Detecting Network Intrusions With Machine Learning-Based Anomaly Detection Techniques

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Author: Clarence Chio

Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context.

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Clarence Chio is a software engineer at Shape Security and a community speaker at Intel. He has recently graduated with a B.S. and M.S. in Computer Science from Stanford University specializing in data mining and artificial intelligence. He is currently working on a product that protects its customers from malicious bot intrusion and on the system that tackles this problem from the angle of big data analysis.

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