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Classifying Malware Represented as Control Flow Graphs using Deep Graph Convolutional Neural Network.

, , and . DSN, page 52-63. IEEE, (2019)

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A Bayesian Cogntive Approach to Quantifying Software Exploitability Based on Reachability Testing., , , and . ISC, volume 9866 of Lecture Notes in Computer Science, page 343-365. Springer, (2016)Classifying Malware Represented as Control Flow Graphs using Deep Graph Convolutional Neural Network., , and . DSN, page 52-63. IEEE, (2019)Modeling Propagation Dynamics of Bluetooth Worms (Extended Version)., and . IEEE Trans. Mob. Comput., 8 (3): 353-368 (2009)Transductive malware label propagation: Find your lineage from your neighbors., and . INFOCOM, page 1411-1419. IEEE, (2014)Criticality analysis of Internet infrastructure., , , , and . Comput. Networks, 54 (7): 1169-1182 (2010)IoTInfer: Automated Blackbox Fuzz Testing of IoT Network Protocols Guided by Finite State Machine Inference., and . IEEE Internet Things J., 9 (22): 22737-22751 (2022)Improving Large-Scale Network Traffic Simulation with Multi-Resolution Models.. Dartmouth College, USA, (2005)Bluetooth worm propagation: mobility pattern matters!, , , , , and . AsiaCCS, page 32-44. ACM, (2007)SciBlock: A Blockchain-Based Tamper-Proof Non-Repudiable Storage for Scientific Workflow Provenance., , , , , , , , and . CIC, page 81-90. IEEE, (2019)CyberSim: Geographic, temporal, and organizational dynamics of malware propagation., , and . WSC, page 2876-2887. IEEE, (2010)