An Efficient Detection of Malware by Naive Bayes Classifier Using GPGPU

Sahay, Sanjay K.; Chaudhari, Mayank

doi:10.1007/978-981-13-6861-5_22

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…for malware detection by Sahay and Chaudhari (2019), among many other authors. NB requires short computational time for training compared with others and also, it is highly scalable, see Ashari et al (2013).…”

Section: Trainingmentioning

confidence: 96%

Protecting from Malware Obfuscation Attacks through Adversarial Risk Analysis

Redondo¹,

Insua²

2019

Preprint

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Malware constitutes a major global risk affecting millions of users each year. Standard algorithms in detection systems perform insufficiently when dealing with malware passed through obfuscation tools. We illustrate this studying in detail an open source metamorphic software, making use of a hybrid framework to obtain the relevant features from binaries. We then provide an improved alternative solution based on adversarial risk analysis which we illustrate describe with an example.

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Section: Trainingmentioning

confidence: 96%

Protecting from Malware Obfuscation Attacks through Adversarial Risk Analysis

Redondo¹,

Insua²

2019

Preprint

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“…Range of Detection Accuracy Rate K-Means [53], [54] 88% -> 90% NB [29], [38], [39], [40], [41], [54], [57], Pang, [58], [59], [60], [61], [74], [84], [91], [97], [101] 73.01% -98% SVM [36], [39], [40], [41], [45], [46], [47], [48], [51], [55], [61], [64], [65], [75], [78], [79], [80], [81], [82], [83], [84], [85], [86], [94], [95], [99], [100], [101] 64.7% -100% DT [29], [33], [34], [35]<...…”

Section: Related Workmentioning

confidence: 99%

“…, [56], [57], [60], [61], [64], [66], [67], [69], [70], [71], [76], [81], [83], [84], [85], [87], [88], [90], [94], [96], [100], [101], [102], [105], [109], [113], [119], [121], [125] Signaturebased false positive rate and raising the false negative rate overcomes the limitations of heuristic-based approaches.…”

Section: D: Specification-basedmentioning

confidence: 99%

Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

et al. 2023

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Malware has emerged as a cyber security threat that continuously changes to target computer systems, smart devices, and extensive networks with the development of information technologies. As a result, malware detection has always been a major worry and a difficult issue, owing to shortcomings in performance accuracy, analysis type, and malware detection approaches that fail to identify unexpected malware attacks. This paper seeks to conduct a thorough literature review and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen research works. The research investigates malware and machine learning in the context of cybersecurity, including malware detection taxonomy and machine learning algorithm classification into numerous categories. Furthermore, the taxonomy was used to evaluate the most recent machine learning algorithm and analysis. The paper also examines the obstacles and associated concerns encountered in malware detection and potential remedies. Finally, to address the related issues that would motivate researchers in their future work, an empirical study was utilized to assess the performance of several machine learning algorithms.INDEX TERMS Malware detection, machine learning algorithms, state-of-the-art.

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“…In this article, we consider as benchmark an NB classifier (Lewis, 1998). This classifier has been used for malware detection by Sahay and Chaudhari (2019), among many other authors. NB requires short computational time for training compared with others and also, it is highly scalable (see Ashari, Paryudi, & Tjoa, 2013).…”

Section: A Framework To Protect From Malware Using a Hybrid Approachmentioning

confidence: 99%