Side-channel Attacks with Multi-thread Mixed Leakage

Side-channel attacks are one of the greatest practical threats to security-related applications, because they are capable of breaking ciphers that are assumed to be mathematically secure. Lots of studies have been devoted to power or electro-magnetic (EM) analysis against desktop CPUs, mobile CPUs (including ARM, MSP, AVR, etc) and FPGAs, but rarely targeted modern GPUs. Modern GPUs feature their special and specific single instruction multiple threads (SIMT) execution fashion, which makes their power/EM leakage more sophisticated in practical scenarios. In this paper, we study side-channel attacks with leakage from SIMT systems, and propose leakage models suited to any SIMT systems and specifically to CUDA-enabled GPUs. Afterwards, we instantiate the models with a GPU AES implementation, which is also used for performance evaluations. In addition to the models, we provide optimizations on the attacks that are based on the models. To evaluate the models and optimizations, we run the GPU AES implementation on a CUDA-enabled GPU and, at the same time, collect its EM leakage. The experimental results show that the proposed models are more efficient and the optimizations are effective as well. Our study suggests that GPU-based cryptographic implementations may be much vulnerable to microarchitecture-based side-channel attacks. Therefore, GPU-specific countermeasures should be considered for GPU-based cryptographic implementations in practical applications

How Does Strict Parallelism Affect Security? A Case Study on the Side-Channel Attacks against GPU-based Bitsliced AES Implementation

Parallel cryptographic implementations are generally considered to be more advantageous than their non-parallel counterparts in mitigating side-channel attacks because of their higher noise-level. So far as we know, the side-channel security of GPU-based cryptographic implementations have been studied in recent years, and those implementations then turn out to be susceptible to some side-channel attacks. Unfortunately, the target parallel implementations in their work do not achieve strict parallelism because of the occurrence of cached memory accesses or the use of conditional branches, so how strict parallelism affects the side-channel security of cryptographic implementations is still an open problem. In this work, we make a case study of the side-channel security of a GPU-based bitsliced AES implementation in terms of bit-level parallelism and thread-level parallelism in order to show the way that works to reduce the side-channel security of strict parallel implementations. We present GPU-based bitsliced AES implementation as the study case because (1) it achieves strict parallelism so as to be resistant to cache-based attacks and timing attacks; and (2) it achieves both bit-level parallelism and thread-level parallelism (a.k.a. task-level parallelism), which enables us to research from multiple perspectives. More specifically, we first set up our testbed and collect electro-magnetic (EM) traces with some special techniques. Then, the measured traces are analyzed in two granularity. In bit-level parallelism, we give a non-profiled leakage detection test before mounting attacks with our proposed bit-level fusion techniques like multi-bits feature-level fusion attacks (MBFFA) and multi-bits decision-level fusion attacks (MBDFA). In thread-level parallelism, a profiled leakage detection test is employed to extract some special information from multi-threads leakages, and with the help of those information our proposed multi-threads hybrid fusion attack (MTHFA) method takes effect. Last, we propose a simple metric to quantify the side-channel security of parallel cryptographic implementations. Our research shows that the secret key of our target implementation can be recovered with less cost than expected, which suggests that the side-channel security of parallel cryptographic implementations should be reevaluated before application

Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models

Task-oriented dialogue (TOD) systems facilitate users in executing various activities via multi-turn dialogues, but Large Language Models (LLMs) often struggle to comprehend these intricate contexts. In this study, we propose a novel "Self-Explanation" prompting strategy to enhance the comprehension abilities of LLMs in multi-turn dialogues. This task-agnostic approach requires the model to analyze each dialogue utterance before task execution, thereby improving performance across various dialogue-centric tasks. Experimental results from six benchmark datasets confirm that our method consistently outperforms other zero-shot prompts and matches or exceeds the efficacy of few-shot prompts, demonstrating its potential as a powerful tool in enhancing LLMs' comprehension in complex dialogue tasks

Relational Topology-based Heterogeneous Network Embedding for Predicting Drug-Target Interactions

ABSTRACTPredicting interactions between drugs and target proteins has become an essential task in the drug discovery process. Although the method of validation via wet-lab experiments has become available, experimental methods for drug-target interaction (DTI) identification remain either time consuming or heavily dependent on domain expertise. Therefore, various computational models have been proposed to predict possible interactions between drugs and target proteins. However, most prediction methods do not consider the topological structures characteristics of the relationship. In this paper, we propose a relational topology-based heterogeneous network embedding method to predict drug-target interactions, abbreviated as RTHNE_ DTI. We first construct a heterogeneous information network based on the interaction between different types of nodes, to enhance the ability of association discovery by fully considering the topology of the network. Then drug and target protein nodes can be represented by the other types of nodes. According to the different topological structure of the relationship between the nodes, we divide the relationship in the heterogeneous network into two categories and model them separately. Extensive experiments on the real-world drug datasets, RTHNE_DTI produces high efficiency and outperforms other state-of-the-art methods. RTHNE_DTI can be further used to predict the interaction between unknown interaction drug-target pairs

SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents

Task-oriented dialogue (TOD) models have made significant progress in recent years. However, previous studies primarily focus on datasets written by annotators, which has resulted in a gap between academic research and real-world spoken conversation scenarios. While several small-scale spoken TOD datasets are proposed to address robustness issues such as ASR errors, they ignore the unique challenges in spoken conversation. To tackle the limitations, we introduce SpokenWOZ, a large-scale speech-text dataset for spoken TOD, containing 8 domains, 203k turns, 5.7k dialogues and 249 hours of audios from human-to-human spoken conversations. SpokenWOZ further incorporates common spoken characteristics such as word-by-word processing and reasoning in spoken language. Based on these characteristics, we present cross-turn slot and reasoning slot detection as new challenges. We conduct experiments on various baselines, including text-modal models, newly proposed dual-modal models, and LLMs, e.g., ChatGPT. The results show that the current models still have substantial room for improvement in spoken conversation, where the most advanced dialogue state tracker only achieves 25.65% in joint goal accuracy and the SOTA end-to-end model only correctly completes the user request in 52.1% of dialogues. The dataset, code, and leaderboard are available: https://spokenwoz.github.io/SpokenWOZ-github.io/

Pretreating poplar cuttings with low nitrogen ameliorates salt stress responses by increasing stored carbohydrates and priming stress signaling pathways

Soil salinity is a widespread stress in semi-arid forests worldwide, but how to manage nitrogen (N) nutrition to improve plant saline tolerance remains unclear. Here, the cuttings of a widely distributed poplar from central Asia, Populus russikki Jabl., were exposed to either normal or low nitrogen (LN) concentrations for two weeks in semi-controlled greenhouse, and then they were added with moderate salt solution or not for another two weeks to evaluate their physiological, biochemical, metabolites and transcriptomic profile changes. LN-pretreating alleviated the toxicity caused by the subsequent salt stress in the poplar plants, demonstrated by a significant reduction in the influx of Na+ and Cl- and improvement of the K+/Na+ ratio. The other salt-stressed traits were also ameliarated, indicated by the variations of chlorophyll content, PSII photochemical activity and lipid peroxidation. Stress alleviation resulted from two different processes. First, LN pretreatment caused a significant increase of non-structural carbohydrates (NSC), allowed for an increased production of osmolytes and a higher potential fueling ion transport under subsequent salt condition, along with increased transcript levels of the cation/H+ ATPase. Second, LN pretreatment enhanced the transcript levels of stress signaling components and phytohormones pathway as well as antioxidant enzyme activities. The results indicate that early restrictions of N supply could enhance posterior survival under saline stress in poplar plants, which is important for plantation programs and restoration activities in semi-arid areas.This research was supported by Natural Science Foundation of China ( 31770644 and 31270660 ), Project of Innovation research team in Sichuan Education Administration in China (No. 13TD0023 )

Exogenous treatment with melatonin enhances waterlogging tolerance of kiwifruit plants

Waterlogging stress has an enormous negative impact on the kiwifruit yield and quality. The protective role of exogenous melatonin on water stress has been widely studied, especially in drought stress. However, the research on melatonin-induced waterlogging tolerance is scarce. Here, we found that treatment with exogenous melatonin could effectively alleviate the damage on kiwifruit plants in response to waterlogging treatment. This was accompanied by higher antioxidant activity and lower ROS accumulation in kiwifruit roots during stress period. The detection of changes in amino acid levels of kiwifruit roots during waterlogging stress showed a possible interaction between melatonin and amino acid metabolism, which promoted the tolerance of kiwifruit plants to waterlogging. The higher levels of GABA and Pro in the roots of melatonin-treated kiwifruit plants partly contributed to their improved waterlogging tolerance. In addition, some plant hormones were also involved in the melatonin-mediated waterlogging tolerance, such as the enhancement of ACC accumulation. This study discussed the melatonin-mediated water stress tolerance of plants from the perspective of amino acid metabolism for the first time