Behavioral Malware Detection and Classification using Windows Prefetch Files

Malware analysis is an important aspect of cyber security and is a key component in securing systems from attackers. New malware signatures are being created continuously and detection techniques need to keep pace with them. The primary objective is to propose a solution which detects malicious files in real time by evaluating each file. Other objectives are to assess the threat level of the malware and recognize the family of malicious file. Hence, to cover all the needs and to fulfill the motivation, a deep neural network is more suitable to detect and classify the malware. Convolutional neural network-based system MalNet-D is designed to detect the presence of malware, and subsequently, to classify the detected malware into the family in which it belongs, a variation of MalNet-D termed as MalNet-C is proposed. Images of the executable files, both malign and benign, are used as input data, which is trained by the respective MalNet. This is used to detect and classify malware into families. The system achieved 93% accuracy in malware detection and 96% accuracy in malware classification.

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A framework for Android Malware detection and classification

2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS) ◽

10.1109/icetas.2018.8629270 ◽

2018 ◽

Cited By ~ 1

Author(s):

Muhammad Murtaz ◽

Hassan Azwar ◽

Syed Baqir Ali ◽

Saad Rehman

Keyword(s):

Malware Detection ◽

Android Malware ◽

Android Malware Detection ◽

Malware Detection And Classification

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A Novel Malware Analysis Framework for Malware Detection and Classification using Machine Learning Approach

Proceedings of the 19th International Conference on Distributed Computing and Networking - ICDCN '18 ◽

10.1145/3154273.3154326 ◽

2018 ◽

Cited By ~ 6

Author(s):

Kamalakanta Sethi ◽

Shankar Kumar Chaudhary ◽

Bata Krishan Tripathy ◽

Padmalochan Bera

Keyword(s):

Machine Learning ◽

Malware Detection ◽

Learning Approach ◽

Malware Analysis ◽

Analysis Framework ◽

Machine Learning Approach ◽

Malware Detection And Classification

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Feature Reduction and Optimization of Malware Detection System Using Ant Colony Optimization and Rough Sets

International Journal of Information Security and Privacy ◽

10.4018/ijisp.2020070106 ◽

2020 ◽

Vol 14 (3) ◽

pp. 95-114

Author(s):

Ravi Kiran Varma Penmatsa ◽

Akhila Kalidindi ◽

S. Kumar Reddy Mallidi

Keyword(s):

Ant Colony Optimization ◽

Detection System ◽

Malware Detection ◽

Feature Reduction ◽

Ant Colony ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Feature Significance ◽

Malware Detection And Classification ◽

Malicious Program

Malware is a malicious program that can cause a security breach of a system. Malware detection and classification is one of the burning topics of research in information security. Executable files are the major source of input for static malware detection. Machine learning techniques are very efficient in behavioral-based malware detection and need a dataset of malware with different features. In windows, malware can be detected by analyzing the portable executable (PE) files. This work contributes to identifying the minimum feature set for malware detection employing a rough set dependent feature significance combined with Ant Colony Optimization (ACO) as the heuristic-search technique. A malware dataset named claMP with both integrated features and raw features was considered as the benchmark dataset for this work. The analytical results prove that 97.15% and 92.8% data size optimization has been achieved with a minimum loss of accuracy for claMP integrated and raw datasets, respectively.

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