相关论文: An AI-Enabled Side Channel Power Analysis Based Ha…

The success of deep neural networks (DNNs) in real-world applications has benefited from abundant pre-trained models. However, the backdoored pre-trained models can pose a significant trojan threat to the deployment of downstream DNNs.…

Trojan attacks are sophisticated training-time attacks on neural networks that embed backdoor triggers which force the network to produce a specific output on any input which includes the trigger. With the increasing relevance of deep…

Security vulnerability analysis of Integrated Circuits using conventional design-time validation and verification techniques (like simulations, emulations, etc.) is generally a computationally intensive task and incomplete by nature,…

Physical side channels emerge from the relation between internal computation or data with observable physical parameters of a chip. Previous works mostly focus on properties related to current consumption such as power consumption. The…

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…

Due to the globalization of semiconductor manufacturing and test processes, the system-on-a-chip (SoC) designers no longer design the complete SoC and manufacture chips on their own. This outsourcing of the design and manufacturing of…

Deep neural networks are known to have security issues. One particular threat is the Trojan attack. It occurs when the attackers stealthily manipulate the model's behavior through Trojaned training samples, which can later be exploited.…

While neural networks demonstrate stronger capabilities in pattern recognition nowadays, they are also becoming larger and deeper. As a result, the effort needed to train a network also increases dramatically. In many cases, it is more…

Digital Manufacturing (DM) refers to the on-going adoption of smarter, more agile manufacturing processes and cyber-physical systems. This includes modern techniques and technologies such as Additive Manufacturing (AM)/3D printing, as well…

Cyberattacks can cause a severe impact on power systems unless detected early. However, accurate and timely detection in critical infrastructure systems presents challenges, e.g., due to zero-day vulnerability exploitations and the…

To meet the ever-growing need for performance in silicon devices, SoC providers have been increasingly relying on software-hardware cooperation. By controlling hardware resources such as power or clock management from the software,…

Neural networks are increasingly used for intrusion detection on industrial control systems (ICS). With neural networks being vulnerable to adversarial examples, attackers who wish to cause damage to an ICS can attempt to hide their attacks…

Backdoor (trojan) attacks embed hidden, controllable behaviors into machine-learning models so that models behave normally on benign inputs but produce attacker-chosen outputs when a trigger is present. This survey reviews the rapidly…

Backdoor attacks have been shown to be a serious threat against deep learning systems such as biometric authentication and autonomous driving. An effective backdoor attack could enforce the model misbehave under certain predefined…

Offshoring the proprietary Intellectual property (IP) has recently increased the threat of malicious logic insertion in the form of Hardware Trojan (HT). A potential and stealthy HT is triggered with nets that switch rarely during regular…

Deep neural networks are being widely deployed for many critical tasks due to their high classification accuracy. In many cases, pre-trained models are sourced from vendors who may have disrupted the training pipeline to insert Trojan…

As the number of IoT devices has increased rapidly, IoT botnets have exploited the vulnerabilities of IoT devices. However, it is still challenging to detect the initial intrusion on IoT devices prior to massive attacks. Recent studies have…

As training artificial intelligence (AI) models is a lengthy and hence costly process, leakage of such a model's internal parameters is highly undesirable. In the case of AI accelerators, side-channel information leakage opens up the threat…

This paper presents an experimental design and data analytics approach aimed at power-based malware detection on general-purpose computers. Leveraging the fact that malware executions must consume power, we explore the postulate that…

In recent years a new class of side-channel attacks has emerged. Instead of targeting device emissions during dynamic computation, adversaries now frequently exploit the leakage or response behaviour of integrated circuits in a static…