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Improving Effectiveness of Intrusion Detection by Correlation Feature Selection.

, , and . ARES, page 17-24. IEEE Computer Society, (2010)

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Ink Markup Language, , , , , , , , , and . World Wide Web Consortium, Working Draft WD-InkML-20061023, (October 2006)Understanding Neuro-Fuzzy on a class of multinomial malware detection problems., , and . IJCNN, page 684-691. IEEE, (2016)Towards Improvement of Multinomial Classification Accuracy of Neuro-Fuzzy for Digital Forensics Applications., and . HIS, volume 420 of Advances in Intelligent Systems and Computing, page 199-210. Springer, (2015)Generic feature selection measure for botnet malware detection., , and . ISDA, page 711-717. IEEE, (2012)Identifying Anomalous HTTP Traffic with Association Rule Mining., , , and . ANTS, page 1-6. IEEE, (2019)POSTER: Towards Automating Detection of Anomalous HTTP Requests with Joint Probability Estimation of Characters., , and . AsiaCCS, page 889-891. ACM, (2020)Automated generation of fuzzy rules from large-scale network traffic analysis in digital forensics investigations., and . SoCPaR, page 31-36. IEEE, (2015)Analysis of Authentic Signatures and Forgeries.. IWCF, volume 5718 of Lecture Notes in Computer Science, page 150-164. Springer, (2009)The impact of preprocessing in natural language for open source intelligence and criminal investigation., and . IEEE BigData, page 4248-4254. IEEE, (2019)Cyber crime investigations in the era of big data., , and . IEEE BigData, page 3672-3676. IEEE Computer Society, (2017)