RNN-Based Classifier to Detect Stealthy Malware using Localized Features and Complex Symbolic Sequence

Shukla, Sanket; Kolhe, Gaurav; PD, Sai Manoj; Rafatirad, Setareh

doi:10.1109/icmla.2019.00076

Cited by 28 publications

(10 citation statements)

References 9 publications

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“…They have also used ML-models to classify applications and supported their claim. In [6,7] authors detect stealthy malwares by converting malware binaries into grayscale images and then extracting patterns by performing raster scanning. The grayscale images are further represented as sequence of patterns which are further used for sequence classification using RNN-LSTM's.…”

Section: Classification Resultsmentioning

confidence: 99%

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A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images

Dhavlle,

Shukla

2021

Preprint

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Our computer systems since decades have been threatened by various types of harware and software attacks of which Malwares have been one of them. These malwares have the ability to steal, destroy, contaminate, gain unintended access or even disrupt the entire system. There have been techniques to detect malwares by performing static and dynamic analysis of malware files, but, stealthy malware has circumvented the static analysis method and for dynamic analysis there have been previous works that propose different methods to detect malwares but, in this work we propose a novel technique to detect malwares. We use malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. We show that this technique is successful in differentiating classes of malware based on the features extracted.

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Section: Classification Resultsmentioning

confidence: 99%

“…The hardware security discipline in recent years experienced a plethora of threats like the Malware attacks [1,2,3,4,5,6,7], Side-Channel Attacks [8,9,10,11], Hardware Trojan attacks [12], reverse engineering threats [13,14,15] and so on. We focus on the malware detection technique here along with some state-of-the-art works.…”

Section: Introductionmentioning

confidence: 99%

A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images

Dhavlle,

Shukla

2021

Preprint

Self Cite

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“…Shukla et al [52] designed a malware detection model based on recurrent neural networks, using grey-scale images and hardware-based performance counters to extract feature, which improved the average accurate detection rate and precision by 11% compared to CNN-based sequence classification and Hidden Markov Model-based methods. The accuracy was as high as 94%.…”

Section: B a Static Malware Detection Solution Based On Deep Learning...mentioning

confidence: 99%

A Malware Detection Approach Using Autoencoder in Deep Learning

et al. 2022

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Today, in the field of malware detection, the expanding limitations of traditional detection methods and the increasing accuracy of detection methods designed on the basis of artificial intelligence algorithms are driving research findings in this area in favour of the latter. Therefore, we propose a novel malware detection model in this paper. This model combines a grey-scale image representation of malware with an autoencoder network in a deep learning model, analyses the feasibility of the grey-scale image approach of malware based on the reconstruction error of the autoencoder, and uses the dimensionality reduction features of the autoencoder to achieve the classification of malware from benign software. The proposed detection model achieved an accuracy of 96% and a stable F-score of about 96% by using the Android-side dataset we collected, which outperformed some traditional machine learning detection algorithms.

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“…The hardware security domain in recent years has experienced a plethora of threats Side-Channel Attacks [1,2], Malware attacks [3][4][5][6][7][8][9], Hardware Trojan attacks [10], reverse engineering threats [11][12][13] and so on. Among multiple threats, the side-channel attacks (SCAs) is one of the pivotal threats due to it's capability to exploit the design despite being introduced in the market post-validation.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Learning Inspired Emerging Side-Channel Attacks and Defenses

Dhavlle¹

2021

Preprint

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Evolving attacks on the vulnerabilities of the computing systems demand novel defense strategies to keep pace with newer attacks. This report discusses previous works on side-channel attacks (SCAs) and defenses for cache-targeted and physical proximity attacks. We then discuss the proposed Entropy-Shield as a defense against timing SCAs, and explain how we can extend the same to hardware-based implementations of crypto applications as "Entropy-Shield for FPGA". We then discuss why we want to build newer attacks with the hope of coming up with better defense strategies.

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RNN-Based Classifier to Detect Stealthy Malware using Localized Features and Complex Symbolic Sequence

Cited by 28 publications

References 9 publications

A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images

A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images

A Malware Detection Approach Using Autoencoder in Deep Learning

Adversarial Learning Inspired Emerging Side-Channel Attacks and Defenses

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