Android gaming malware detection using system call analysis

Jaiswal, Mayank; Malik, Yasir; Jaafar, Fehmi

doi:10.1109/isdfs.2018.8355360

Cited by 16 publications

(1 citation statement)

References 5 publications

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“…In [31], it was deduced that Java, JNI, or native code execution initiated the potential source of unwanted malicious behavior in the malware applications by observing system calls of malicious Android apps. In [32], system calls of apps were analyzed to differentiate benign from malware ones. In [33], malicious apps were detected by Iqbal et al by analyzing several dynamic features such as memory consumption, CPU usage, and system call events.…”

Section: Dynamic Detectionmentioning

confidence: 99%

Blockchain Based Detection of Android Malware using Ranked Permissions

Gupta

Sethi

Chaudhary

et al. 2021

IJEAT

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Android mobile devices are a prime target for a huge number of cyber-criminals as they aim to create malware for disrupting and damaging the servers, clients, or networks. Android malware are in the form of malicious apps, that get downloaded on mobile devices via the Play Store or third-party app markets. Such malicious apps pose serious threats like system damage, information leakage, financial loss to user, etc. Thus, predicting which apps contain malicious behavior will help in preventing malware attacks on mobile devices. Identifying Android malware has become a major challenge because of the ever-increasing number of permissions that applications ask for, to enhance the experience of the users. And most of the times, permissions and other features defined in normal and malicious apps are generally the same. In this paper, we aim to detect Android malware using machine learning, deep learning, and natural language processing techniques. To delve into the problem, we use the Android manifest files which provide us with features like permissions which become the basis for detecting Android malware. We have used the concept of information value for ranking permissions. Further, we have proposed a consensus-based blockchain framework for making more concrete predictions as blockchain have high reliability and low cost. The experimental results demonstrate that the proposed model gives the detection accuracy of 95.44% with the Random Forest classifier. This accuracy is achieved with top 45 permissions ranked according to Information Value.

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Section: Dynamic Detectionmentioning

confidence: 99%