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LUT-Lock: A Novel LUT-based Logic Obfuscation for FPGA-Bitstream and ASIC-Hardware Protection
In this work, we propose LUT-Lock, a novel Look-Up-Table-based netlist obfuscation algorithm, for protecting the intellectual property that is mapped to an FPGA bitstream or an ASIC netlist. We, first, illustrate the effectiveness of several key features that make the LUT-based obfuscation more resilient against SAT attacks and then we embed the proposed key features into our proposed LUT-Lock algorithm. We illustrates that LUT-Lock maximizes the resiliency of the LUT-based obfuscation against SAT attacks by forcing a near exponential increase in the execution time of a SAT solver with respect to the number of obfuscated gates. Hence, by adopting LUT-Lock algorithm, SAT attack execution time could be made unreasonably long by increasing the number of utilized LUTs.
04/30/2018 ∙ by Hadi Mardani Kamali, et al. ∙
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Benchmarking the Capabilities and Limitations of SAT Solvers in Defeating Obfuscation Schemes
In this paper, we investigate the strength of six different SAT solvers in attacking various obfuscation schemes. Our investigation revealed that Glucose and Lingeling SAT solvers are generally suited for attacking small-to-midsize obfuscated circuits, while the MapleGlucose, if the system is not memory bound, is best suited for attacking mid-to-difficult obfuscation methods. Our experimental result indicates that when dealing with extremely large circuits and very difficult obfuscation problems, the SAT solver may be memory bound, and Lingeling, for having the most memory efficient implementation, is the best-suited solver for such problems. Additionally, our investigation revealed that SAT solver execution times may vary widely across different SAT solvers. Hence, when testing the hardness of an obfuscation method, although the increase in difficulty could be verified by one SAT solver, the pace of increase in difficulty is dependent on the choice of a SAT solver.
04/30/2018 ∙ by Shervin Roshanisefat, et al. ∙ 0
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Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning
Circuit obfuscation is a recently proposed defense mechanism to protect digital integrated circuits (ICs) from reverse engineering by using camouflaged gates i.e., logic gates whose functionality cannot be precisely determined by the attacker. There have been effective schemes such as satisfiability-checking (SAT)-based attacks that can potentially decrypt obfuscated circuits, called deobfuscation. Deobfuscation runtime could have a large span ranging from few milliseconds to thousands of years or more, depending on the number and layouts of the ICs and camouflaged gates. And hence accurately pre-estimating the deobfuscation runtime is highly crucial for the defenders to maximize it and optimize their defense. However, estimating the deobfuscation runtime is a challenging task due to 1) the complexity and heterogeneity of graph-structured circuit, 2) the unknown and sophisticated mechanisms of the attackers for deobfuscation. To address the above mentioned challenges, this work proposes the first machine-learning framework that predicts the deobfuscation runtime based on graph deep learning techniques. Specifically, we design a new model, ICNet with new input and convolution layers to characterize and extract graph frequencies from ICs, which are then integrated by heterogeneous deep fully-connected layers to obtain final output. ICNet is an end-to-end framework which can automatically extract the determinant features for deobfuscation runtime. Extensive experiments demonstrate its effectiveness and efficiency.
02/14/2019 ∙ by Zhiqian Chen, et al. ∙ 0
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Threats on Logic Locking: A Decade Later
To reduce the cost of ICs and to meet the market's demand, a considerable portion of manufacturing supply chain, including silicon fabrication, packaging and testing may be pushed offshore. Utilizing a global IC manufacturing supply chain, and inclusion of non-trusted parties in the supply chain has raised concerns over security and trust related challenges including those of overproduction, counterfeiting, IP piracy, and Hardware Trojans to name a few. To reduce the risk of IC manufacturing in an untrusted and globally distributed supply chain, the researchers have proposed various locking and obfuscation mechanisms for hiding the functionality of the ICs during the manufacturing, that requires the activation of the IP after fabrication using the key value(s) that is only known to the IP/IC owner. At the same time, many such proposed obfuscation and locking mechanisms are broken with attacks that exploit the inherent vulnerabilities in such solutions. The past decade of research in this area, has resulted in many such defense and attack solutions. In this paper, we review a decade of research on hardware obfuscation from an attacker perspective, elaborate on attack and defense lessons learned, and discuss future directions that could be exploited for building stronger defenses.
05/15/2019 ∙ by Kimia Zamiri Azar, et al. ∙ 0
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Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification
Advances in embedded systems have enabled integration of many lightweight sensory devices within our daily life. In particular, this trend has given rise to continuous expansion of wearable sensors in a broad range of applications from health and fitness monitoring to social networking and military surveillance. Wearables leverage machine learning techniques to profile behavioral routine of their end-users through activity recognition algorithms. Current research assumes that such machine learning algorithms are trained offline. In reality, however, wearables demand continuous reconfiguration of their computational algorithms due to their highly dynamic operation. Developing a personalized and adaptive machine learning model requires real-time reconfiguration of the model. Due to stringent computation and memory constraints of these embedded sensors, the training/re-training of the computational algorithms need to be memory- and computation-efficient. In this paper, we propose a framework, based on the notion of online learning, for real-time and on-device machine learning training. We propose to transform the activity recognition problem from a multi-class classification problem to a hierarchical model of binary decisions using cascading online binary classifiers. Our results, based on Pegasos online learning, demonstrate that the proposed approach achieves 97 intensities using a limited memory while power usages of the system is reduced by more than 40
07/07/2019 ∙ by Mahdi Pedram, et al. ∙ 0
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