Abstract In the realm of lightweight cryptography, the construction of efficient and secure diffusion layers constitutes a critical component in block cipher design. The evaluation of diffusion layers primarily focuses on their cryptographic properties and circuit implementation costs. We propose an automatic model based on the g-XOR metric to design optimal binary diffusion layers with the lowest latency. The model employs graph theory, and from a circuit implementation perspective, translates branch number, invertibility, circuit area and depth into mathematical problems. Under these constraints, it explores improved implementations of Maximum Distance Binary Linear (MDBL) matrices (achieving maximum branch number) across different dimensions from 4 to 16 under the minimum latency. We find the tight lower bound for the implementation area of matrices with dimensions 4, 6, and 8, all with branch number of 4. We present matrices with dimensions 8 and 10, and branch numbers 5 and 6, whose implementation cost outperform existing results. For the first time, we provide optimized experimental results for dimensions 12, 14, and 16 with a branch number of 8 under the minimum depth of 3. Our work achieves the lowest latency, facilitating the design of highly efficient lightweight block ciphers.

Deep learning (DL) methods have shown great potential for remote sensing image change detection recently, but still suffer from several limitations. Within the identical semantic concept, significant but irrelevant changes in surface texture, color and spatial shifting of building objects resulted from variations in imaging physical factors, causes feature inconsistency of building objects in bitemporal sences. Conventional DL methods lack the capability to effectively distinguish real changes from irrelevant changes, leading to some false detections. This letter propose a novel framework, the multi-scale residual alignment transformer (AlignFormer), to mitigate the above issues. Specifically, inspired by deformable attention mechanism, we firstly design an adaptive feature alignment module (AFAM) to suppress the inconsistency of feature pairs, where the regions of building can be adaptively focused on and the spatial-temporal dependencies of relevant building objects in feature pairs effectively captured, via scheme of flexibly sampling Keys/Values for each given Query. Besides, we utilize an extremely tiny Swin Transformer as the backbone of differencing-based framework for obtaining hierarchical features. Moreover, inspired by residual learning strategy, three AFAMs are integrated into the framework to form the multi-scale residual architecture for the coarse-to-fine alignment of the paired features. Experimental results confirm the superiority of our proposed method over several state-of-the-art algorithms. Our code will be released at https://github.com/lilei-aircas/AlignFormer_CD.

GOST, known as GOST-28147-89, was standardized as the Russian encryption standard in 1989. It is a lightweight-friendly cipher and suitable for the resource-constrained environments. However, due to the simplicity of GOST’s key schedule, it encountered reflection attack and fixed point attack. In order to resist such attacks, the designers of GOST proposed a modification of GOST, namely, 2-GOST. This new version changes the order of subkeys in the key schedule and uses concrete S-boxes in round function. But regarding single-key attacks on full-round 2-GOST, Ashur et al. proposed a reflection attack with data of 2 32 on a weak-key class of size 2 224 , as well as the fixed point attack and impossible reflection attack with data of 2 64 for all possible keys. Note that the attacks applicable for all possible keys need the entire plaintext space. In other words, these are codebook attacks. In this paper, we propose single-key attacks on 2-GOST with only about 2 32 data instead of codebook. Firstly, we apply 2-dimensional meet-in-the-middle attack combined with splice-cut technique on full-round 2-GOST. This attack is applicable for all possible keys, and its data complexity reduces from previous 2 64 to 2 32 . Besides that, we apply splice-cut meet-in-the-middle attack on 31-round 2-GOST with only data of 2 32 . In this attack, we only need 8 bytes of memory, which is negligible.

An enormous number of applications have been developed for Android in recent years, making it one of the most popular mobile operating systems. However, it is obvious that more vulnerabilities would appear along with the booming amounts of applications. Poorly designed applications may contain security vulnerabilities that can dramatically undermine users' security and privacy. In this paper, we studied a kind of recently reported application vulnerability named confused deputy - a specific type of privilege escalation vulnerability, which can result in unauthorized operations, and so on. We proposed a novel system with code-level static analysis to analyze the applications and automatically detect possible confused deputy vulnerabilities. To tackle analysis challenges imposed by Android's component-based programming paradigm, we employed special control flow graph construction techniques to build call relations among components and function call graph within components. We developed a prototype of this system named PaddyFrog and evaluated with 7190 real world Android applications from two of the most popular markets in China. We found 1240 applications with confused deputy vulnerability and proved to be exploitable. The median execution time of this system on an application is 14.4s, which is fast enough to be used in volumes of applications testing scenarios. Copyright © 2015 John Wiley & Sons, Ltd.

Considering the incompleteness of localization information in wireless sensor networks, the sensor network monitoring region was divided into a plurality of small grids. Sensors and targets are randomly dropped in the grids. Defining the targets position information as a sparse vector, a range-free multiple target localization algorithm using compressive sensing theory (MTLCS) was proposed. Only targets number sensed by sensor nodes is needed in the algorithm. It doesn't depend on extra hardware measurements. MTLCS can provide the targets position with sparse detected information. The number of targets detected by sensor nodes was expressed as the product of measurement matrix, sparse matrix and sparse vector in compressive sensing theory. Targets are localized with the sparse signal reconstruction. In order to explore MTLCS performance, BP and OMP are applied to recover targets localization. In case of grid number N=20 × 20, simulation is done with different measurement noise, sensing radius, targets number and sensor numbers. In case of 0.6≤r/n≤0.8, simulation results show that MTLCS has the localization error smaller than 30% without physical distance measurement. MTLCS can satisfy the requirements of target localization in wireless sensor network in the case of incomplete information.

We consider reliable facility location models in which facilities are subject to unexpected failures, and customers may be reassigned to facilities other than their regular facilities. The objective is to minimize the total expected costs in normal and failure scenarios. We allow facilities to have different failure rates and do not limit the number of facilities that might be assigned to a customer. Lower bounds for reliable uncapacitated fixed-charge location problem (RUFLP) are derived and used to introduce a class of efficient algorithms for solving the RUFLP problem.

Abstract The input to an inverse shortest path lengths problem (ISPL) consists of a graph G with arc weights, and a collection of source‐sink pairs with prescribed distances that do not necessarily conform to the shortest path lengths in G. The goal is to modify the arc weights, subject to a penalty on the deviation from the given weights, so that the shortest path lengths are equal to the prescribed values. We show that although ISPL is an NP‐hard problem, several ISPL classes are polynomially solvable. These cases include ISPL where the collection of the pairs share a single source and all other nodes as destinations (the single‐source all‐sink problem SAISPL). For the case where the collection contains a single node pair (the single‐source single‐sink problem SSISPL), we identify conditions on the uniformity of the penalty functions and on the original arc weights, which make SSISPL polynomially solvable. These results cannot be strengthened significantly as the general single‐source ISPL is NP‐hard and the all‐sink case, with more than one source, is also NP‐hard. We further provide a convex programming formulation for a relaxation of ISPL in which the shortest path lengths are only required to be no less than the given values (LBISPL). It is demonstrated how this compact formulation leads to efficient algorithms for ISPL. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010