droidmat android malware detection through manifest and api calls tracing pdf

Droidmat android malware detection through manifest and api calls tracing pdf

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DroidMat: Android Malware Detection through Manifest and API Calls Tracing

Runtime Detection Framework for Android Malware

Comprehensive Behavior Profiling for Proactive Android Malware Detection

DroidMat: Android Malware Detection through Manifest and API Calls Tracing

DroidMat: Android Malware Detection through Manifest and API Calls Tracing

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Recently, the threat of Android malware is spreading rapidly, especially those repackaged Android malware. In order to recognize different intentions of Android malware, different kinds of clustering algorithms can be applied to enhance the malware modeling capability. Besides, we leverage the proposed… Expand Abstract. View on IEEE. Save to Library.

Create Alert. Launch Research Feed. Share This Paper. Background Citations. Methods Citations. Results Citations. Figures, Tables, and Topics from this paper. Figures and Tables. Citation Type. Has PDF. Publication Type. More Filters. View 1 excerpt. Research Feed. View 1 excerpt, cites methods. View 2 excerpts, cites methods and background.

View 3 excerpts, cites methods and background. View 3 excerpts, cites methods. Highly Influenced. View 4 excerpts, cites background. Detecting Android malware using sequences of system calls.

View 1 excerpt, cites background. Investigating Android permissions and intents for malware detection. DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model.

View 2 excerpts, cites background. Dissecting Android Malware: Characterization and Evolution. Detecting Symbian OS malware through static function call analysis. View 1 excerpt, references background. Behavioral detection of malware on mobile handsets.

Detecting energy-greedy anomalies and mobile malware variants. View 1 excerpt, references methods. Android Market Analysis with Activation Patterns. Analyzing inter-application communication in Android. Related Papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy , Terms of Service , and Dataset License.

Runtime Detection Framework for Android Malware

We present a new method of screening for malicious Android applications that uses two types of information about the application: the permissions that the application requests in its installation manifest and a metric called percentage of valid call sites PVCS. PVCS measures the riskiness of the application based on a data flow graph. The information is used with machine learning algorithms to classify previously unseen applications as malicious or benign with a high degree of accuracy. Our classifier outperforms the previous state of the art by a significant margin, with particularly low false positive rates. Furthermore, the classifier evaluation is performed on malware families that were not used in the training phase, simulating the accuracy of the classifier on malware yet to be developed. Unable to display preview.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Recently, the threat of Android malware is spreading rapidly, especially those repackaged Android malware. In order to recognize different intentions of Android malware, different kinds of clustering algorithms can be applied to enhance the malware modeling capability. Besides, we leverage the proposed… Expand Abstract.

With an ever-increasing and ever more aggressive proliferation of malware, its detection is of utmost importance. However, due to the fact that IoT devices are resource-constrained, it is difficult to provide effective solutions. The main goal of this paper is the development of lightweight techniques for dynamic malware detection. For this purpose, we identify an optimized set of features to be monitored at runtime on mobile devices as well as detection algorithms that are suitable for battery-operated environments. We propose to use a minimal set of most indicative memory and CPU features reflecting malicious behavior.

Comprehensive Behavior Profiling for Proactive Android Malware Detection

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Although understanding Android malware using dynamic analysis can provide a comprehensive view, it is still subjected to high cost in environment deployment and manual efforts in investigation.

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As the number of Android malware has been increased rapidly over the years, various malware detection methods have been proposed so far. Existing methods can be classified into two categories: static analysis-based methods and dynamic analysis-based methods. Both approaches have some limitations: static analysis-based methods are relatively easy to be avoided through transformation techniques such as junk instruction insertions, code reordering, and so on.

Comprehensive Behavior Profiling for Proactive Android Malware Detection

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DroidMat: Android Malware Detection through Manifest and API Calls Tracing

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Mathematical Problems in Engineering

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5 comments

  • Anja Z. 20.05.2021 at 06:32

    An explosive spread of Android malware causes a serious concern for Android application security.

    Reply
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  • Arlet E. 25.05.2021 at 11:34

    There are thousands of malicious applications that invade Google Play Store every day and seem to be legal applications.

    Reply
  • Charlotte B. 26.05.2021 at 14:51

    The mechanism considers the static information including permissions, deployment of components, Intent messages passing and API calls for characterizing the.

    Reply

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