A Cloud-Based Energy Efficient System for Enhancing the Detection and Prevention of Modern Malware

Mirza, Qublai Khan Ali; Mohi-Ud-Din, Ghulam; Awan, Irfan

doi:10.1109/aina.2016.133

Cited by 6 publications

(14 citation statements)

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“…It will not only enhance systems awareness about malicious and legitimate files, it will reduce the time and resources consumed while detecting the malware. This paper is an extension of an existing work published in [10], which mainly focused on two very important objectives; a) higher detection rate, b) low resource consumption. Higher detection rate [10] was achieved by combining the detection mechanism of ten antiviruses with a comprehensive open source static analysis tool, which gave a thorough output quite rapidly.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an extension of an existing work published in [10], which mainly focused on two very important objectives; a) higher detection rate, b) low resource consumption. Higher detection rate [10] was achieved by combining the detection mechanism of ten antiviruses with a comprehensive open source static analysis tool, which gave a thorough output quite rapidly. Resource consumption was reduced by setting up the entire detection engine on a cloud-based server.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2 presents the comparison of detection ratio between initial study [10] and major antiviruses. Although, the result does support the initial hypothesis but system does need a more resilient back end architecture supported and trained by some verified machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Understanding malware; their behavior, motives, and origins, is an infinite [10] paradigm and it is practically impossible to do that even by investing a massive amount of resources in the field. The recent news about an old attack [11] is a proof that the size of an organization is directly proportional to the level of destruction a cyberattack can cause.…”

Section: Introductionmentioning

confidence: 99%

“…The system developed and implemented in [10] was part of a pilot study, which had a primary objective of proving the hypothesis that a system can be developed that can consume very limited resources of the host machine to detect around 98% of the malicious files. Figure 2 presents the comparison of detection ratio between initial study [10] and major antiviruses.…”

Section: Introductionmentioning

confidence: 99%

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CloudIntell: An intelligent malware detection system

Mirza

Awan

Younas

2018

Future Generation Computer Systems

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Enterprises and individual users heavily rely on the abilities of antiviruses and other security mechanisms. However, the methodologies used by such software are not enough to detect and prevent most of the malicious activities and also consume a huge amount of resources of the host machine for their regular operations. In this paper, we propose a combination of machine learning techniques applied on a rich set of features extracted from a large dataset of benign and malicious files through a bespoke feature extraction tool. We extracted a rich set of features from each file and applied support vector machine, decision tree, and boosting on decision tree to get the highest possible detection rate. We also introduce a cloud-based scalable architecture hosted on Amazon web services to cater the needs of detection methodology. We tested our methodology against different scenarios and generated high achieving results with lowest energy consumption of the host machine.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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