Adaptive Semantics-Aware Malware Classification

Detection of Intrusions and Malware, and Vulnerability Assessment - Lecture Notes in Computer Science ◽

10.1007/978-3-319-40667-1_21 ◽

2016 ◽

pp. 419-439 ◽

Cited By ~ 9

Author(s):

Bojan Kolosnjaji ◽

Apostolis Zarras ◽

Tamas Lengyel ◽

George Webster ◽

Claudia Eckert

Keyword(s):

Malware Classification

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Methodology for Malware Classification using a Random Forest Classifier

2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) ◽

10.1109/ropec.2018.8661441 ◽

2018 ◽

Cited By ~ 4

Author(s):

Carlos Domenick Morales-Molina ◽

Diego Santamaria-Guerrero ◽

Gabriel Sanchez-Perez ◽

Hector Perez-Meana ◽

Aldo Hernandez-Suarez

Keyword(s):

Random Forest ◽

Random Forest Classifier ◽

Malware Classification

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Malware Detection Based on Code Visualization and Two-Level Classification

Information ◽

10.3390/info12030118 ◽

2021 ◽

Vol 12 (3) ◽

pp. 118

Author(s):

Vassilios Moussas ◽

Antonios Andreatos

Keyword(s):

Web Applications ◽

Detection System ◽

Malware Detection ◽

Image Features ◽

Malicious Code ◽

Malware Analysis ◽

Malicious Software ◽

Antivirus Software ◽

Malware Classification ◽

Artificial Neural Network Ann

Malware creators generate new malicious software samples by making minor changes in previously generated code, in order to reuse malicious code, as well as to go unnoticed from signature-based antivirus software. As a result, various families of variations of the same initial code exist today. Visualization of compiled executables for malware analysis has been proposed several years ago. Visualization can greatly assist malware classification and requires neither disassembly nor code execution. Moreover, new variations of known malware families are instantly detected, in contrast to traditional signature-based antivirus software. This paper addresses the problem of identifying variations of existing malware visualized as images. A new malware detection system based on a two-level Artificial Neural Network (ANN) is proposed. The classification is based on file and image features. The proposed system is tested on the ‘Malimg’ dataset consisting of the visual representation of well-known malware families. From this set some important image features are extracted. Based on these features, the ANN is trained. Then, this ANN is used to detect and classify other samples of the dataset. Malware families creating a confusion are classified by a second level of ANNs. The proposed two-level ANN method excels in simplicity, accuracy, and speed; it is easy to implement and fast to run, thus it can be applied to antivirus software, smart firewalls, web applications, etc.

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