Related papers: DRAM-Profiler: An Experimental DRAM RowHammer Vuln…

Desktops and laptops can be maliciously exploited to violate privacy. In this paper, we consider the daily battle between the passive attacker who is targeting a specific user against a user that may be adversarial opponent. In this…

DRAM scaling has exacerbated the RowHammer vulnerability. To counter this, JEDEC recently introduced Per Row Activation Counting (PRAC) with the Alert Back-Off protocol as an optional DDR5 feature. While promising, PRAC requires per-row…

Rowhammer is a well-studied DRAM phenomenon wherein multiple activations to a given row can cause bit flips in adjacent rows. Many mitigation techniques have been introduced to address Rowhammer, with some support being incorporated into…

We consider Parallel Random Access Machine (PRAM) which has some processors and memory cells faulty. The faults considered are static, i.e., once the machine starts to operate, the operational/faulty status of PRAM components does not…

The security vulnerabilities due to Rowhammer have worsened over the last decade, with existing in-DRAM solutions, such as TRR, getting broken with simple patterns. In response, the DDR5 specifications have been extended to support Per-Row…

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…

Emerging electronic devices are promising to drive the performance of computer systems to new heights, against the notable saturation in traditional transistor-based architectures. Among them, resistive RAM -- or ReRAM -- has attracted a…

This article summarizes key results of our work on experimental characterization and analysis of latency variation and latency-reliability trade-offs in modern DRAM chips, which was published in SIGMETRICS 2016, and examines the work's…

Predictable execution time upon accessing shared memories in multi-core real-time systems is a stringent requirement. A plethora of existing works focus on the analysis of Double Data Rate Dynamic Random Access Memories (DDR DRAMs), or…

A fundamental assumption in software security is that memory contents do not change unless there is a legitimate deliberate modification. Classical fault attacks show that this assumption does not hold if the attacker has physical access.…

Machine learning based solutions have been very helpful in solving problems that deal with immense amounts of data, such as malware detection and classification. However, deep neural networks have been found to be vulnerable to adversarial…

Resistive Random Access Memory (RRAM) is a type of Non-Volatile Memory (NVM). In this paper we investigate the sensitivity of the TiN/Ti/Al:HfO2/TiN-based 1T-1R RRAM cells implemented in a 250 nm CMOS IHP technology to the laser irradiation…

DRAM-based main memories have read operations that destroy the read data, and as a result, must buffer large amounts of data on each array access to keep chip costs low. Unfortunately, system-level trends such as increased memory contention…

Resistive Random-Access Memory (ReRAM) crossbar arrays are promising candidates for in-situ matrix-vector multiplication (MVM), a frequent operation in Deep Learning algorithms. Despite their advantages, these emerging non-volatile memories…

Deep learning (DL) compilers are core infrastructure in modern DL systems, offering flexibility and scalability beyond vendor-specific libraries. This work uncovers a fundamental vulnerability in their design: can an official, unmodified…

Malware detectors based on deep learning (DL) have been shown to be susceptible to malware examples that have been deliberately manipulated in order to evade detection, a.k.a. adversarial malware examples. More specifically, it has been…

Microarchitectural vulnerabilities increasingly undermine the assumption that hardware can be treated as a reliable root of trust. Prevention mechanisms often lag behind evolving attack techniques, leaving deployed systems unable to assume…

In this work, we investigate if statistical privacy can enhance the performance of ORAM mechanisms while providing rigorous privacy guarantees. We propose a formal and rigorous framework for developing ORAM protocols with statistical…

Since its inception, Rowhammer exploits have rapidly evolved into increasingly sophisticated threats compromising data integrity and the control flow integrity of victim processes. Nevertheless, it remains a challenge for an attacker to…

In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems. However, even the state-of-the-art DRL models have been shown to…