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Detecting Malicious URLs: A Semi-Supervised Machine Learning System Approach.

, , , and . SYNASC, page 233-239. IEEE, (2016)

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Short- versus long-term performance of detection models for obfuscated MSOffice-embedded malware., , , and . Int. J. Inf. Sec., 23 (1): 271-297 (February 2024)Improving detection of malicious samples by using state-of-the-art adversarial machine learning algorithms., , and . SIN, page 1-8. IEEE, (2022)A Practical Approach on Cleaning-Up Large Data Sets., , and . SYNASC, page 280-284. IEEE Computer Society, (2014)Evolution of macro VBA obfuscation techniques., , , and . SIN, page 1-8. IEEE, (2022)Real-Time Deep Learning-Based Malware Detection Using Static and Dynamic Features., , and . ICAART (3), page 226-234. SCITEPRESS, (2024)An Automatic Updating Perceptron-Based System for Malware Detection., , and . SYNASC, page 303-307. IEEE Computer Society, (2013)Increasing Protection Against Internet Attacks through Contextual Feature Pairing., , and . SYNASC, page 430-434. IEEE, (2018)GPU memory leveraged for accelerated training using Tensorflow., , and . ICCP, page 347-353. IEEE, (2017)Identifying DGA-Based Botnets Using Network Anomaly Detection., , and . SYNASC, page 292-299. IEEE, (2016)Adjusting SVMs for Large Data Sets using Balanced Decision Trees., , and . SYNASC, page 223-229. IEEE, (2018)