On possibility of using convolutional neural networks for creating universal attacks on iterative block ciphers

Исследуется возможность применения свёрточных нейронных сетей к задаче анализа стойкости итеративных блочных шифров. Предлагается новый подход к построению атак-различителей на основе свёрточной нейронной сети, обученной различать графические эквиваленты шифртекстов, полученных в режиме шифрования CTR (счётчика) после разного числа раундов, в том числе после такого, которое обеспечивает удовлетворительные статистические свойства шифртекста. По аналогии со статистическими тестами, предложенный подход позволяет создавать различители без необходимости проведения аналитического исследования каждого шифра, что даёт возможность строить универсальные различители сразу для серии шифров. Предлагается несколько схем построения универсальных атак-различителей, которые, как демонстрируется экспериментально, в ряде случаев позволяют выявлять отклонения от случайности на меньших выборках и при большем числе раундов, чем ранее известные статистические тесты

Undermining User Privacy on Mobile Devices Using AI

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users

Effective Feature Extraction Method for SVM-Based Profiled Attacks

Nowadays, one of the most powerful side channel attacks (SCA) is profiled attack. Machine learning algorithms, for example support vector machine, are currently used for improving the effectiveness of the attack. One issue when using SVM-based profiled attack is extracting points of interest, or features from power traces. So far, studies in SCA domain have selected the points of interest (POIs) from the raw power trace for the classifiers. Our work proposes a novel method for finding POIs that based on the combining variational mode decomposition (VMD) and Gram-Schmidt orthogonalization (GSO). That is, VMD is used to decompose the power traces into sub-signals (modes) of different frequencies and POIs selection process based on GSO is conducted on these sub-signals. As a result, the selected POIs are used for SVM classifier to conduct profiled attack. This attack method outperforms other profiled attacks in the same attack scenario. Experiments were performed on a trace data set collected from the Atmega8515 smart card run on the side channel evaluation board Sakura-G/W and the data set of DPA contest v4 to verify the effectiveness of our method in reducing number of power traces for the attacks, especially with noisy power traces

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

Auto-tune POIs: Estimation of distribution algorithms for efficient side-channel analysis

Due to the constant increase and versatility of IoT devices that should keep sensitive information private, Side-Channel Analysis (SCA) attacks on embedded devices are gaining visibility in the industrial field. The integration and validation of countermeasures against SCA can be an expensive and cumbersome process, especially for the less experienced ones, and current certification procedures require to attack the devices under test using multiple SCA techniques and attack vectors, often implying a high degree of complexity. The goal of this paper is to ease one of the most crucial and tedious steps of profiling attacks i.e. the points of interest (POI) selection and hence assist the SCA evaluation process. To this end, we introduce the usage of Estimation of Distribution Algorithms (EDAs) in the SCA field in order to automatically tune the point of interest selection. We showcase our approach on several experimental use cases, including attacks on unprotected and protected AES implementations over distinct copies of the same device, dismissing in this way the portability issue

Safety, security and privacy in machine learning based Internet of Things

Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy attacks, such as denial of service, spoofing, phishing, obfuscations, jamming, eavesdropping, intrusions, and other unforeseen cyber threats to IoT systems. The traditional tools and techniques are not very efficient to prevent and protect against the new cyber-physical security challenges. Robust, dynamic, and up-to-date security measures are required to secure IoT systems. The machine learning (ML) technique is considered the most advanced and promising method, and opened up many research directions to address new security challenges in the cyber-physical systems (CPS). This research survey presents the architecture of IoT systems, investigates different attacks on IoT systems, and reviews the latest research directions to solve the safety and security of IoT systems based on machine learning techniques. Moreover, it discusses the potential future research challenges when employing security methods in IoT systems

An End-to-end Plaintext-based Side-channel Collision Attack without Trace Segmentation

Side-channel Collision Attacks (SCCA) constitute a subset of non-profiling attacks that exploit information dependency leaked during cryptographic operations. Unlike traditional collision attacks, which seek instances where two different inputs to a cryptographic algorithm yield identical outputs, SCCAs specifically target the internal state, where identical outputs are more likely. In CHES 2023, Staib et al. presented a Deep Learning-based SCCA (DL-SCCA), which enhanced the attack performance while decreasing the required effort for leakage preprocessing. Nevertheless, this method inherits the conventional SCCA\u27s limitations, as it operates on trace segments reflecting the target operation explicitly, leading to issues such as portability and low tolerance to errors. This paper introduces an end-to-end plaintext-based SCCA to address these challenges. We leverage the bijective relationship between plaintext and secret data to label the leakage measurement with known information, then learn plaintext-based profiling models to depict leakages from varying operations. By comparing the leakage representations produced by the profiling model, an adversary can reveal the key difference. As an end-to-end approach, we propose an error correction scheme to rectify false predictions. Experimental results indicate our approach significantly surpasses DL-SCCA in terms of attack performance (e.g., success rate increased from 53\% to 100\%) and computational complexity (training time reduced from approximately 2 hours to 10 minutes). These findings underscore our method\u27s effectiveness and practicality in real-world attack scenarios