The globalization of the integrated circuit (IC) supply chain has moved most of the design, fabrication, and testing process from a single trusted entity to various untrusted third-party entities worldwide. The risk of using untrusted third-Party Intellectual Property (3PIP) is the possibility for adversaries to insert malicious modifications known as Hardware Trojans (HTs). These HTs can compromise the integrity, deteriorate the performance, deny the service, and alter the functionality of the design. While numerous HT detection methods have been proposed in the literature, the crucial task of HT localization is overlooked. Moreover, a few existing HT localization methods have several weaknesses: reliance on a golden reference, inability to generalize for all types of HT, lack of scalability, low localization resolution, and manual feature engineering/property definition. To overcome their shortcomings, we propose a novel, golden reference-free HT localization method at the pre-silicon stage by leveraging graph convolutional network (GCN). In this work, we convert the circuit design into its intrinsic data structure, graph, and extract the node attributes. Afterward, the graph convolution performs automatic feature extraction for nodes to classify the nodes as Trojan or benign. Our approach is automated and does not burden the designer with manual code review. It locates the Trojan signals with 99.6% accuracy, 93.1%$F1$-score, and a false-positive rate below 0.009%.

Design and fabrication outsourcing has made integrated circuits (IC) vulnerable to malicious modifications by third parties known as hardware Trojans (HT). Over the last decade, the use of side-channel measurements for detecting the malicious manipulation of the ICs has been extensively studied. However, the suggested approaches often suffer from three major limitations: 1) reliance on a trusted identical chip (i.e. golden chip), 2) untraceable footprints of subtle hardware Trojans which remain inactive during the testing phase, and 3) the need to identify the best discriminative features that can be used for separating side-channel signals coming from HT-free and HT-infected circuits. To overcome these shortcomings, we propose a novel neural network design (i.e. HTNet) and a feature extractor training methodology that can be used for HT detection in run time. We create a library of known hardware Trojans and collect electromagnetic and power side-channel signals for each case and train HTnet to learn the best discriminative features based on this library. Then, in the test time we fine tune HTnet to learn the behavior of the particular chip under test. We use HTnet followed by an anomaly detection mechanism in run-time to monitor the chip behavior and report malicious activities in the side-channel signals. We evaluate our methodology using TrustHub [15] benchmarks and show that HTnet can extract a robust set of features that can be used for HT-detection purpose.

In cyber-physical additive manufacturing systems, side-channel attacks have been used to reconstruct the G/M-code (which are instructions given to a manufacturing system) of 3D objects being produced. This method is effective for stealing intellectual property from an organization, through least expected means, during prototyping stage before the product goes through a large-scale fabrication and comes out in the market. However, an attacker can be far from being able to completely reconstruct the G/M-code due to lack of enough information leakage through the side-channels. In this paper, we propose a novel way to amplify the information leakage and thus boost the chances of recovery of G/M-code by surreptitiously altering the compiler. By using this compiler, an adversary may easily control various parameters to magnify the leakage of information from a 3D printer while still producing the desired object, thus remaining hidden from the authentic users. This type of attack may be implemented by strong attackers having access to the tool chain and seeking high level of stealth. We have implemented such a compiler and have demonstrated that it increases the success rate of recovering G/M-codes from the four side-channels (acoustic, power, vibration, and electromagnetic) by up to 39 percent compared to previously proposed attacks.

Since 2007, the use of side-channel measurements for detecting Hardware Trojan (HT) has been extensively studied. However, the majority of works either rely on a golden chip, or they rely on methods that are not robust against subtle acceptable changes that would occur over the life-cycle of an integrated circuit (IC). In this paper, we propose using a brain-inspired architecture called Hierarchical Temporal Memory (HTM) for HT detection. Similar to the human brain, our proposed solution is resilient againstnaturalchanges that might happen in the side-channel measurements while being able to accurately detect abnormal behavior of the chip when the HT gets triggered. We use a self-referencing method for HT detection, which eliminates the need for the golden chip. The effectiveness of our approach is evaluated using TrustHub benchmarks, which shows 92.20% detection accuracy on average.

Cyber-physical additive manufacturing systems consist of tight integration of cyber and physical domains. This union, however, induces new cross-domain vulnerabilities that pose unique security challenges. One of these challenges is preventing confidentiality breach, caused by physical-to-cyber domain attacks. In this form of attack, attackers utilize the side-channels (such as acoustics, power, electromagnetic emissions, and so on) in the physical-domain to estimate and steal cyber-domain data (such as G/M-codes). Since these emissions depend on the physical structure of the system, one way to minimize the information leakage is to modify the physical-domain. However, this process can be costly due to added hardware modification. Instead, we propose a novel methodology that allows the cyber-domain tools [such as computer aided-manufacturing (CAM)] to be aware of the existing information leakage. Then, we propose to change either machine process or product design parameters in the cyber-domain to minimize the information leakage. Our methodology aids the existing cyber-domain and physical-domain security solution by utilizing the cross-domain relationship. We have implemented our methodology in a fused-deposition modeling-based Cartesian additive manufacturing system. Our methodology achieves reduction of mutual information by 24.94% in acoustic side-channel, 32.91% in power side-channel, 32.29% in magnetic side-channel, and 55.65% in vibration side-channel. As a case study, to help understand the implication of mutual information drop, we have also presented the calculation of success rate and the reconstruction of the 3D object based on an attack model. For the given attack model, our leakage-aware CAM tool decreases the success rate of an attacker by 8.74% and obstructs the reconstruction of finer geometry details.

Cyber-physical additive manufacturing systems consists of tight integration of cyber and physical domains. This results in new cross-domain vulnerabilities that poses unique security challenges. One of the challenges is preventing confidentiality breach due to physical-to-cyber domain attacks, where attackers can analyze various analog emissions from the side-channels to steal the cyber-domain information. This information theft is based on the idea that an attacker can accurately estimate the relation between the analog emissions (acoustics, power, electromagnetic emissions, etc.,) and the cyber-domain data (such as G-code). To obstruct this estimation process, it is crucial to quantize the relation between the analog emissions and the cyber-data, and use it as a metric to generate computer aided manufacturing tools, such as slicing and tool-path generation algorithms, that are aware of these information leakage through the side-channels. In this paper, we present a novel methodology that uses mutual information as a metric to quantize the information leakage from the side-channels, and demonstrates how various design variables (such as object orientation, nozzle velocity, etc.,) can be used in an optimization algorithm to minimize the information leakage. Our methodology integrates this leakage aware algorithms to the state-of-the-art slicing and tool-path generation algorithms and achieves 24.76% average drop in the information leakage through acoustic side-channel. To the best of our knowledge, this is the first work that demonstrates the idea of generating information leakage aware computer aided manufacturing tools for protecting the confidentiality of the manufacturing system.

The next industrial revolution will incorporate various enabling technologies. These technologies will make the product lifecycle of the manufacturing system efficient, decentralized, and well-connected. However, these technologies have various security issues, and when integrated in the product lifecycle of manufacturing systems can pose various challenges for maintaining the security requirements such as confidentiality, integrity, and availability. In this paper, we will present the various trends and advances in the security of the product lifecycle of the manufacturing system while highlighting the roles played by the major enabling components of Industry 4.0.