Online detection and control of malware infected assets

Çam, Hasan

doi:10.1109/milcom.2017.8170869

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…"Droid-N for Android Malware Detection" with accuracy of 0.988895% ±0.007 and doesn't provide information extraction. In future a quantitative estimated focus alongside the information investigation of temporary interconnection, can cooperatively work in the recognition and control of malware polluted resources [50].…”

Section: Discussionmentioning

confidence: 99%

“…The limitations will be set apart as indicated by their lucidity and likewise gave information. Polymorphism 97.7 % 18 [46] Android Call Classification 96.18 % 17 [47] Botnet Traffic 97 % 16 [48] Prediction for Malware Class using Sigmoid Function 0.988895% ±0.007 16 [49] Clustering Approach 94.3% 12 [50] Malware Infection activities in networks Not Scalable 13 [51] Upcoming Trends in Security Standards using Machine Learning Not Scalable 18 [56] Hardware Malware Detectors 97.3% 14 [57] Hardware Malware Detectors 95.8% 15 [58] Android API 97.25% 18 [59] Android Applications 99.34% 18 [60] Android API 97.…”

Section: Quality Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Role of Logistic Regression in Malware Detection: A Systematic Literature Review

Farooq,

Akram,

Alvi

et al. 2022

VFAST trans. softw. eng.

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When brain, the first virus known introduced in computer systems, requirement of security was raised. Malware Detection turn out to be more vital when network is used for transferring Secret Information. Nowadays our central attributes i.e., Banking, Agriculture, Robotics, Virtual Social Life, Online Multiplayer Gaming, Private Conversations etc. is practicing internet and Malware will abolish everything if we discount it. Lots of new malwares are located by the passage of time, so we need a reliable, fast and trustworthy machine learning technique to handle them. Logistic Regression Classifier is useable for handling such a huge data, majorly counted in this paper. This is a complete SLR that delivers progressive approach in the field of malware detection. It legally reduces time and the cost of researchers. Limitations and future directions of machine learning classifiers to detect malwares are discussed in this paper.

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Section: Discussionmentioning

confidence: 99%