Related papers: DAPPER: A Performance-Attack-Resilient Tracker for…

Rowhammer is a serious security problem of contemporary dynamic random-access memory (DRAM) where reads or writes of bits can flip other bits. DRAM manufacturers add mitigations, but don't disclose details, making it difficult for customers…

We have witnessed the continuing arms race between backdoor attacks and the corresponding defense strategies on Deep Neural Networks (DNNs). Most state-of-the-art defenses rely on the statistical sanitization of the "inputs" or "latent DNN…

The demand for accurate information about the internal structure and characteristics of dynamic random-access memory (DRAM) has been on the rise. Recent studies have explored the structure and characteristics of DRAM to improve processing…

Code models are increasingly adopted in software development but remain vulnerable to backdoor attacks via poisoned training data. Existing backdoor attacks on code models face a fundamental trade-off between transferability and…

Traditional side-channels take advantage of secrets being used as inputs to unsafe instructions, used for memory accesses, or used in control flow decisions. Constant-time programming, which restricts such code patterns, has been widely…

Deep neural networks are vulnerable to adversarial examples, i.e., carefully-crafted inputs that mislead classification at test time. Recent defenses have been shown to improve adversarial robustness by detecting anomalous deviations from…

In most PUF-based authentication schemes, a central server is usually engaged to verify the response of the device's PUF to challenge bit-streams. However, the server availability may be intermittent in practice. To tackle such an issue,…

Rowhammer is a hardware-based bug that allows the attacker to modify the data in the memory without accessing it, just repeatedly and frequently accessing (or hammering) physically adjacent memory rows. So that it can break the memory…

Ransomware can produce direct and controllable economic loss, which makes it one of the most prominent threats in cyber security. As per the latest statistics, more than half of malwares reported in Q1 of 2017 are ransomware and there is a…

In this work, we propose DRAM-Locker as a robust general-purpose defense mechanism that can protect DRAM against various adversarial Deep Neural Network (DNN) weight attacks affecting data or page tables. DRAM-Locker harnesses the…

Deep learning has shown impressive performance on challenging perceptual tasks and has been widely used in software to provide intelligent services. However, researchers found deep neural networks vulnerable to adversarial examples. Since…

Today's systems have diverse needs that are difficult to address using one-size-fits-all commodity DRAM. Unfortunately, although system designers can theoretically adapt commodity DRAM chips to meet their particular design goals (e.g., by…

Distributed Hash Tables (DHTs) have been used in several applications, but most DHTs have opted to solve lookups with multiple hops, to minimize bandwidth costs while sacrificing lookup latency. This paper presents D1HT, an original DHT…

We note that constituent fields (notably the fraction-of-seconds timestamp field) in the data payload structure of the synchrophasor communication protocol (IEEE C37.118 standard) are overprovisioned relative to real-world usage and needs,…

As SRAM-based caches are hitting a scaling wall, manufacturers are integrating DRAM-based caches into system designs to continue increasing cache sizes. While DRAM caches can improve the performance of memory systems, existing DRAM cache…

Increasing storage density exacerbates DRAM read disturbance, a circuit-level vulnerability exploited by system-level attacks. Unfortunately, existing defenses are either ineffective or prohibitively expensive. Efficient mitigation is…

Return-Oriented Programming (ROP) is a typical attack technique that exploits return addresses to abuse existing code repeatedly. Most of the current return address protecting mechanisms (also known as the Backward-Edge Control-Flow…

Benefiting from its superior feature learning capabilities and efficiency, deep hashing has achieved remarkable success in large-scale image retrieval. Recent studies have demonstrated the vulnerability of deep hashing models to backdoor…

As an increasing amount of research is being done on various applications of sensor networks in adversarial environments, ensuring secure routing becomes of critical importance for the success of such deployments. The problem of designing a…

Caches are used to reduce the speed differential between the CPU and memory to improve the performance of modern processors. However, attackers can use contention-based cache timing attacks to steal sensitive information from victim…