5 research outputs found

    An Evaluation Of N-gram System Call Sequence In Mobile Malware Detection

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    The rapid growth of Android-based mobile devices technology in recent years has increased the proliferation of mobile devices throughout the community at large. The ability of Android mobile devices has become similar to its desktop environment; users can do more than just a phone call and short text messaging. These days, Android mobile devices are used for various applications such as web browsing, ubiquitous services, social networking, MMS and many more. However, the rapid growth of Android mobile devices technology has also triggered the malware author to start exploiting the vulnerabilities of the devices. Based on this reason, this paper explores mobile malware detection through an n-gram system call sequence which uses a sequence of system call invoked by the mobile application as the feature in classifying a benign and malicious mobile application. Several n-gram values are evaluated with Linear-SVM classifier to determine the best n system call sequence that produces the highest detection accuracy and highest True Positive Rate (TPR) with low False Positive Rate (FPR)

    MiMaLo: advanced normalization method for mobile malware detection

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    A range of research procedures have been executed to overcome malware attacks. This research used a malware behavior observe approach on device calls on mobile devices operating gadget kernel. An application used to be mounted on mobile gadget to gather facts and processed them to get dataset. This research used data mining classification approach method and validates it using ten fold cross validation. MiMaLo is a method to normalize a dataset the usage of the min-max aggregate and logarithm function. The application of the MiMaLo method aims to increase the accuracy value. Derived from the experiments, the classifiers overall performance level used to be extensively increasing. The application of the MiMaLo method using the neural network algorithm produces an accuracy of 93.54% with AUC of 0.982

    App Store Analysis for Software Engineering

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    App Store Analysis concerns the mining of data from apps, made possible through app stores. This thesis extracts publicly available data from app stores, in order to detect and analyse relationships between technical attributes, such as software features, and non-technical attributes, such as rating and popularity information. The thesis identifies the App Sampling Problem, its effects and a methodology to ameliorate the problem. The App Sampling Problem is a fundamental sampling issue concerned with mining app stores, caused by the rather limited ‘most-popular-only’ ranked app discovery present in mobile app stores. This thesis provides novel techniques for the analysis of technical and non-technical data from app stores. Topic modelling is used as a feature extraction technique, which is shown to produce the same results as n-gram feature extraction, that also enables linking technical features from app descriptions with those in user reviews. Causal impact analysis is applied to app store performance data, leading to the identification of properties of statistically significant releases, and developer-controlled properties which could increase a release’s chance for causal significance. This thesis introduces the Causal Impact Release Analysis tool, CIRA, for performing causal impact analysis on app store data, which makes the aforementioned research possible; combined with the earlier feature extraction technique, this enables the identification of the claimed software features that may have led to significant positive and negative changes after a release
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