A survey on security analysis of machine learning-oriented hardware and software intellectual property

Intellectual Property (IP) includes ideas, innovations, methodologies, works of authorship (viz., literary and artistic works), emblems, brands, images, etc. This property is intangible since it is pertinent to the human intellect. Therefore, IP entities are indisputably vulnerable to infringements and modifications without the owner’s consent. IP protection regulations have been deployed and are still in practice, including patents, copyrights, contracts, trademarks, trade secrets, etc., to address these challenges. Unfortunately, these protections are insufficient to keep IP entities from being changed or stolen without permission. As for this, some IPs require hardware IP protection mechanisms, and others require software IP protection techniques. To secure these IPs, researchers have explored the domain of Intellectual Property Protection (IPP) using different approaches. In this paper, we discuss the existing IP rights and concurrent breakthroughs in the field of IPP research; provide discussions on hardware IP and software IP attacks and defense techniques; summarize different applications of IP protection; and lastly, identify the challenges and future research prospects in hardware and software IP security

An End-to-End Bitstream Tamper Attack Against Flip-Chip FPGAs

FPGA bitstream encryption and authentication can be defeated by various techniques and it is critical to understand how these vulnerabilities enable extraction and tampering of commercial FPGA bitstreams. We exploit the physical vulnerability of bitstream encryption keys to readout using failure analysis equipment and conduct an end-to-end bitstream tamper attack. Our work underscores the feasibility of supply chain bitstream tampering and the necessity of guarding against such attacks in critical systems

Quantifiable Assurance: From IPs to Platforms

Hardware vulnerabilities are generally considered more difficult to fix than software ones because they are persistent after fabrication. Thus, it is crucial to assess the security and fix the vulnerabilities at earlier design phases, such as Register Transfer Level (RTL) and gate level. The focus of the existing security assessment techniques is mainly twofold. First, they check the security of Intellectual Property (IP) blocks separately. Second, they aim to assess the security against individual threats considering the threats are orthogonal. We argue that IP-level security assessment is not sufficient. Eventually, the IPs are placed in a platform, such as a system-on-chip (SoC), where each IP is surrounded by other IPs connected through glue logic and shared/private buses. Hence, we must develop a methodology to assess the platform-level security by considering both the IP-level security and the impact of the additional parameters introduced during platform integration. Another important factor to consider is that the threats are not always orthogonal. Improving security against one threat may affect the security against other threats. Hence, to build a secure platform, we must first answer the following questions: What additional parameters are introduced during the platform integration? How do we define and characterize the impact of these parameters on security? How do the mitigation techniques of one threat impact others? This paper aims to answer these important questions and proposes techniques for quantifiable assurance by quantitatively estimating and measuring the security of a platform at the pre-silicon stages. We also touch upon the term security optimization and present the challenges for future research directions

Circuit-Variant Moving Target Defense for Side-Channel Attacks on Reconfigurable Hardware

With the emergence of side-channel analysis (SCA) attacks, bits of a secret key may be derived by correlating key values with physical properties of cryptographic process execution. Power and Electromagnetic (EM) analysis attacks are based on the principle that current flow within a cryptographic device is key-dependent and therefore, the resulting power consumption and EM emanations during encryption and/or decryption can be correlated to secret key values. These side-channel attacks require several measurements of the target process in order to amplify the signal of interest, filter out noise, and derive the secret key through statistical analysis methods. Differential power and EM analysis attacks rely on correlating actual side-channel measurements to hypothetical models. This research proposes increasing resistance to differential power and EM analysis attacks through structural and spatial randomization of an implementation. By introducing randomly located circuit variants of encryption components, the proposed moving target defense aims to disrupt side-channel collection and correlation needed to successfully implement an attac