Profiling side-channel analysis (SCA) is widely used to evaluate the security of cryptographic implementations under worst-case attack scenarios. This method assumes a strong adversary with a fully controlled device clone, known as a profiling device, with full access to the internal state of the target algorithm, including the mask shares. However, acquiring such a profiling device in the real world is challenging, as secure products enforce strong life cycle protection, particularly on devices that allow the user partial (e.g., debug mode) or full (e.g., test mode) control. This enforcement restricts access to profiling devices, significantly reducing the effectiveness of profiling SCA. To address this limitation, this paper introduces a novel framework that allows an attacker to create and learn from their own white-box reference design without needing privileged access on the profiling device. Specifically, the attacker first implements the target algorithm on a different type of device with full control. Since this device is a white box to the attacker, they can access all internal states and mask shares. A novel conditional generative adversarial network (CGAN) framework is then introduced to mimic the feature extraction procedure from the reference device and transfer this experience to extract high-order leakages from the target device. These extracted features then serve as inputs for profiled SCA. Experiments show that our approach significantly enhances the efficacy of black-box profiling SCA, matching or potentially exceeding the results of worst-case security evaluations. Compared with conventional profiling SCA, which has strict requirements on the profiling device, our framework relaxes this threat model and, thus, can be better adapted to real-world attacks.

Editor’s notes: Side-channel attacks have been undermining cryptosystems for almost three decades. Advances in machine learning techniques have shown great promise in improving the performance and efficiency of side-channel attacks, even on systems with countermeasures. This article provides a systematic approach to applying ML techniques for side-channel attacks.

Today, the deep learning-based side-channel analysis represents a widely researched topic, with numerous results indicating the advantages of such an approach. Indeed, breaking protected implementations while not requiring complex feature selection made deep learning a preferred option for profiling side-channel analysis. Still, this does not mean it is trivial to mount a successful deep learning-based side-channel analysis. One of the biggest challenges is to find optimal hyperparameters for neural networks resulting in powerful side-channel attacks. This work proposes an automated way for deep learning hyperparameter tuning based on Bayesian optimization. We build a custom framework denoted AutoSCA supporting machine learning and side-channel metrics. Our experimental analysis shows that our framework performs well regardless of the dataset, leakage model, or neural network type. We find several neural network architectures outperforming state-of-the-art attacks. Finally, while not considered a powerful option, we observe that neural networks obtained via random search can perform well, indicating that the publicly available datasets are relatively easy to break.

Speaker identification (SI) determines a speaker's identity based on their utterances. Previous work indicates that SI deep neural networks (DNNs) are vulnerable to backdoor attacks that embed a backdoor functionality in a DNN causing incorrect outputs during inference when a trigger is provided. This is the first work exploring SI DNNs' vulnerability to backdoor attacks using speakers' emotional prosody, resulting in dynamic, inconspicuous triggers. We used three datasets and three DNN architectures to determine the impact of using emotions as backdoor triggers on the accuracy of SI DNNs. Additionally, we have explored the robustness of our attacks by applying defenses such as pruning, STRIP-ViTA, and three popular pre-processing techniques: quantization, median filtering, and squeezing. We show that the aforementioned models are prone to our attack (EmoBack), indicating that emotional triggers (i.e., the most effective being neutral, sad, angry, and surprised prosody) can be effectively used to compromise the integrity of SI DNNs. However, our pruning experiments suggest potential ways to reinforce backdoored models against our attacks across multiple emotions, decreasing the attack success rate up to 41.4%.

The use of deep learning-based side-channel analysis is an effective way of performing profiling attacks on power and electromagnetic leakages, even against targets protected with countermeasures. While many research articles have reported successful results, they typically focus on profiling and attacking a single device, assuming that leakages are similar between devices of the same type. However, this assumption is not always realistic due to variations in hardware and measurement setups, creating what is known as the portability problem. Profiling multiple devices has been proposed as a solution, but obtaining access to these devices may pose a challenge for attackers. This article proposes a new approach to overcome the portability problem by introducing a neural network layer assessment methodology based on the ablation paradigm. This methodology evaluates the sensitivity and resilience of each layer, providing valuable knowledge to create a Multiple Device Model from Single Device (MDMSD). Specifically, it involves ablating a specific neural network section and performing recovery training. As a result, the profiling model, trained initially on a single device, can be generalized to leakage traces measured from various devices. By addressing the portability problem through a single device, practical side-channel attacks could be more accessible and effective for attackers.

Given today's ongoing deployment of deep learning models, ensuring their security against adversarial attacks has become paramount. This paper introduces an enhanced version of the PhantomSponges attack by Shapira et al. The attack exploits the non-maximum suppression (NMS) algorithm in YOLO object detection (OD) models without compromising OD, substantially increasing inference time. Our enhancement focuses on improving the attack's impact on YOLOv5 models by modifying its bounding box area loss term, aiming to directly decrease the intersection over union and, thus, exacerbate the computational load on NMS. Through a parameter study using the Berkeley Deep Drive dataset, we evaluate the enhanced attack's efficacy against various sizes of YOLOv5, demonstrating, under certain circumstances, an improved capability to increase NMS time with a minimal loss in OD accuracy. Furthermore, we propose a novel defense that dynamically resizes input images to mitigate the attack's effectiveness, showcasing a substantial restoration in inference speed and OD accuracy. Our findings show that the enhanced attack could result in a 550% increase in NMS time on the YOLOv5 small configuration. Moreover, our defense's results show a substantial decrease of 90.18% in NMS execution time when applied to an attacked YOLOv5 large model.

Graph neural networks (GNNs) have gained significant popularity as powerful deep learning methods for processing graph data. However, centralized GNNs face challenges in data-sensitive scenarios due to privacy concerns and regulatory restrictions. Federated learning has emerged as a promising technology that enables collaborative training of a shared global model while preserving privacy. Although federated learning has been applied to train GNNs, no research focuses on the robustness of Federated GNNs against backdoor attacks.

This article bridges this research gap by investigating two types of backdoor attacks in Federated GNNs: centralized backdoor attack (CBA) and distributed backdoor attack (DBA). Through extensive experiments, we demonstrate that DBA exhibits a higher success rate than CBA across various scenarios. To further explore the characteristics of these backdoor attacks in Federated GNNs, we evaluate their performance under different scenarios, including varying numbers of clients, trigger sizes, poisoning intensities, and trigger densities. Additionally, we explore the resilience of DBA and CBA against two defense mechanisms. Our findings reveal that both defenses cannot eliminate DBA and CBA without affecting the original task. This highlights the necessity of developing tailored defenses to mitigate the novel threat of backdoor attacks in Federated GNNs.

Byzantine-robust Federated Learning (FL) aims to counter malicious clients and train an accurate global model while maintaining an extremely low attack success rate. Most existing systems, however, are only robust when most of the clients are honest. FLTrust (NDSS '21) and Zeno++ (ICML '20) do not make such an honest majority assumption but can only be applied to scenarios where the server is provided with an auxiliary dataset used to filter malicious updates. FLAME (USENIX '22) and EIFFeL (CCS '22) maintain the semi-honest majority assumption to guarantee robustness and the confidentiality of updates. It is therefore currently impossible to ensure Byzantine robustness and confidentiality of updates without assuming a semi-honest majority. To tackle this problem, we propose a novel Byzantine-robust and privacy-preserving FL system, called MUDGUARD, to capture malicious minority and majority for server and client sides, respectively. Our experimental results demonstrate that the accuracy of MUDGUARD is practically close to the FL baseline using FedAvg without attacks (approximate 0.8% gap on average). Meanwhile, the attack success rate is around 0%-5% even under an adaptive attack tailored to MUDGUARD. We further optimize our design by using binary secret sharing and polynomial transformation leading to communication overhead and runtime decreases of 67%-89.17% and 66.05%-68.75%, respectively.

Recently, attackers have targeted machine learning systems, introducing various attacks. The backdoor attack is popular in this field and is usually realized through data poisoning. To the best of our knowledge, we are the first to investigate whether the backdoor attacks remain effective when manifold learning algorithms are applied to the poisoned dataset. We conducted our experiments using two manifold learning techniques (Autoencoder and UMAP) on two benchmark datasets (MNIST and CIFAR10) and two backdoor strategies (clean and dirty label). We performed an array of experiments using different parameters, finding that we could reach an attack success rate of 95% and 75% even after reducing our data to two dimensions using Autoencoders and UMAP, respectively.

Deep learning found its place in various real-world applications, where many also have security requirements. Unfortunately, as these systems become more pervasive, understanding how they fail becomes more challenging. While there are multiple failure modes in machine learning, one category received significant attention in the last few years-backdoor attacks. Backdoor attacks aim to make a model misclassify some of its inputs to a preset-specific label while other classification results would behave normally. While many works investigate various backdoor attacks and defenses for different domains, no works aim to provide a systematic comparison of backdoor attacks for different scenarios. This work considers backdoor attacks in image, sound, text, and graph domains and provides a comparative analysis of their respective strengths.

This work explores stylistic triggers for backdoor attacks in the audio domain: dynamic transformations of malicious samples through guitar effects. We first formalize stylistic triggers – currently missing in the literature. Second, we explore how to develop stylistic triggers in the audio domain by proposing JingleBack. Our experiments confirm the effectiveness of the attack, achieving a 96% attack success rate. Our code is available in https://github.com/skoffas/going-in-style.

Backdoor attacks have been demonstrated as a security threat for machine learning models. Traditional backdoor attacks intend to inject backdoor functionality into the model such that the backdoored model will perform abnormally on inputs with predefined backdoor triggers and still retain state-of-the-art performance on the clean inputs. While there are already some works on backdoor attacks on Graph Neural Networks (GNNs), the backdoor trigger in the graph domain is mostly injected into random positions of the sample. There is no work analyzing and explaining the backdoor attack performance when injecting triggers into the most important or least important area in the sample, which we refer to as trigger-injecting strategies MIAS and LIAS, respectively. Our results show that, generally, LIAS performs better, and the differences between the LIAS and MIAS performance can be significant. Furthermore, we explain these two strategies’ similar (better) attack performance through explanation techniques, which results in a further understanding of backdoor attacks in GNNs.

SHA-3 is considered to be one of the most secure standardized hash functions. It relies on the Keccak-f[1 600] permutation, which operates on an internal state of 1 600 bits, mostly represented as a 5 x 5 x 64-bit matrix. While existing implementations process the state sequentially in chunks of typically 32 or 64 bits, the Keccak-f[1 600] permutation can benefit a lot from speedup through parallelization. This paper is the first to explore the full potential of parallelization of Keccak-f[1 600] in RISC-V based processors through custom vector extensions on 32-bit and 64-bit architectures. We analyze the Keccak $\mathbf{f}[1 \ 600]$ permutation, composed of five different step mappings, and propose ten custom vector instructions to speed up the computation. We realize these extensions in a SIMD processor described in System Verilog. We compare the performance of our designs to existing architectures based on vectorized application-specific instruction set processors (ASIP). We show that our designs outperform all related work in throughput due to our carefully selected custom vector instructions.

One of the Round 3 Finalists in the NIST post-quantum cryptography call is the Classic McEliece cryptosystem. Although it is one of the most secure cryptosystems, the large size of its public key remains a practical limitation. In this work, we propose a McEliece-type cryptosystem using large minimum distance error-correcting codes derived from self-dual codes. To the best of our knowledge, such codes have not been implemented in a code-based cryptosystem until now. Moreover, we modify the decryption step of the system by introducing a decryption algorithm based on two private keys. We determine the parameters of binary codes with large minimum distance, which, if implemented into a McEliece-type cryptosystem, would provide a security level respectively of 80, 128, and 256 bits. For the 80-bit security case, we construct a large minimum distance self-dual code of length 1064, and use it to derive a random punctured code to be used in the corresponding McEliece-type cryptosystem. Compared to the original McEliece cryptosystem, the key size is reduced by about 38.5%, although an optimal decoding set is yet to be constructed to make the new system fully defined and usable.

Side-channel attacks represent a realistic and serious threat to the security of embedded devices for already almost three decades. A variety of attacks and targets they can be applied to have been introduced, and while the area of side-channel attacks and their mitigation is very well-researched, it is yet to be consolidated. Deep learning-based side-channel attacks entered the field in recent years with the promise of more competitive performance and enlarged attackers' capabilities compared to other techniques. At the same time, the new attacks bring new challenges and complexities to the domain, making the systematization of knowledge (SoK) even more critical.We first dissect deep learning-based side-channel attacks according to the different phases they can be used in and map those phases to the efforts conducted so far in the domain. For each phase, we identify the weaknesses and challenges that triggered the known open problems. We also connect the attacks to the threat models and evaluate their advantages and drawbacks. Finally, we provide a number of recommendations to be followed in deep learning-based side-channel attacks.

Deep learning is a powerful direction for profiling side-channel analysis as it can break targets protected with countermeasures even with a relatively small number of attack traces. Still, it is necessary to conduct hyperparameter tuning to reach strong attack performance, which can be far from trivial. Besides many options stemming from the machine learning domain, recent years also brought neural network elements specially designed for side-channel analysis. The loss function, which calculates the error or loss between the actual and desired output, is one of the most important neural network elements. The resulting loss values guide the weights update associated with the connections between the neurons or filters of the deep learning neural network. Unfortunately, despite being a highly relevant hyperparameter, there are no systematic comparisons among different loss functions regarding their effectiveness in side-channel attacks. This work provides a detailed study of the efficiency of different loss functions in the SCA context. We evaluate five loss functions commonly used in machine learning and three loss functions specifically designed for SCA. Our results show that an SCA-specific loss function (called CER) performs very well and outperforms other loss functions in most evaluated settings. Still, categorical cross-entropy represents a good option, especially considering the variety of neural network architectures.

Evolutionary algorithms have been successfully applied to attack Physically Unclonable Functions (PUFs). CMA-ES is recognized as the most powerful option for a type of attack called the reliability attack. In this paper, we take a step back and systematically evaluate several metaheuristics for the challenge-response pair-based attack on strong PUFs. Our results confirm that CMA-ES has the best performance, but we note several other algorithms with similar performance while having smaller computational costs.

The efficiency of the profiling side-channel analysis can be significantly improved with machine learning techniques. Although powerful, a fundamental machine learning limitation of being data-hungry received little attention in the side-channel community. In practice, the maximum number of leakage traces that evaluators/attackers can obtain is constrained by the scheme requirements or the limited accessibility of the target. Even worse, various countermeasures in modern devices increase the conditions on the profiling size to break the target. This work demonstrates a practical approach to dealing with the lack of profiling traces. Instead of learning from a one-hot encoded label, transferring the labels to their distribution can significantly speed up the convergence of guessing entropy. By studying the relationship between all possible key candidates, we propose a new metric, denoted Label Correlation (LC), to evaluate the generalization ability of the profiling model. We validate LC with two common use cases: early stopping and network architecture search, and the results indicate its superior performance.

Federated Learning (FL) has become very popular since it enables clients to train a joint model collaboratively without sharing their private data. However, FL has been shown to be susceptible to backdoor and inference attacks. While in the former, the adversary injects manipulated updates into the aggregation process; the latter leverages clients' local models to deduce their private data. Contemporary solutions to address the security concerns of FL are either impractical for real-world deployment due to high-performance overheads or are tailored towards addressing specific threats, for instance, privacy-preserving aggregation or backdoor defenses. Given these limitations, our research delves into the advantages of harnessing the FPGA-based computing paradigm to overcome performance bottlenecks of software-only solutions while mitigating backdoor and inference attacks. We utilize FPGA-based enclaves to address inference attacks during the aggregation process of FL. We adopt an advanced backdoor-aware aggregation algorithm on the FPGA to counter backdoor attacks. We implemented and evaluated our method on Xilinx VMK-180, yielding a significant speed-up of around 300 times on the IoT-Traffic dataset and more than 506 times on the CIFAR-10 dataset.

Outsourced training and crowdsourced datasets lead to a new threat for deep learning models: the backdoor attack. In this attack, the adversary inserts a secret functionality in a model, activated through malicious inputs. Backdoor attacks represent an active research area due to diverse settings where they represent a real threat. Still, there is no framework to evaluate existing attacks and defenses in different domains. Only a few toolboxes have been implemented, but most of them focus on computer vision and are difficult to use. To bridge this gap, we implement Backdoor Pony, a framework for evaluating attacks and defenses in different domains through a user-friendly GUI.