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A Framework for Detecting Hardware Trojans in RTL Using Artificial Immune Systems

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Behavioral Synthesis for Hardware Security

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Abstract

Security assurance in a computer system can be viewed as distinguishing between self and non-self; such is the view of Artificial Immune Systems (AIS), which are a class of Machine Learning (ML) algorithms, inspired by innate behavior of biological immune systems that have evolved over generations to accurately classify self-behavior from non-self-behavior to fight diseases. This chapter describes a technique leveraging AIS-based ML techniques and the associated software tool used to identify behavioral traits in high-level hardware descriptions for classifying unsafe or undesirable behaviors. Such behaviors include those caused by human error during development, or intentional, malicious circuit modifications, known as hardware Trojans, without the need for a golden reference model. Negative Selection and Clonal Selection Algorithms, which have historically been applied to malware detection on software binaries to detect potentially unsafe or malicious behavior, are applied to analyze hardware control and data-flow graphs (CDFGs) of Trojan-inserted benchmarks to train an AIS behavior model, against which novel hardware descriptions may be tested. This model efficiently detects specified (Trojan or Trojan-like) behavior with an accuracy of 86.3% and an average false negative rate of 12.6% for Negative Selection and 12.8% for Clonal Selection.***

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Authors and Affiliations

  1. University of South Florida, Tampa, FL, USA

    Farhath Zareen & Robert Karam

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  1. Farhath Zareen
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Correspondence to Farhath Zareen .

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  1. Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA

    Srinivas Katkoori

  2. Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, TX, USA

    Sheikh Ariful Islam

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Zareen, F., Karam, R. (2022). A Framework for Detecting Hardware Trojans in RTL Using Artificial Immune Systems. In: Katkoori, S., Islam, S.A. (eds) Behavioral Synthesis for Hardware Security. Springer, Cham. https://doi.org/10.1007/978-3-030-78841-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-78841-4_12

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