Computer Information and Graphics Technology Works

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Browsing Computer Information and Graphics Technology Works by Author "Al Hasan, Mohammad"

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    ACTS: Extracting Android App Topological Signature through Graphlet Sampling
    (IEEE, 2016-10) Peng, Wei; Gao, Tianchong; Sisodia, Devkishen; Saha, Tanay Kumar; Li, Feng; Al Hasan, Mohammad; Computer Information and Graphics Technology, School of Engineering and Technology
    Android systems are widely used in mobile & wireless distributed systems. In the near future, Android is believed to dominate the mobile distributed environment. However, with the popularity of Android-based smartphones/tablets comes the rampancy of Android-based malware. In this paper, we propose a novel topological signature of Android apps based on the function call graphs (FCGs) extracted from their Android App Packages (APKs). Specifically, by leveraging recent advances in graphlet sampling, the proposed method fully captures the invocator-invocatee relationship at local neighborhoods in an FCG without exponentially inflating the state space. Using real benign app and malware samples, we demonstrate that our method, ACTS (App topologiCal signature through graphleT Sampling), can detect malware and identify malware families robustly and efficiently. More importantly, we demonstrate that, without augmenting the FCG with any semantic features such as bytecode-based vertex typing, local topological information captured by ACTS alone can achieve a high malware detection accuracy. Since ACTS only uses structural features, which are orthogonal to semantic features, it is expected that combining them would give a greater improvement in malware detection accuracy than combining non-orthogonal semantic features.
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    Android Malware Detection via Graphlet Sampling
    (IEEE, 2018-11) Gao, Tianchong; Peng, Wei; Sisodia, Devkishen; Saha, Tanay Kumar; Li, Feng; Al Hasan, Mohammad; Computer Information and Graphics Technology, School of Engineering and Technology
    Android systems are widely used in mobile & wireless distributed systems. In the near future, Android is believed to dominate the mobile distributed environment. However, with the popularity of Android-based smartphones/tablets comes the rampancy of Android-based malware. In this paper, we propose a novel topological signature of Android apps based on the function call graphs (FCGs) extracted from their Android App PacKages (APKs). Specifically, by leveraging recent advances on graphlet mining, the proposed method fully captures the invocator-invocatee relationship at local neighborhoods in an FCG without exponentially inflating the state space. Using real benign app and malware samples, we demonstrate that our method, ACTS (App topologiCal signature through graphleT Sampling), can detect malware and identify malware families robustly and efficiently. More importantly, we demonstrate that, without augmenting the FCG with any semantic features such as bytecode-based vertex typing, local topological information captured by ACTS alone can achieve a high malware detection accuracy. Since ACTS only uses structural features, which are orthogonal to semantic features, it is expected that combining them would give a greater improvement in malware detection accuracy than combining non-orthogonal semantic features.
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    Representing Graphs as Bag of Vertices and Partitions for Graph Classification
    (Springer, 2018-06-01) Bhuiyan, Mansurul; Al Hasan, Mohammad; Department of Computer and Information sciences, School of Science
    Graph classification is a difficult task because finding a good feature representation for graphs is challenging. Existing methods use topological metrics or local subgraphs as features, but the time complexity for finding discriminatory subgraphs or computing some of the crucial topological metrics (such as diameter and shortest path) is high, so existing methods do not scale well when the graphs to be classified are large. Another issue of graph classification is that the number of distinct graphs for each class that are available for training a classification model is generally limited. Such scarcity of graph data resources yields models that have much fewer instances than the model parameters, which leads to poor classification performance. In this work, we propose a novel approach for solving graph classification by using two alternative graph representations: the bag of vertices and the bag of partitions. For the first representation, we use representation learning-based node features and for the second, we use traditional metric-based features. Our experiments with 43 real-life graphs from seven different domains show that the bag representation of a graph improves the performance of graph classification significantly. We have shown 4–75% improvement on the vertex-based and 4–36% improvement on partition-based approach over the existing best methods. Besides, our vertex and partition multi-instance methods are on average 75 and 11 times faster in feature construction time than the current best, respectively.