相关论文: Trojan Awakener: Detecting Dormant Malicious Hardw…

Commercial-off-the-shelf (COTS) components are often preferred over custom Integrated Circuits (ICs) to achieve reduced system development time and cost, easy adoption of new technologies, and replaceability. Unfortunately, the integration…

At S&P 2023, Puschner et al. made a valuable dataset for hardware Trojan detection research publicly available. It contains a complete set of Scanning Electron Microscope (SEM) images of four different digital Integrated Circuits (ICs)…

We present a novel methodology for neural network backdoor attacks. Unlike existing training-time attacks where the Trojaned network would respond to the Trojan trigger after training, our approach inserts a Trojan that will remain dormant…

In Natural Language Processing (NLP), intelligent neuron models can be susceptible to textual Trojan attacks. Such attacks occur when Trojan models behave normally for standard inputs but generate malicious output for inputs that contain a…

The growing complexity of global supply chains has made hardware Trojans a significant threat in sensor-based power electronics. Traditional Trojan designs depend on digital triggers or fixed threshold conditions that can be detected during…

Trojans are one of the most threatening network attacks currently. HTTP-based Trojan, in particular, accounts for a considerable proportion of them. Moreover, as the network environment becomes more complex, HTTP-based Trojan is more…

Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains, but their vulnerability to trojan or backdoor attacks poses significant security risks. This paper explores the challenges and insights gained from…

Deep learning architectures (DLA) have shown impressive performance in computer vision, natural language processing and so on. Many DLA make use of cloud computing to achieve classification due to the high computation and memory…

We introduce a new class of hardware trojans called interrupt-resilient trojans (IRTs). Our work is motivated by the observation that hardware trojan attacks on CPUs, even under favorable attack scenarios (e.g., an attacker with local…

Economic and operational advantages have led the supply chain of printed circuit boards (PCBs) to incorporate various untrusted entities. Any of the untrusted entities are capable of introducing malicious alterations to facilitate a…

Trojan attacks on deep neural networks are both dangerous and surreptitious. Over the past few years, Trojan attacks have advanced from using only a single input-agnostic trigger and targeting only one class to using multiple,…

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…

Fabrication-less design houses outsource their designs to 3rd party foundries to lower fabrication cost. However, this creates opportunities for a rogue in the foundry to introduce hardware Trojans, which stay inactive most of the time and…

As industry moves toward chiplet-based designs, the insertion of hardware Trojans poses a significant threat to the security of these systems. These systems rely heavily on cache coherence for coherent data communication, making coherence…

Outsourcing integrated circuit (IC) manufacturing to offshore foundries has grown exponentially in recent years. Given the critical role of ICs in the control and operation of vehicular systems and other modern engineering designs, such…

A trojan backdoor is a hidden pattern typically implanted in a deep neural network. It could be activated and thus forces that infected model behaving abnormally only when an input data sample with a particular trigger present is fed to…

Hardware trojan detection methods, based on machine learning (ML) techniques, mainly identify suspected circuits but lack the ability to explain how the decision was arrived at. An explainable methodology and architecture is introduced…

Despite their success and popularity, deep neural networks (DNNs) are vulnerable when facing backdoor attacks. This impedes their wider adoption, especially in mission critical applications. This paper tackles the problem of Trojan…

Safety-critical sensors in autonomous vehicles (AVs) form an essential part of the vehicle's trusted computing base (TCB), yet they are highly susceptible to attacks. Alarmingly, Tier 1 manufacturers have already exposed vulnerabilities to…

When the training data are maliciously tampered, the predictions of the acquired deep neural network (DNN) can be manipulated by an adversary known as the Trojan attack (or poisoning backdoor attack). The lack of robustness of DNNs against…