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Deception-Enhanced Threat Sensing for Resilient Intrusion Detection

  • Ehab Al-Shaer
  • Jinpeng Wei
  • Kevin W. Hamlen
  • Cliff Wang
Chapter

Abstract

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.

The approach presented in this chapter examines how deceptive web service responses can be realized as software security patches that double as feature extraction engines for a network-level intrusion detection system. The resulting system coordinates multiple levels of the software stack to achieve fast, automatic, and accurate labeling of live web data streams, and thereby detects attacks with higher accuracy and adaptability than comparable non-deceptive defenses.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ehab Al-Shaer
    • 1
  • Jinpeng Wei
    • 2
  • Kevin W. Hamlen
    • 3
  • Cliff Wang
    • 4
  1. 1.Department of Software & Information SystemUniversity of North Carolina CharlotteCharlotteUSA
  2. 2.Department of Software and Information SystemUniversity of North CarolinaCharlotteUSA
  3. 3.Computer Science DepartmentUniversity of Texas at DallasRichardsonUSA
  4. 4.Computing and Information Science DivisionArmy Research OfficeDurhamUSA

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