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Multi-Domain Network Traffic Analysis using Machine Learning and Deep Learning Techniques.

, , , , , , , , and . IC3, page 305-312. ACM, (2022)

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NLP-Based Techniques for Cyber Threat Intelligence., , , , , , and . CoRR, (2023)Identification of malicious android app using manifest and opcode features., , and . J. Comput. Virol. Hacking Tech., 13 (2): 125-138 (2017)Linux Malware Detection Using eXtended-Symmetric Uncertainty., and . SPACE, volume 8804 of Lecture Notes in Computer Science, page 319-332. Springer, (2014)Assessing motion-coherency in video watermarking., , and . MM&Sec, page 114-119. ACM, (2006)X-ANOVA and X-Utest features for Android malware analysis., , and . ICACCI, page 1643-1649. IEEE, (2014)Heterogeneous feature space for Android malware detection., , and . IC3, page 383-388. IEEE Computer Society, (2015)Multi-Domain Network Traffic Analysis using Machine Learning and Deep Learning Techniques., , , , , , , , and . IC3, page 305-312. ACM, (2022)SeCTIS: A Framework to Secure CTI Sharing., , , , , , and . CoRR, (2024)Obfuscated Malware Detection in IoT Android Applications Using Markov Images and CNN., , , , , , , , and . IEEE Syst. J., 17 (2): 2756-2766 (June 2023)A machine learning based approach to detect malicious android apps using discriminant system calls., , and . Future Gener. Comput. Syst., (2019)