A SVM-Based Malware Detection Mechanism for Android Devices

Lu, Yung-Feng; Kuo, C.‐C. Jay; Chen, Hung-Yuan; Chen, Changwei; Chou, Shih‐Chien

doi:10.1109/icsse.2018.8520241

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…[56] UMUDGA 49 [59] Windows 10 ISO files 50 [63] Windows 7 Ultimate 32-bit directory 51 [94] Windows OS clean files 52 [117] Windows Portable Executables 53 [78] Windows Registry 54 [103] WireLurker 55 [83] Xiaomi App Store 56 [61], [62], [95] Android Malware Dataset (AMD) 57 [83], [105] AndroidManifest files 58 [81], [93] CLEAN…”

Section: Resultsmentioning

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: Resultsmentioning

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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“…Benchmark testing was based on the calculated overhead consumption of CPU, memory, I/O, 2D and 3D graphic video games, with total consumption of 0.9%, 9.4%, 4%, 0%, and 0%, respectively. Lu et al [122] collected 400 apps (200 normal apps and 200 malicious apps), extracted permission logs, which were Figure 8: Multi-level Anomaly Detector for Android Malware (MADAM) scheme [118] initially saved in a sequential manner in CSV format, then translated the file into (.scale) for later categorization of the features. The authors utilized signatures in Antivirus programs and applied an LibSVM [123] algorithm to classify the apps, and obtained 100%, 0%, 2%, 98%, and 99% for TP, FN, FP, TN, and recognition rate, respectively.…”

Section: Tip Calculatormentioning

confidence: 99%

Recent Advances in Android Mobile Malware Detection: A Systematic Literature Review

Alzubaidi¹

2021

IEEE Access

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In recent years, the global pervasiveness of smartphones has prompted the development of millions of free and commercially available applications. These applications allow users to perform various activities, such as communicating, gaming, and completing financial and educational tasks. These commonly used devices often store sensitive private information and, consequently, have been increasingly targeted by harmful malicious software. This paper focuses on the concepts and risks associated with malware, and reviews current approaches and mechanisms used to detect malware with respect to their methodology, associated datasets, and evaluation metrics.

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