No-Skim: Towards Efficiency Robustness Evaluation on Skimming-based Language Models

To reduce the computation cost and the energy consumption in large language models (LLM), skimming-based acceleration dynamically drops unimportant tokens of the input sequence progressively along layers of the LLM while preserving the tokens of semantic importance. However, our work for the first time reveals the acceleration may be vulnerable to Denial-of-Service (DoS) attacks. In this paper, we propose No-Skim, a general framework to help the owners of skimming-based LLM to understand and measure the robustness of their acceleration scheme. Specifically, our framework searches minimal and unnoticeable perturbations at character-level and token-level to generate adversarial inputs that sufficiently increase the remaining token ratio, thus increasing the computation cost and energy consumption. We systematically evaluate the vulnerability of the skimming acceleration in various LLM architectures including BERT and RoBERTa on the GLUE benchmark. In the worst case, the perturbation found by No-Skim substantially increases the running cost of LLM by over 145% on average. Moreover, No-Skim extends the evaluation framework to various scenarios, making the evaluation conductible with different level of knowledge

Towards Strengthening Deep Learning-based Side Channel Attacks with Mixup

In recent years, various deep learning techniques have been exploited in side channel attacks, with the anticipation of obtaining more appreciable attack results. Most of them concentrate on improving network architectures or putting forward novel algorithms, assuming that there are adequate profiling traces available to train an appropriate neural network. However, in practical scenarios, profiling traces are probably insufficient, which makes the network learn deficiently and compromises attack performance. In this paper, we investigate a kind of data augmentation technique, called mixup, and first propose to exploit it in deep-learning based side channel attacks, for the purpose of expanding the profiling set and facilitating the chances of mounting a successful attack. We perform Correlation Power Analysis for generated traces and original traces, and discover that there exists consistency between them regarding leakage information. Our experiments show that mixup is truly capable of enhancing attack performance especially for insufficient profiling traces. Specifically, when the size of the training set is decreased to 30% of the original set, mixup can significantly reduce acquired attacking traces. We test three mixup parameter values and conclude that generally all of them can bring about improvements. Besides, we compare three leakage models and unexpectedly find that least significant bit model, which is less frequently used in previous works, actually surpasses prevalent identity model and hamming weight model in terms of attack results

A Survey on Acoustic Side Channel Attacks on Keyboards

Most electronic devices utilize mechanical keyboards to receive inputs, including sensitive information such as authentication credentials, personal and private data, emails, plans, etc. However, these systems are susceptible to acoustic side-channel attacks. Researchers have successfully developed methods that can extract typed keystrokes from ambient noise. As the prevalence of keyboard-based input systems continues to expand across various computing platforms, and with the improvement of microphone technology, the potential vulnerability to acoustic side-channel attacks also increases. This survey paper thoroughly reviews existing research, explaining why such attacks are feasible, the applicable threat models, and the methodologies employed to launch and enhance these attacks.Comment: 22 pages, conferenc

A Review and Comparison of AI Enhanced Side Channel Analysis

Side Channel Analysis (SCA) presents a clear threat to privacy and security in modern computing systems. The vast majority of communications are secured through cryptographic algorithms. These algorithms are often provably-secure from a cryptographical perspective, but their implementation on real hardware introduces vulnerabilities. Adversaries can exploit these vulnerabilities to conduct SCA and recover confidential information, such as secret keys or internal states. The threat of SCA has greatly increased as machine learning, and in particular deep learning, enhanced attacks become more common. In this work, we will examine the latest state-of-the-art deep learning techniques for side channel analysis, the theory behind them, and how they are conducted. Our focus will be on profiling attacks using deep learning techniques, but we will also examine some new and emerging methodologies enhanced by deep learning techniques, such as non-profiled attacks, artificial trace generation, and others. Finally, different deep learning enhanced SCA schemes attempted against the ANSSI SCA Database (ASCAD) and their relative performance will be evaluated and compared. This will lead to new research directions to secure cryptographic implementations against the latest SCA attacks.Comment: This paper has been accepted by ACM Journal on Emerging Technologies in Computing Systems (JETC

Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks

Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces

Analysis of Countermeasures Against Remote and Local Power Side Channel Attacks using Correlation Power Analysis

Countermeasures and deterrents to power side-channel attacks targeting the alteration or scrambling of the power delivery network have been shown to be effective against local attacks where the malicious agent has physical access to the target system. However, remote attacks that capture the leaked information from within the IC power grid are shown herein to be nonetheless effective at uncovering the secret key in the presence of these countermeasures/deterrents. Theoretical studies and experimental analysis are carried out to define and quantify the impact of integrated voltage regulators, voltage noise injection, and integration of on-package decoupling capacitors for both remote and local attacks. An outcome yielded by the studies is that the use of an integrated voltage regulator as a countermeasure is effective for a local attack. However, remote attacks are still effective and hence break the integrated voltage regulator countermeasure. From the experimental analysis, it is observed that within the range of designs\u27 practical values, the adoption of on-package decoupling capacitors provides only a 1.3x increase in the minimum number of traces required to discover the secret key. However, the injection of noise in the IC power delivery network yields a 37x increase in the minimum number of traces to discover. Thus, increasing the number of on-package decoupling capacitors or the impedance between locally measured power and the IC power grid should not be relied on as countermeasures to power side-channel attacks, for remote attack schemes. Noise injection should be considered as it is more effective at scrambling the leaked signal to eliminate sensitive identifying information

SCA-CGAN:A New Side-Channel Attack Method for Imbalanced Small Samples

In recent years, many deep learning and machine learning based side channel analysis (SCA) techniques have been proposed, most of which are based on the optimization of existing network models to improve the performance of SCA. However, in practice, the attacker often captures unbalanced and small samples of data due to various environmental factors that limit and interfere with the successful implementation of SCA. To address this problem, in this paper, we firstly introduced the Conditional Generation Adversarial Network (CGAN). We proposed a new model SCA-CGAN that combines SCA and CGAN. We used it to generate a specified number and class of simulated energy traces to expand and augment the original energy traces. Finally, we used the augmented data to implement SCA and achieved a good result. Through experiments on the unprotected ChipWhisperer (CW) data and the ASCAD jittered dataset, the results shown that the SCA using the augmented data is the most efficient, and the correct key is successfully recovered on both datasets. For the CW dataset, the model accuracy is improved by 20.75% and the traces number required to recover the correct key is reduced by about 79.5%. For the ASCAD jittered dataset, when the jitter is 0 and 50, the traces number required to recover the correct key is reduced by about 76.8% and 75.7% respectively