Detection of repackaged Android applications based on Apps Permissions

Nisha, O. S. Jannath; Bhanu, S. Mary Saira

doi:10.1109/rait.2018.8388984

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…However, it is justified that the intents are not an ultimate solution and should be used in conjunction with other characteristics. Similarly, Nisha et al [2] proposed the detection of repackaged Android malware using mutual information and chi-square techniques for the selection of features. Random forest classifier was able to achieve the highest accuracy of 91.76% among employed classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…After identifying the list of permissions, this section aims to determine the most significant permissions that can be used for distinguishing Apps from malicious and benign ones. To select the significant key permissions for malware detection, we have explored and evaluated Google's dangerous permission list [2] and the Zhu et al [9] permission set as shown in Table 2. From Table 2, it is observed that Zhu et al [9] overlapped the nine Google permission features for its evaluation while employing an additional feature named permission rate.…”

Section: Filtering Finalizing and Extracting The Core Featuresmentioning

confidence: 99%

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Permissions-Based Detection of Android Malware Using Machine Learning

et al. 2022

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Malware applications (Apps) targeting mobile devices are widespread, and compromise the sensitive and private information stored on the devices. This is due to the asymmetry between informative permissions and irrelevant and redundant permissions for benign Apps. It also depends on the characteristics of the Android platform, such as adopting an open-source policy, supporting unofficial App stores, and the great tolerance for App verification; therefore the Android platform is destined to face such malicious intrusions. In this paper, we propose a permissions-based malware detection system (PerDRaML) that determines the App’s maliciousness based on the usage of suspicious permissions. The system uses a multi-level based methodology; we first extract and identify the significant features such as permissions, smali sizes, and permission rates from a manually collected dataset of 10,000 applications. Further, we employ various machine learning models to categorize the Apps into their malicious or benign categories. Through extensive experimentations, the proposed method successfully identifies the 5× most significant features to predict malicious Apps. The proposed method outperformed the existing techniques by achieving high accuracies of malware detection i.e., 89.7% with Support Vector Machine, 89.96% with Random Forest, 86.25% with Rotation Forest, and 89.52% with Naïve Bayes models. Moreover, the proposed method optimized up to ~77% of the feature set as compared to the recent approaches, while improving the evaluation metrics such as precision, sensitivity, accuracy, and F-measure. The experimental results show that the proposed system provides a high level of symmetry between irrelevant permissions and malware Apps. Further, the proposed system is promising and may provide a low-cost alternative for Android malware detection for malicious or repackaged Apps.

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Section: Literature Reviewmentioning

confidence: 99%