An Efficient Malware Detection Technique using Complex Network-based Approach

Mohanasruthi, V.; Chakraborty, Abhishek; Thanudas, B.; Sreelal, S.; Manoj, B. S.

doi:10.1109/ncc48643.2020.9056080

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Newest articles on the ML-based malware detection also report innovative feature spaces. In [145], the authors proposed an Application Program Interface Call Transition Matrix (API-CTM) to generate network topology and analyse various network metrics to extract features. A novel malware detection method based on audio signal processing is presented in [146].…”

Section: B Researching New Features For Shallow Machine Learningmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

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Section: B Researching New Features For Shallow Machine Learningmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

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“…The newest research on the machine learning-based malware detection focus on innovative feature spaces. Mohanasruthi et al, proposed an Application Program Interface Call Transition Matrix (API-CTM) [30], Farrokhmanesh et al, proposed to convert malware data bytes into audio signals [31]; however, the shortcoming is that the detection methods based on machine learning still rely on feature engineering and on complex or expert features to complete the learning task. The final effect of the machine learning based detection model is related to the selection of selected features, which is very subjective.…”

Section: Shallow Machine Learning Based Malware Detection Approachesmentioning

confidence: 99%

MalCaps: A Capsule Network Based Model for the Malware Classification

Zhang

Chen

et al. 2021

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The research on malware detection enabled by deep learning has become a hot issue in the field of network security. The existing malware detection methods based on deep learning suffer from some issues, such as weak ability of deep feature extraction, relatively complex model, and insufficient ability of model generalization. Traditional deep learning architectures, such as convolutional neural networks (CNNs) variants, do not consider the spatial hierarchies between features, and lose some information on the precise position of a feature within the feature region, which is crucial for a malware file which has specific sections. In this paper, we draw on the idea of image classification in the field of computer vision and propose a novel malware detection method based on capsule network architecture with hyper-parameter optimized convolutional layers (MalCaps), which overcomes CNNs limitations by removing the need for a pooling layer and introduces capsule layers. Firstly, the malware is transformed into a grayscale image. Then, the dynamic routing-based capsule network is used to detect and classify the image. Without advanced feature extraction and with only a small number of labeled samples, the presented method is tested on an unbalanced Microsoft Malware Classification Challenge (MMCC) dataset and experimental results produce testing accuracy of 99.34%, improving on a number of traditional deep learning models posited in recent malware classification literature.

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Analyzing Malware From API Call Sequences Using Support Vector Machines

Al‐Haija

Krichen²

2023

Advances in Cybersecurity, Cybercrimes, and Smart Emerging Technologies

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An Efficient Malware Detection Technique using Complex Network-based Approach

Cited by 6 publications

References 8 publications

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

MalCaps: A Capsule Network Based Model for the Malware Classification

Analyzing Malware From API Call Sequences Using Support Vector Machines

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