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Classification and clustering are two important classes of machine learning techniques that have been widely used for cyber defense purposes. However, these mechanisms can be defeated by intelligent evasion attacks, such as Adversarial Machine Learning. Currently, there are no effective countermeasures against these sophisticated attacks. The objective of the project is to investigate effective countermeasures to make classification and clustering mechanisms robust against intelligent evasion attacks. The scientific contributions of the project include advancing our understanding of the feasibility and impact of evasion attacks, and the design of machine learning algorithms that are robust against such attacks. Since machine learning techniques are widely employed in many other areas such as real-world fraud and crime detection, those areas would benefit from this project too. The project will involve PhD students who will directly contribute to the next-generation workforce and will address diversity by involving female students and students from underrepresented groups.
The project plans to achieve its goal by investigating a more powerful class of attacks, called gray-box attacks, than the traditional black-box attacks investigated in the literature. In the gray-box attack model, the attacker can perform all the activities that a defender would normally perform. The project will build a theoretical model and framework for characterizing the vulnerability and resilience of classification and clustering mechanisms with respect to intelligent evasion attacks under the gray-box model, enhance classification and clustering mechanisms to withstand intelligent evasion attacks with quantifiable resilience gains.
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SaTC: CORE: Small: Collaborative: A Framework for Enhancing the Resilience of Cyber Attack Classification and Clustering Mechanisms