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A Study on Techniques for Proactively Identifying Malicious URLs.

, , and . SYNASC, page 204-211. IEEE Computer Society, (2015)

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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)An Adversarial Machine Learning Approach to Evaluate the Robustness of a Security Solution., , and . SYNASC, page 148-154. IEEE, (2019)Optimizing Cleanset Growth by Using Multi-Class Neural Networks., , , and . SYNASC, page 425-429. IEEE, (2018)Combining Restricted Boltzmann Machine and One Side Perceptron for Malware Detection., and . ICCS, volume 8577 of Lecture Notes in Computer Science, page 93-103. Springer, (2014)Identifying DGA-Based Botnets Using Network Anomaly Detection., , and . SYNASC, page 292-299. IEEE, (2016)