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Malware detection : a framework for reverse engineered android applications through machine learning algorithms
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Urooj, Beenish, Shah, Munam Ali, Maple, Carsten, Abbasi, Muhammad Kamran and Riasat, Sidra (2022) Malware detection : a framework for reverse engineered android applications through machine learning algorithms. IEEE Access, 10 . 89031 -89050. doi:10.1109/ACCESS.2022.3149053 ISSN 2169-3536.
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WRAP-malware-detection-framework-reverse-engineered-android-applications-through-machine-learning-algorithms-Maple-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (2144Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/ACCESS.2022.3149053
Abstract
Today, Android is one of the most used operating systems in smartphone technology. This is the main reason, Android has become the favorite target for hackers and attackers. Malicious codes are being embedded in Android applications in such a sophisticated manner that detecting and identifying an application as a malware has become the toughest job for security providers. In terms of ingenuity and cognition, Android malware has progressed to the point where they’re more impervious to conventional detection techniques. Approaches based on machine learning have emerged as a much more effective way to tackle the intricacy and originality of developing Android threats. They function by first identifying current patterns of malware activity and then using this information to distinguish between identified threats and unidentified threats with unknown behavior. This research paper uses Reverse Engineered Android applications’ features and Machine Learning algorithms to find vulnerabilities present in Smartphone applications. Our contribution is twofold. Firstly, we propose a model that incorporates more innovative static feature sets with the largest current datasets of malware samples than conventional methods. Secondly, we have used ensemble learning with machine learning algorithms such as AdaBoost, SVM, etc. to improve our model’s performance. Our experimental results and findings exhibit 96.24% accuracy to detect extracted malware from Android applications, with a 0.3 False Positive Rate (FPR). The proposed model incorporates ignored detrimental features such as permissions, intents, API calls, and so on, trained by feeding a solitary arbitrary feature, extracted by reverse engineering as an input to the machine.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||
Library of Congress Subject Headings (LCSH): | Androids , Application software , Malware (Computer software) , Computer viruses -- Mathematical models, Computer networks -- Security measures, Reverse engineering , Machine learning | ||||
Journal or Publication Title: | IEEE Access | ||||
Publisher: | IEEE | ||||
ISSN: | 2169-3536 | ||||
Official Date: | 4 February 2022 | ||||
Dates: |
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Volume: | 10 | ||||
Page Range: | 89031 -89050 | ||||
DOI: | 10.1109/ACCESS.2022.3149053 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 9 March 2022 | ||||
Date of first compliant Open Access: | 10 March 2022 |
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