Enhancing standard web services with deceptive responses to cyberattacks can be a powerful and practical strategy for improved intrusion detection. Such deceptions are particularly helpful for addressing and overcoming barriers to effective machine learning-based intrusion detection encountered in many practical deployments. For example, they can provide a rich source of training data when training data is scarce, they avoid imposing a labeling burden on operators in the context of (semi-)supervised learning, they can be deployed post-decryption on encrypted data streams, and they learn concept differences between honeypot attacks and attacks against genuine assets
CITATION STYLE
Araujo, F., Ayoade, G., Hamlen, K. W., & Khan, L. (2019). Deception-Enhanced Threat Sensing for Resilient Intrusion Detection. In Autonomous Cyber Deception: Reasoning, Adaptive Planning, and Evaluation of HoneyThings (pp. 147–165). Springer International Publishing. https://doi.org/10.1007/978-3-030-02110-8_8
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