Malware Detection Based on Feature Library and Machine Learning

Hu, Dongfang; Xu, Bin; Wang, Jun; Han, Linfeng; Liu, Jiayi

doi:10.1109/auteee50969.2020.9315607

Cited by 2 publications

(5 citation statements)

References 22 publications

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“…Also, ContagioDump, VirusTotal, and VirusShare were also employed for malware samples. However, in this SLR study, VirusShare [68], [63], [76], [86], [97], [102], [107], [117], [120], [58], [116] is found as the most popular dataset used in their experiments, followed by DREBIN, [67], [72], [80], [87], [88], [108], [109], [62], [64] Malware Genome Project, [114], [115], [64], [74], [112], [118], Google Play Store, [64], [74], [85], [115], [114] and many more type of datasets as shown in TABLE 14 in Appendix A.…”

Section: ) Classification By Datasetmentioning

confidence: 95%

“…Meta-Heuristic [30] 81.23% -99.91% NF [87], [92], [93] 69.44% -91% Bayesian [32], [49], [66], [67], [88] 80% -> 97% Gaussian [32], [36], [52], [70], [94] 80% -> 91.1% KNN [28], [29], [37], [48], [51], [55], [60], [69], [71], [72], [73], [74], [85], [96], [99], [100] 80.50% -99.2% N-grams [30], [31], [42], [43], [44], [56], [62], [63], [76], [77], [98] 81.23% -100% Meanwhile, each algorithm's average detection accuracy rate has been obtained, and SVM continues to perform well, with a 90.55% accuracy rate. N-grams have the greatest average detection accuracy rate of 97.80%, followed by KNN 92.72%, DT 92.23%, K-Means 89%, Bayesian 89.08%, Gaussian 87.42%, NB 86.45%, NF 83.48%, and Meta-Heuristic with 81.23%.…”

Section: Related Workmentioning

confidence: 99%

“…This method is essentially a manual comparison of some systems' typical actions. Lowering the , [55], [58], [59], [62], [63], [65], [68], [72], [73], [74], [75], [78], [79], [80], [82], [86], [91], [92], [93], [95], [97], [103], [104], [106], [107], [108], [110], [111], [112], [114], [115], [116], [117], [118], [120], [122], [123], [126] Behaviorbased…”

Section: D: Specification-basedmentioning

confidence: 99%

“…Dataset issues were found as the most common research gap in the selected studies. It is not only because the size of the dataset is small [59], [63], [72]; the lack of a standard dataset benchmark [58] also contributed to this issue. Besides, various types of datasets were used in the experiment leading to poor performance [61], [69].…”

Section: A Dataset Usedmentioning

confidence: 99%

“…Furthermore, the outdated dataset used in the experiment, which offers little or no utility as a benchmark for the performance of malware detection systems on a modern network [119], also contributed to this issue. [53], [58], [59], [65], [68], [78], [79], [82], [86], [91], [95], [97], [103], [104], [106], [108], [111], [112], [115], [118], [120], [122], [123] Dynamic 2 6 5 9 2 2 1 1 2 0 2 [55], [62], [63], [72], [74], [75], [92], [93], [107], [110], [113], [114], [116], [117], [126] Hybrid 2 4 3 4 1 0 0 3 1 1 3 [54], [56], [57], [60], [61], [64], [66], [67], [69], [70]...…”

Section: A Dataset Usedmentioning

confidence: 99%

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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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