format Broché - Livre Informatique

Présentation Android Malware Detection Using Machine Learning Format Broché - Livre Informatique

Présentation Android Malware Detection Using Machine Learning Format Broché
- Livre Informatique