Related papers: STBPU: A Reasonably Secure Branch Prediction Unit

Spiking Neural Networks (SNNs) have gained increasing attention for their superior energy efficiency compared to Artificial Neural Networks (ANNs). However, their security aspects, particularly under backdoor attacks, have received limited…

Secure speculation schemes have shown great promise in the war against speculative side-channel attacks, and will be a key building block for developing secure, high-performance architectures moving forward. As the field matures, the need…

Intel SGX and hypervisors isolate non-privileged programs from other software, ensuring confidentiality and integrity. However, side-channel attacks continue to threaten Intel SGX's security, enabling malicious OS to manipulate PTE present…

Modern processors widely equip the Performance Monitoring Unit (PMU) to collect various architecture and microarchitecture events. Software developers often utilize the PMU to enhance program's performance, but the potential side effects…

Split learning enables collaborative deep learning model training while preserving data privacy and model security by avoiding direct sharing of raw data and model details (i.e., sever and clients only hold partial sub-networks and exchange…

Deep neural networks can be fooled by adversarial attacks: adding carefully computed small adversarial perturbations to clean inputs can cause misclassification on state-of-the-art machine learning models. The reason is that neural networks…

Machine learning has become a critical component of modern data-driven online services. Typically, the training phase of machine learning techniques requires to process large-scale datasets which may contain private and sensitive…

Many contemporary applications feature multi-megabyte instruction footprints that overwhelm the capacity of branch target buffers (BTB) and instruction caches (L1-I), causing frequent front-end stalls that inevitably hurt performance. BTB…

Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs). Recent works have shown that DNNs are vulnerable to adversarial…

Performance-enhancing mechanisms such as branch prediction, out-of-order execution, and return stack buffer (RSB) have been widely employed in today's modern processing units. Although successful in increasing the CPU performance,…

Research on transient execution attacks including Spectre and Meltdown showed that exception or branch misprediction events might leave secret-dependent traces in the CPU's microarchitectural state. This observation led to a proliferation…

Federated learning is a promising approach for training machine learning models while preserving data privacy. However, its distributed nature makes it vulnerable to backdoor attacks, particularly in NLP tasks, where related research…

Binarized Neural Networks (BNN) offer efficient implementations for machine learning tasks and facilitate Privacy-Preserving Machine Learning (PPML) by simplifying operations with binary values. Nevertheless, challenges persist in terms of…

Recent discovery of security attacks in advanced processors, known as Spectre and Meltdown, has resulted in high public alertness about security of hardware. The root cause of these attacks is information leakage across "covert channels"…

Intel SGX is known to be vulnerable to a class of practical attacks exploiting memory access pattern side-channels, notably page-fault attacks and cache timing attacks. A promising hardening scheme is to wrap applications in hardware…

Practical attacks that exploit speculative execution can leak confidential information via microarchitectural side channels. The recently-demonstrated Spectre attacks leverage speculative loads which circumvent access checks to read…

Spectre attacks exploit speculative execution to leak sensitive information. In the last few years, a number of static side-channel detectors have been proposed to detect cache leakage in the presence of speculative execution. However,…

Cyber-physical systems (CPS) integrate sensing, computing, communication and actuation capabilities to monitor and control operations in the physical environment. A key requirement of such systems is the need to provide predictable…

In the last years, Deep Learning technology has been proposed in different fields, bringing many advances in each of them, but identifying new threats in these solutions regarding cybersecurity. Those implemented models have brought several…

Recent security vulnerabilities that target speculative execution (e.g., Spectre) present a significant challenge for processor design. The highly publicized vulnerability uses speculative execution to learn victim secrets by changing cache…