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Persistent fault analysis is effective when a single element of the S-box is altered. However, most fault injection techniques tend to induce multiple faults. In such multiple-fault scenarios, existing key recovery methods either fail to identify the unique key or require high computational complexity. To address this, we propose the count-ranking method to accelerate Persistent-Fault Based Differential Analysis (PFDA). The method counts subkey byte frequencies and selects the most frequent value per byte. Additionally, this approach allows us to further explore faults occurring during deeper rounds of encryption, as it independently determines the unique value of each subkey byte. Consequently, we successfully recover the unique key for both serial and parallel implementations of AES with a complexity of O ( N f 2 × N c ) table lookups. Finally, the count-ranking method facilitates the recovery of several subkeys in Feistel ciphers without dramatically increasing the number of key candidates. We apply the revised PFDA to both DES and Camellia, efficiently achieving full-key recovery. These results demonstrate the practical applicability of the extended PFDA across both serial and parallel cipher architectures.

Persistent fault analysis (PFA) has emerged as a powerful technique that can recover the secret key by influencing ciphertext distribution. Most research work highlights its application for investigating the last round key. This work presents PFA attack methods to recover deeper round keys of SPN ciphers, wherein the last round key alone can not determine the entire master key. We use GIFT and KLEIN ciphers to validate our methods and show the effectiveness of the proposed approach through simulation. We could recover the full master keys of both the GIFT cipher versions by retrieving the round keys up to the depth 2 and 4 for GIFT-128 and GIFT-64, respectively. Our method recovered KLEIN’s last round key and penultimate round key in average 75 and 180 ciphertexts, respectively. We also analyzed the success rate of our approach for varying depths and Hamming distances . In GIFT-64, for Hamming distance 1, keys were recovered in approximately 110, 290, and 750 ciphertexts for round numbers 28, 27, and 26, respectively, with a 100% success rate. For round 25, around 2000 ciphertexts were sufficient to recover the round key in 90% of the cases out of 1000 experiments. For 39th round of GIFT-128, the round key can be recovered with a 100% success rate in roughly 380, 575, and 1100 ciphertexts for the Hamming distance 1, 2, and 3, respectively. However, for the same round with Hamming distance of value 4, the success rate is 75% for around 2000 ciphertexts. In addition, we propose a countermeasure to thwart PFA attacks and Intermediate-oriented fault attacks, such as, differential fault analysis.

基于持久性故障的碰撞攻击可以有效恢复 AES 加密/解密模块 (基于 S 盒实现) 中使用的密钥. 现实中处理长消息需要调用相应的工作模式, 不能满足基于持久性故障的碰撞攻击的前提假设. 此外, 广泛应用的开源密码库 OpenSSL中 AES 密码模块采用多个 T 盒而非 S 盒实现, 导致已有的持久性故障注入模式失效. 本文针对 OpenSSL 中的不同工作模式分别研究.对于 ECB 模式, 通过分别在 T 盒注入置零故障或随机故障, 分别攻击 ECB 模式加/解密实现; 对于 CBC 模式, 通过挑战密文的方式攻击 CBC 模式的解密实现, 从而避免加密时输入随机初始向量对中间值的干扰; 对于 OFB 和 CFB 这种不直接操作消息的模式, 通过挑战密文的方式仍能成功攻击. 本文还证明了获得加密模块 (或解密模块) 的直接输出并非持久性故障碰撞攻击的必要条件, 并通过对 CMAC 的分析验证了只要可以观测到中间状态的碰撞信息, 就可以恢复密钥. 通过在 PC 上仿真注入故障, 针对上述工作模式实施密钥恢复实验, 表明不论是单字节故障还是多字节故障, 攻击成功率都为 100%.

This paper is concerned with a robust adaptive fault-tolerant compensation control problem based on sliding mode technique for an unmanned marine vehicle (UMV) with thruster faults and unknown persistent ocean disturbances. A general thruster fault model including partial, total and time-varying stuck is built for the first time. Once the thrusters occur unknown and time-varying stuck faults, the mission of the UMV may be canceled. To avoid it, full-rank decomposition of the thruster configuration matrix is made, based on which a linear sliding surface is constructed and adaptive mechanism is incorporated into sliding mode reaching law. Without the prior knowledge of ocean external disturbances, sliding mode stability is analyzed and a sufficient stability condition through H∞ technique is given. Further the nonlinear unit vector gain of the adaptive sliding mode fault-tolerant compensation controller is designed to ensure the UMV system errors converge to zero independent of fault detection and diagnosis (FDD) mechanism. Finally, the comparison simulation results through a typical floating production ship are shown to testify the feasibility of the presented method.

In the Beijing Plain, land subsidence is one of the most prominent geological problems, which is affected by multiple factors. Groundwater exploitation, thickness of the Quaternary deposit and urban development and construction are important factors affecting the formation and development of land subsidence. Here we choose groundwater level change, thickness of the Quaternary deposit and index-based built-up index (IBI) as influencing factors, and we use the influence factors to predict the subsidence amount in the Beijing Plain. The Sentinel-1 radar images and the persistent scatters interferometry (PSI) were adopted to obtain the information of land subsidence. By using Google Earth Engine platform and Landsat8 optical images, IBI was extracted. Groundwater level change and thickness of the Quaternary deposit were obtained from hydrogeological data. Machine learning algorithms Linear Regression and Principal Component Analysis (PCA) were used to investigate the relationship between land subsidence and influencing factors. Based on the results obtained by Linear Regression and PCA, a suitable machine learning algorithm was selected to predict the subsidence amount in the Beijing Plain in 2018 through influencing factors. In this study, we found that the maximum subsidence rate in the Beijing Plain had reached 115.96 mm/y from 2016 to 2018. The land subsidence was serious in eastern Chaoyang and northwestern Tongzhou. In addition, the area where thickness of the Quaternary deposit reached 150–200 m was prone to more serious land subsidence in the Beijing Plain. In groundwater exploitation, the second confined aquifer had the greatest impact on land subsidence. Through Linear Regression and PCA, we found that the relationship between land subsidence and influencing factors was nonlinear. XGBoost was feasible to predict subsidence amount. The prediction accuracy of XGBoost on the subsidence amount reached 0.9431, and the mean square error was controlled at 15.97. By using XGBoost to predict the subsidence amount, our research provides a new idea for land subsidence prediction.

This paper deals with the fault detection observer (FDO) design for discrete-time switched singular systems (SSSs) under persistent dwell time (PDT) switching. Compared to the widely used dwell time and average dwell time switching in the literature, PDT switching is more general due to its covering such two switchings as special cases. The PDT switching is firstly employed to establish the admissibility criterion and the H ∞ and H - observer synthesis conditions for SSSs. Also, an efficient H - / H ∞ FDO design algorithm is proposed. First, the admissibility analysis of the observing error system is addressed by incorporating the PDT technique into the multiple Lyapunov function method, and an admissibility criterion in the form of linear matrix inequality is established. Based on this, two FDO synthesis conditions are then developed to guarantee that the generated residual signal achieves prescribed H ∞ and H - performance with regard to disturbances and faults, respectively. The FDO should be designed such that the effects of faults and disturbances on the residual signal are maximized and minimized, respectively. To this end, the FDO design is expressed as a multi-objective optimization problem, and the FDO gains are characterized in terms of the solution of the multi-objective optimization problem. Moreover, a suitable trade-off between the robustness to disturbances and the sensitivity to faults is obtained based on a corresponding proposed algorithm. Finally, one illustrative example is given to show the validity of the developed method.

The work proposes an original method for modeling and simulating the triggering of 110 kV interconnection power lines in case of common faults, such as transient or persistent faults. Urban and industrial areas, surrounding urban areas, require a high energy consumption that is being supplied through 110 kV overhead power lines, responsible for distributing power to the industrial and domestic consumers. High voltage distribution power lines are most prone to failure, due to their exposure, affecting a large number of consumers if a fault occurs. Faults of power lines in service certify that currently there is no perfectly controllable operation mode in terms of load rating, environmental factors, insulation resistance, or mechanical resistance, which would allow total avoidance of faults, it is only possible to reduce the impact they have on the network as a whole. Mathematical models have been developed to determine the experimental voltage and current responses describing the fault propagation, expressed as a 7th-degree polynomial curve, as a second-order transfer function or as the Gaussian model type. By comparing these mathematical models, the most probable answers that can lead to the development of a control structure for rapid identification of a fault were obtained, with the possibility of triggering the line protection relay. In the final part of the manuscript, the viability of applying artificial intelligence techniques, for the approached fault management application, is proven. The developed control structure evaluates the nature of the fault and determines a faster reaction of the line protection causing an increase in the performance of the distribution service.

This paper analyzes the timing performance of a persistent storage designed for distributed container-based architectures in industrial control applications. The timing performance analysis is conducted using an in-house simulator, which mirrors our testbed specifications. The storage ensures data availability and consistency even in presence of faults. The analysis considers four aspects: 1. placement strategy, 2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Comparison results also reveal that, while the method may underperform current centralized solutions in fault-free conditions, it outperforms the centralized solutions in failure scenario. Moreover, the used evaluation method is applicable for assessing other container-based critical applications with timing constraints that require persistent storage.

PSI data are extremely useful for monitoring on-ground displacements. In many cases, clustering algorithms are adopted to highlight the presence of homogeneous patterns; however, clustering algorithms can fail to consider spatial constraints and be poorly specific in revealing patterns at lower scales or possible anomalies. Hence, we proposed a novel framework which combines a spatially-constrained clustering algorithm (SKATER) with a hypothesis testing procedure which evaluates and establishes the presence of significant local spatial correlations, namely the LISA method. The designed workflow ensures the retrieval of homogeneous clusters and a reliable anomaly detection; to validate this workflow, we collected Sentinel-1 time series from the Sibari and Metaponto coastal plains in Italy, ranging from 2015 to 2021. This particular study area is interesting due to the presence of important industrial and agricultural settlements. The proposed workflow effectively outlines the presence of both subsidence and uplifting that deserve to be focused and continuous monitoring, both for environmental and infrastructural purposes.

Algebraic persistent fault analysis (APFA) combines algebraic analysis with persistent fault analysis, providing a novel approach for examining block cipher implementation security. Since its introduction, APFA has attracted considerable attention. Traditionally, APFA has assumed that fault injection occurs solely within the S-box during the encryption process. Yet, algorithms like PRESENT and AES also utilize S-boxes in the key scheduling phase, sharing the same S-box implementation as encryption. This presents a previously unaddressed challenge for APFA. In this work, we extend APFA’s fault injection and analysis capabilities to encompass the key scheduling stage, validating our approach on PRESENT. Our experimental findings indicate that APFA continues to be a viable approach. However, due to faults arising during the key scheduling process, the number of feasible candidate keys does not converge. To address this challenge, we expanded the depth of our fault analysis without increasing the number of faulty ciphertexts, effectively narrowing the key search space to near-uniqueness. By employing a compact S-box modeling approach, we were able to construct more concise algebraic equations with solving efficiency improvements ranging from tens to hundreds of times for PRESENT, SKINNY and CRAFT block ciphers. The efficiency gains became even more pronounced as the depth of the fault leakage increased, demonstrating the robustness and scalability of our approach.