Heuristic metamorphic malware detection based on statistics of assembly instructions using classification algorithms

The amazing advances of mobile phones enable their wide utilize. Since mobiles are joined with pariah applications, bundles of security and insurance issues are incited. But, current mobile malware analysis and detection advances are as yet flawed, incapable, and incomprehensive. On account of particular qualities of mobiles such as constrained assets, user action and neighborhood correspondence ability, consistent system network, versatile malware detection faces new difficulties, particularly on remarkable runtime malware area. This paper provides overview on malware classification, methodologies of assessment, analysis and on and off device detection methods on android. The work mainly focuses on different classification algorithms which are used as a part of dynamic malware detection on android.

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Metamorphic Malware Detection Based on Support Vector Machine Classification of Malware Sub-Signatures

TELKOMNIKA (Telecommunication Computing Electronics and Control) ◽

10.12928/telkomnika.v14i3.3850 ◽

2016 ◽

Vol 14 (3) ◽

pp. 1157 ◽

Cited By ~ 4

Author(s):

Ban Mohammed Khammas ◽

Alireza Monemi ◽

Ismahani Ismail ◽

Sulaiman Mohd Nor ◽

M.N. Marsono

Keyword(s):

Support Vector Machine ◽

Malware Detection ◽

Support Vector ◽

Support Vector Machine Classification ◽

Metamorphic Malware

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Metamorphic Malware Detection Using LLVM IR and Hidden Markov Model

Proceedings of the International Congress on Information and Communication Technology - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-10-0767-5_44 ◽

2016 ◽

pp. 411-421 ◽

Cited By ~ 1

Author(s):

Ginika Mahajan ◽

Raja

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Malware Detection ◽

Metamorphic Malware

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Nonnegative matrix factorization and metamorphic malware detection

Journal of Computer Virology and Hacking Techniques ◽

10.1007/s11416-019-00331-0 ◽

2019 ◽

Vol 15 (3) ◽

pp. 195-208

Author(s):

Yeong Tyng Ling ◽

Nor Fazlida Mohd Sani ◽

Mohd Taufik Abdullah ◽

Nor Asilah Wati Abdul Hamid

Keyword(s):

Matrix Factorization ◽

Nonnegative Matrix Factorization ◽

Malware Detection ◽

Nonnegative Matrix ◽

Metamorphic Malware

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Metamorphic Malware Detection Using Minimal Opcode Statistical Patterns

Security and Privacy Management, Techniques, and Protocols - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-5583-4.ch014 ◽

2018 ◽

pp. 337-359

Author(s):

Mahmood Fazlali ◽

Peyman Khodamoradi

Keyword(s):

Feature Selection ◽

Decision Tree ◽

High Speed ◽

Detection Method ◽

Malware Detection ◽

Feature Selection Method ◽

Selection Method ◽

Input File ◽

Feature Vectors ◽

Metamorphic Malware

High-speed and accurate malware detection for metamorphic malware are two goals in antiviruses. To reach beyond this issue, this chapter presents a new malware detection method that can be summarized as follows: (1) Input file is disassembled and classified to obtain the minimal opcode pattern as feature vectors; (2) a forward feature selection method (i.e., maximum relevancy and minimum redundancy) is applied to remove the redundant as well as irrelevant features; and (3) the process ends by classification through using decision tree. The results indicate the proposed method can effectively detect metamorphic malware in terms of speed, efficiency, and accuracy.

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