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Benefiting from the rapid development of deep learning, 2D and 3D computer vision applications are deployed in many safe-critical systems, such as autopilot and identity authentication. However, deep learning models are not trustworthy…

Spin Transfer Torque RAM (STTRAM) is a promising candidate for Last Level Cache (LLC) due to high endurance, high density and low leakage. One of the major disadvantages of STTRAM is high write latency and write current. Additionally, the…

Phishing attacks trick victims into disclosing sensitive information. To counter rapidly evolving attacks, we must explore machine learning and deep learning models leveraging large-scale data. We discuss models built on different kinds of…

This paper proposes the use of iterative transfer learning applied to deep learning models for side-channel attacks. Currently, most of the side-channel attack methods train a model for each individual byte, without considering the…

Compression algorithms are widely used as they save memory without losing data. However, elimination of redundant symbols and sequences in data leads to a compression side channel. So far, compression attacks have only focused on the…

Collaborative learning allows multiple clients to train a joint model without sharing their data with each other. Each client performs training locally and then submits the model updates to a central server for aggregation. Since the server…

Federated Learning (FL) enables collaborative model training across distributed devices while safeguarding data and user privacy. However, FL remains susceptible to privacy threats that can compromise data via direct means. That said,…

Modern microgrids depend on distributed sensing and communication interfaces, making them increasingly vulnerable to cyber physical disturbances that threaten operational continuity and equipment safety. In this work, a complete virtual…

The proliferation of ubiquitous computing requires energy-efficient as well as secure operation of modern processors. Side channel attacks are becoming a critical threat to security and privacy of devices embedded in modern computing…

Deep learning models are susceptible to adversarial attacks, where slight perturbations to input data lead to misclassification. Adversarial attacks become increasingly effective with access to information about the targeted classifier. In…

Power side-channel attacks, which can deduce secret data via statistical analysis, have become a serious threat. Masking is an effective countermeasure for reducing the statistical dependence between secret data and side-channel…

Cache side-channel attacks lead to severe security threats to the settings that a CPU is shared across users, e.g., in the cloud. The existing attacks rely on sensing the micro-architectural state changes made by victims, and this…

Cache attacks pose a threat to any code whose execution flow or memory accesses depend on sensitive information. Especially in public clouds, where caches are shared across several tenants, cache attacks remain an unsolved problem. Cache…

Timing side channels have been used to extract cryptographic keys and sensitive documents, even from trusted enclaves. In this paper, we focus on cache side channels created by access to shared code or data in the memory hierarchy. This…

Side-channel attacks on shared hardware resources increasingly threaten confidentiality, especially with the rise of Large Language Models (LLMs). In this work, we introduce Spill The Beans, a novel application of cache side-channels to…

With the outsourcing of design flow, ensuring the security and trustworthiness of integrated circuits has become more challenging. Among the security threats, IC counterfeiting and recycled ICs have received a lot of attention due to their…

During the past decade, Deep Neural Networks (DNNs) proved their value on a large variety of subjects. However despite their high value and public accessibility, the protection of the intellectual property of DNNs is still an issue and an…

Power side-channel attacks are a very effective cryptanalysis technique that can infer secret keys of security ICs by monitoring the power consumption. Since the emergence of practical attacks in the late 90s, they have been a major threat…

Deep learning models achieve remarkable accuracy in computer vision tasks, yet remain vulnerable to adversarial examples--carefully crafted perturbations to input images that can deceive these models into making confident but incorrect…

The wide deployment of Large Language Models (LLMs) has given rise to strong demands for optimizing their inference performance. Today's techniques serving this purpose primarily focus on reducing latency and improving throughput through…