An Improved Permission Management Scheme of Android Application Based on Machine Learning

Niu, Shaozhang; Huang, Ruqiang; Chen, Wenbo; Yang, Xue

doi:10.1155/2018/2329891

Cited by 7 publications

(1 citation statement)

References 10 publications

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“…Due to the development of machine learning, it has been widely used in Android malware detection. However, it is difficult to improve the detection results by using a single base classifier, such as support vector machine (SVM) [34,35]; therefore, it has become a trend to use ensemble classifiers for detection. e way to combine classifiers that have independent decision-making ability with each other is called an ensemble classifier.…”

Section: Related Workmentioning

confidence: 99%

FB2Droid: A Novel Malware Family-Based Bagging Algorithm for Android Malware Detection

Shao

Xiong

Cai

2021

Security and Communication Networks

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As the number of Android malware applications continues to grow at a high rate, detecting malware to protect the system security and user privacy is becoming increasingly urgent. Each malware application belongs to a specific family, and there is a gap in the number of malware families. The accuracy of detection can be improved if malware family information is well utilized and certain strategies are adopted to balance the variability among samples. In addition, the performance of a base classifier is limited. If an ensemble classifier or an ensemble method can be adopted, the detection effect can be further improved. Therefore, this paper proposes a novel malware family-based bagging algorithm for Android malware detection, called FB2Droid, to perform malware detection. First, five features are extracted from the Android application package. Then, the relief feature selection algorithm is used for feature selection. Next, we designed two different sampling strategies based on different families of malware to alleviate the sample imbalance in the dataset. Combined with the two sampling strategies, the traditional bagging algorithm is improved to integrate the classifier. In the experiment, several classifiers were used to evaluate the proposed scheme. The experimental results show that the proposed sampling strategy and the improved bagging algorithm can effectively improve the detection accuracy of these classifiers.

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Section: Related Workmentioning

confidence: 99%

FB2Droid: A Novel Malware Family-Based Bagging Algorithm for Android Malware Detection

Shao

Xiong

Cai

2021

Security and Communication Networks

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PEDAM: Priority Execution Based Approach for Detecting Android Malware

Falana

Sodiya

Onashoga

et al. 2021

Lecture Notes in Networks and Systems

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Malware Detection: A Framework for Reverse Engineered Android Applications Through Machine Learning Algorithms

et al. 2022

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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.

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An Improved Permission Management Scheme of Android Application Based on Machine Learning

Cited by 7 publications

References 10 publications

FB2Droid: A Novel Malware Family-Based Bagging Algorithm for Android Malware Detection

FB2Droid: A Novel Malware Family-Based Bagging Algorithm for Android Malware Detection

PEDAM: Priority Execution Based Approach for Detecting Android Malware

Malware Detection: A Framework for Reverse Engineered Android Applications Through Machine Learning Algorithms

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