A detecting method for malicious mobile application based on incremental SVM

Li, Yong; Ma, Yuanyuan; Mu, Chunlai; Dai, Zaojian

doi:10.1109/compcomm.2017.8322742

Cited by 5 publications

(5 citation statements)

References 2 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: 85%

“…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%

“…Furthermore, the outdated dataset used in the experiment, which offers little or no utility as a benchmark for the performance of malware detection systems on a modern network [119],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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Section: ) Classification By Datasetmentioning

confidence: 85%

Section: D: Specification-basedmentioning

confidence: 99%

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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“…Ref. [181] adopts the incremental SVM algorithm to make full use of the prior information of historical samples. It avoids retraining all the data when new samples are added, thus improving detection efficiency.…”

Section: ) Machine Learning Models and Algorithms Used In Android Mamentioning

confidence: 99%

“…[3], [88], [100]- [103], [105], [107]- [109], [113]- [117], [119], [121], [136], [137], [139], [140], [143], [147], [151]- [153], [155], [157], [162], [166], [167], [169], [175]- [177], [179]- [181], [183], [187], [189], [190], [192], [193], [195], [202], [207], [212]- [216] K-Nearest Neighbors (KNN)…”

Section: ) Machine Learning Models and Algorithms Used In Android Mamentioning

confidence: 99%

A Review of Android Malware Detection Approaches Based on Machine Learning

et al. 2020

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Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. We believe our work complements the previous reviews by surveying a wider range of aspects of the topic. This paper presents a comprehensive survey of Android malware detection approaches based on machine learning. We briefly introduce some background on Android applications, including the Android system architecture, security mechanisms, and classification of Android malware. Then, taking machine learning as the focus, we analyze and summarize the research status from key perspectives such as sample acquisition, data preprocessing, feature selection, machine learning models, algorithms, and the evaluation of detection effectiveness. Finally, we assess the future prospects for research into Android malware detection based on machine learning. This review will help academics gain a full picture of Android malware detection based on machine learning. It could then serve as a basis for subsequent researchers to start new work and help to guide research in the field more generally.

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Malware Detection in Android Applications Using Integrated Static Features

Beegom¹,

Ashok²

2020

Communications in Computer and Information Science

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A detecting method for malicious mobile application based on incremental SVM

Cited by 5 publications

References 2 publications

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

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

A Review of Android Malware Detection Approaches Based on Machine Learning

Malware Detection in Android Applications Using Integrated Static Features

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