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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.
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.
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