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White-box implementation can ensure the security of cryptographic algorithm in white-box attack environment without changing the inputs and outputs of the original algorithm. Most existing white-box implementations construct a series of lookup tables to protect the key. However, with the development of white-box attack techniques, many white-box implementations have been proved to be insecure. In this paper, a new white-box implementation of SM4 is proposed, which is based on an equivalent partial SPN structure of the SM4 algorithm. Our implementation includes three types of table lookup operations and XOR operations. The round keys are obfuscated with the self-equivalences of the S-box and random affine encodings. Security analysis shows that our implementation can resist BGE-type attack, the attack based on affine equivalence algorithm, the structure attack, the collision attack and differential computational analysis. Furthermore, our scheme requires 8.125 MB of memory. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of resilient vector consensus for a group of dynamic agents against mobile malicious attacks. As real networks often operate under random environment and noises, we approach this problem by considering general random dynamic networks with weighted directed topologies. We propose three types of mobile attack models, which differ in the timing of moving of attackers and the capability of detecting such moving. By employing distributed discrete-time algorithms, the Lyapunov theory and martingale convergence theorem, resilient vector consensus is shown to be reached for all three models when the underlying network satisfies certain stochastic robustness conditions. [ABSTRACT FROM AUTHOR]

A trusted platform module is identified by its endorsement key, while it uses an attestation key to provide attestation services, for example, signing a set of platform configuration registers, providing a timestamp or certifying another of its keys. This paper addresses the problem of how a certificate authority binds the endorsement and attestation keys together. This is necessary for the authority to be able to reliably certify the attestation key. This key binding also enables the authority to revoke the attestation key should the endorsement key be compromised. We study all of the existing solutions and show that they either do not solve the problem or cannot be implemented with a real trusted platform module (or both). We propose a new solution which addresses this problem. We develop a security model for our solution and provide a rigorous security proof under this model. We have also implemented the solution using a real trusted platform module, and our implementation results show that this solution is feasible and efficient. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) systems employing wearable sensors are a promising area of research for tracking human activity. Recently, wearable devices such as smartwatches and sensors have been developed for activity recognition and monitoring. These systems aim to obtain the subject's state within his or her environment by exploiting heterogeneous sensors attached to the body. With the development of deep learning, new strategies have emerged to facilitate and solve the HAR problems. In this work, a deep multilayer bidirectional long-short memory (Bi-LSTM) architecture has been implemented to detect human activities. Instead of training a single model as in traditional LSTM methods, two models are presented in the Bi-LSTM scheme, one for learning the input data sequence and the other for learning the reverse sequence. Finally, a new novel postprocessing approach has been proposed based on windowing and voting in the last step to improve the average F1 score. Comprehensive investigations on the three publicly available datasets consisting of a different set of activities were used to evaluate the performance of the proposed framework. The empirical results of this paper on AReM, Mhealth and PAMAP2 datasets attained 95.46, 95.79 and 93.41% average F1 score, respectively. The results also revealed that selecting the window size and implementing the appropriate voting method has a significant effect on improving the average percentage of the F1 score. [ABSTRACT FROM AUTHOR]

Named Data Networking (NDN) has gained importance in today's era due to a paradigm shift in the Internet usage pattern which revolves around the content rather than the respective host addresses. Three important data structures in NDN are Content Store (CS), Pending Interest Table (PIT) and Forwarding Information Base (FIB). The search time of PIT is quite high since its size grows with the addition of new content names, and the Interest packets which are not served by CS are searched in millions of existing entries in the PIT. Hence, lookup time can be improved if, instead of checking all the available entries, initial scanning is done to determine whether the required content name exists in the PIT or not. In this paper, we propose a Stable Bloom Filter (SBF) based PIT called S-PIT, to minimize the PIT search time by identifying the existence of query content through SBF. The various experiments performed show that S-PIT outperforms existing data structures in terms of memory consumption, content insertion time, average search time and false positive rate. [ABSTRACT FROM AUTHOR]

A '1-out-of-n' oblivious transfer (OT) protocol involves two participants: a sender, who provides a set of items as input, and a receiver. The protocol guarantees that the receiver gets exactly one of the items of the input set, while the sender is unable to determine which. Priced OT (POT) protocols further allow each item of the input set to be assigned a price in such a way that, after a proper execution of the protocol, the receiver gets the requested item if and only if the corresponding sum of money has been paid. In this paper, we present a construction which takes a '1-out-of-n' OT protocol and transform it into a POT one. Moreover, the resulting system is unlinkable in the sense that the sender is unable to determine whether two executions of the protocol were run by the same receiver or not. When compared to existing unlinkable POT protocols, our construction offers a lower conceptual complexity as it does not involve the use of zero-knowledge proofs. [ABSTRACT FROM AUTHOR]

This paper proposes a general construction method for structural distinguishers, based on the recently proposed 'multiple-of- n property' of a subspace trail. The multiple-of- n property refers to the fact that by appropriate choices of difference for a number of input pairs, it is possible to make sure that the number of times that the difference of the resulting output pairs lie in a particular subspace is always a multiple of n. We weaken the condition for constructing the structural distinguisher from the exact subspace trail to the general subspace trail and this general condition is applied to the SPN cryptographic algorithm, allowing a six-round structural distinguisher to be obtained for the first time. In this way, the best six-round Midori-64 distinguisher is constructed, with its data complexity being identical to Midori's five-round structural distinguisher. We also applied this new condition to the SKINNY algorithm, extending its structural distinguisher from five to six rounds. [ABSTRACT FROM AUTHOR]

Off-chain payment channel network is an effective solution to deal with low throughput and high load on the blockchain. However, the quality of the routing scheme will directly affect the performance of the off-chain network system. Existing routing schemes cannot select an appropriate transaction path according to the actual needs of user nodes. And they may also lead to network congestion, channel imbalance and node centralization. This paper proposes a new balanced routing selection scheme based on weight calculation called BRBW, which builds a weight model by Analytic Hierarchy Process. BRBW comprehensively considers the channel capacity, handling fee and path length to improve transaction success rate, reduce channel congestion and maintain the long-term sustainability of the payment channel. It uses a modified maximum flow algorithm to find paths with sufficient capacity and selects the transaction path for a large payment through linear programming. Finally, we do some experiments and compare with state-of-the-art approaches in the test environment. Simulation results show that under the same network environment, the payment success rate of the BRBW is above 75%, and the channel utilization rate is about 10% higher than other schemes. [ABSTRACT FROM AUTHOR]

SipHash is a family of ARX-based MAC algorithms optimized for short inputs. So far, a lot of implementations and applications for SipHash have been proposed, whereas the cryptanalysis of SipHash still lags behind. In this paper, we study the property of truncated differential in reduced-round SipHash. By exhaustively testing all kinds of 1-bit input differences, we find out the greatest differential biases from corresponding output bits through 3 or 4 SipRounds. Making use of these results, we construct distinguishers for SipHash-2-1 and SipHash-2-2 with practical complexities of |$2^{12}$| and |$2^{36}$|⁠ , respectively. However, one limitation of the latter is that it begins with 1-bit input differences on the most significant message bit, which means it can only work when neglecting the padding rules of SipHash. Furthermore, we reveal the relations between the value of output bias and the difference after the first modular addition step, which is directly determined by corresponding key bits. Based on these relations, we propose a key recovery method for SipHash-2-1 that can obtain a significantly nonuniform distribution of the 128-bit secret key. It is summarized that about |$97\%$| of random keys can be fully recovered under this method within a complexity of |$2^{83}$|⁠. [ABSTRACT FROM AUTHOR]

With the development of information networks, the entities from different network domains interact with each other more and more frequently. Therefore, identity management and authentication are essential in cross-domain setting. The traditional Public Key Infrastructure (PKI) architecture has some problems, including single point of failure, inefficient certificate revocation status management and also lack of privacy protection, which cannot meet the demand of cross-domain identity authentication. Blockchain is suitable for multi-participant collaboration in multi-trust domain scenarios. In this paper, a cross-domain certificate management scheme CD-BCM based on the consortium blockchain is proposed. For the issue of Certificate Authority's single point of failure, we design a multi-signature algorithm. In addition, we propose a unified structure for batch certificates verification and conversion, which improve the efficiency of erroneous certificate identification. Finally, by comparing with current related schemes, our scheme achieves good functionality and scalability in the scenario of cross-domain certificate management. [ABSTRACT FROM AUTHOR]

Cyber Range is an experimental platform based on virtualization technology to construct a controlled simulation environment, providing a real-world simulation environment for cybersecurity personnel to conduct various practical exercises. The problem is that generating a virtual environment satisfying the requirements is labor-intensive and time-consuming. To resolve the above problem, this paper proposes a system to automate the deployment of a cyber range. In our method, the first step is to collect virtual machines (VMs) related to cybersecurity and extract relevant features. Then, machine learning is used to classify VMs to reduce the cost of manual VMs selection. Lastly, leveraging the popular OpenStack cloud platform as the deployment platform enhances the applicability of the cyber range. When it comes time to deploy a virtual environment, the instructor only needs to provide some brief description information of a virtual environment. Then, the system will automatically parse the description file to complete the automated deployment of the virtual environment. This system has been successfully applied to the cyber range of Sichuan University for daily teaching tasks. [ABSTRACT FROM AUTHOR]

With rapid development of next-generation mobile networks and communications (5G networks), group-oriented applications in resource-constrained smart environments (RSEs), such as smart homes and smart classrooms, have attracted great attentions. Due to the insecure communications between resource-constrained devices, secure group communications in RSE toward 5G face many challenges. In RSE toward 5G, lightweight communications and low computational overheads are crucial. Besides, the private tokens used to generate the group key are expected to be reused multiple times. However, the conventional frameworks for secure group communications cannot meet these requirements. A practical construction of extremely lightweight constant-round membership authenticated group key establishment framework is proposed in this paper for RSE toward 5G, which not only implements identity authentication among the members and group key establishment but also ensures extremely lightweight computation and communication costs by each group member. In our proposed scheme, the increase in the number of group members will not lead to a linear or logarithmic increase in the communication and calculation costs at the member side. Our framework also resists external and internal attacks and meets all the desirable security features. In this framework, the privacy of tokens can be well protected, so that they can be reused for multiple times. Therefore, our scheme significantly reduces the costs of communication and calculation, and it is more efficient compared with the related schemes in the literature. This proposal is fairly suitable for lightweight membership authentication and group key establishment in RSE toward 5G. [ABSTRACT FROM AUTHOR]

This paper completes analysis of the worst-case running time of |$ {\tt{Heapsort}}$| measured by the number of comparisons of keys performed while sorting. A derivation of an exact formula for the maximum number of comparisons of keys performed by |$ {\tt{Heapsort}} $| on any array of size |$ N \geq 2 $| is presented. It is equal to $$ \begin{align}& \nonumber 2(N-1)\lceil \lg N \rceil - 2 ^{\lceil \lg N \rceil +1} - 2 s_2(N) - e_2(N) + \min (\lceil \lg N \rceil, 3) + 5 + c, \end{align} $$ where |$s_2(N)$| is the sum of digits of the binary representation of |$N$|⁠ , |$e_2(N)$| is the exponent of |$2$| in the |$N$| 's prime factorization and |$ c $| is a binary function on the set of positive integers defined by $$ \begin{align}& \nonumber c = \left\{ \begin{array}{@{}ll} 1 \text{ if} \; N \leq 2 ^{\lceil \lg N \rceil} - 4 \\[4pt] 0 \text{ otherwise}. \end{array} \right. \end{align} $$ The above formula allows for deciding, in |$ O(N \log N) $| time, if any given |$ N $| -element array is a worst-case array for |$ {\tt{Heapsort}} $|⁠. Its proof yields an algorithm for construction, in |$ O(N \log N) $| time, of worst-case arrays of arbitrary sizes |$ N \geq 2 $| for |$ {\tt{Heapsort}} $|⁠. [ABSTRACT FROM AUTHOR]

In this study, we construct a smoothing term structure, which is an essential part of the energy function in binocular matching. However, the existing energy models are mainly deterministic, which cannot adapt to processing low-quality images, especially when there exists a large proportion of vague areas. In order to perform better in processing these low-quality images, in this paper, we construct the smoothing term based on a fuzzy model, which includes fuzzy segmentation, the fuzzy network between the superpixels and the fuzzy relationship between the pixels. These can be compatible with the uncertainty in the image. In addition, to explain the rationality of the calculation of the degree of correlation between superpixels and further elaborate on the property of these degrees between each superpixel, we propose five corresponding theorems with proofs. After we solve the energy model combined with our proposed smoothing term, we compare our disparity results with the corresponding deterministic model and several state-of-the-art algorithms in the experiment. The results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper aims to improve the overall task processing time of mobile real-time Internet of Things (IoT) applications in fog-cloud computing, considering the various resource and security requirements along with the time constraints of the task. Fog computing extends the cloud resources to serve the IoT devices at the network edge. In such a scenario, deciding whether the tasks should be processed at the fog layer or submitted to the cloud is critical. Moreover, for real-time applications, the mobility of IoT devices and the limited bandwidth available at the edge devices endanger the low processing time of the task. Besides, the security demands of some IoT applications (i.e. healthcare) require processing the tasks by specific fog or cloud servers to assure confidentiality of information, which may also delay the task processing time. Therefore, we first address the mobility issue by proposing three different algorithms that work on allocating the mobile IoT device to the appropriate edge device (i.e. fog gateway), considering the distance and bandwidth load factors. Then, we offer a novel task scheduling algorithm that uses fuzzy logic to optimize the distribution of tasks between the fog and cloud layers, considering the task security requirements. The algorithm selects the proper processing unit to execute the task in the fog layer by exploiting the task demands (i.e. computation, storage, bandwidth, security) and deadline. Results demonstrate that considering the factors of distance and available bandwidth load while allocating the IoT device to the fog gateway improves the task processing time better than adopting one aspect. Results also show that our proposed scheduling algorithm outperforms other existing algorithms regarding makespan, turnaround time, success ratio and processing time metrics. [ABSTRACT FROM AUTHOR]

Maximizing influences in complex networks is a practically important but computationally challenging task for social network analysis, due to its nondeterministic polynomial time (NP)-hard nature. Most current approximation or heuristic methods either require tremendous human design efforts or achieve unsatisfying balances between effectiveness and efficiency. Recent machine learning attempts only focus on speed but lack performance enhancement. In this paper, different from previous attempts, we propose an effective deep reinforcement learning model that achieves superior performances over traditional best influence maximization algorithms. Specifically, we design an end-to-end learning framework that combines graph neural network as the encoder and reinforcement learning as the decoder , named DREIM. Through extensive training on small synthetic graphs, DREIM outperforms the state-of-the-art baseline methods on very large synthetic and real-world networks on solution quality, and we also empirically show its linear scalability with regard to the network size, which demonstrates its superiority in solving this problem. [ABSTRACT FROM AUTHOR]

The lattice-based cryptographic accumulators, which enable short zero-knowledge arguments of membership, have numerous applications in post-quantum privacy-preserving protocols. However, most efficient quantum-safe zero-knowledge arguments are PCP-based systems and rely on non-falsifiable assumptions. For non-PCP-based constructions using the state-of-the-art techniques on compressing lattice-based zero-knowledge proofs, the concrete size of the resulting proof for accumulators with |$2^{32}$| members is at least 500 KB. In this paper, we propose a compact non-PCP zero-knowledge proof for the lattice-based Merkle-tree, which leads to an efficient post-quantum cryptographic accumulator. The complexity of our construction is logarithmic in |$l\cdot n_{s}$|⁠ , where |$l$| and |$n_{s}$| denote the depth of the underlying Merkle-tree and the size of a node, respectively, and the concrete size is only |$143.7\ $| KB when |$l=32$|⁠. In particular, we provide an improved lattice-based Bulletproof with efficient knowledge extraction, which allows large challenge space but small soundness slack. Furthermore, the amortized technique can be applied to the Bulletproof without breaking the knowledge soundness due to our improved knowledge extraction. As a direct application, we present a practical lattice-based ring signature, which can achieve logarithmical signing/verifying computational complexity with the number of the ring, while the state-of-the-art constructions (CRYPTO 21) have linear computational complexity. [ABSTRACT FROM AUTHOR]

Reliability evaluation of multiprocessor systems is of significant importance in the design and maintenance of multiprocessor systems. Based on edge-connectivity, more refined quantitative indicators for the reliability of multiprocessor systems have been introduced. The extra edge-connectivity and the component edge-connectivity, as two important parameters to evaluate the robustness of multiprocessor systems, are explored extensively. In this paper, we determine the |$h$| -extra edge-connectivity and the |$(g+1)$| -component edge-connectivity of the balanced complete |$t$| -partite graph |$K_{r}^{t}$| for |$t, r\geq 2$|⁠ , where |$1\leq h \leq \lfloor tr/2 \rfloor$| and |$2\leq g \leq tr-1$|⁠. [ABSTRACT FROM AUTHOR]

Whether a block cipher can resist impossible differential attack is an important basis to evaluate the security of a block cipher. However, the length of impossible differentials is important for the security evaluation of block ciphers. Most of the previous studies are based on structural cryptanalysis to find the impossible differential, and the structural cryptanalysis covers a lot of specific cryptanalytic vectors which are independent of the nonlinear S-boxes. In this paper, we study the maximum length of the impossible differential of an Advanced Encryption Standard-like cipher in the setting with the details of S-boxes. Inspired by the 'Divide-and-Conquer' technique, we propose a new technique called Reduced Block , which combines the details of the S-box. With this tool, the maximum length of impossible differentials can be proven under reasonable assumptions. As applications, we use this tool on uBlock and Midori. Consequently, we prove that for uBlock-128, uBlock-256 and Midori-64, there are no impossible five-round, six-round and seven-round differentials with one active input nibble and one active output nibble, even when considering the details of S-boxes. Furthermore, we reveal some properties of the uBlock S-box and linear layer and demonstrate theoretically that there are no impossible differentials longer than four rounds for uBlock-128 under the assumption that the round keys are independent and uniformly random. This study might provide some insight into the bounds of the length of impossible differentials. [ABSTRACT FROM AUTHOR]

Falling is one of the major health concerns, and its early detection is very important. The goal of this study is an early prediction of impending falls using wearable sensors data. The SisFall data set has been used along with two deep learning models (CNN and a combination model named Conv_Lstm). Also, a dynamic sampling method is offered to improve the accuracy of the models by increasing the equilibrium rate between the samples of the majority and minority classes. To fulfill the main idea of this paper, we present a future prediction strategy. Then, by defining a time variable 'T', the system replaces and labels the state of the next T s instead of considering the current state only. This leads to predicting falling states at the beginning moments of balance disturbance. The results of the experiments show that the Conv_Lstm model was able to predict the fall in 78% of cases and an average of 340 ms before the accident. Also, for the Sensitivity criterion, a value of 95.18% has been obtained. A post-processing module based on the median filter was implemented, which could increase the accuracy of predictions to 95%. [ABSTRACT FROM AUTHOR]

Cloud medical treatment provides real-time data sharing in a cost-effective method, making it more practical to create, collect and manage vast amounts of personal health records (PHR) of patients. However, health information is considered highly sensitive. How to securely store and dynamically process massive patients' PHR data in a public cloud environment has become one of the most important challenges. Therefore, we introduce a novel solution to the problems of privacy exposure, data security and flexible access of storage modules in medical systems. In this paper, we present a privacy-preserving certificateless broadcast encryption with authorization for the PHR system, which is the best approach to effectively solve the above problems and avoid key escrow. In our work, users (patients) outsource their encrypted data to the cloud server and reallocate data accessing rights of recipients through an authorization set, sharing with a group of authorized receivers (doctors) in a secure and efficient manner. In addition, it is shown to be capable of achieving both plaintext confidentiality and receiver anonymity under the random oracle model. Moreover, the experimental evaluation shows that the proposed scheme enjoys low computational and communication overhead, indicating the feasibility and practicality of the scheme. [ABSTRACT FROM AUTHOR]

Graph products have been extensively applied to model complex networks with striking properties observed in real-world complex systems. In this paper, we study the hitting times for random walks on a class of graphs generated iteratively by edge corona product. We first derive recursive solutions to the eigenvalues and eigenvectors of the normalized adjacency matrix associated with the graphs. Based on these results, we further obtain interesting quantities about hitting times of random walks, providing iterative formulas for two-node hitting time, as well as closed-form expressions for the Kemeny's constant defined as a weighted average of hitting times over all node pairs, as well as the arithmetic mean of hitting times of all pairs of nodes. [ABSTRACT FROM AUTHOR]

In CRYPTO 2019, Gohr made a pioneering attempt and successfully applied deep learning to the differential cryptanalysis against NSA block cipher Speck 32/64, achieving higher accuracy than the pure differential distinguishers. By its very nature, mining effective features in data plays a crucial role in data-driven deep learning. In this paper, in addition to considering the integrity of the information from the training data of the ciphertext pair, domain knowledge about the structure of differential cryptanalysis is also considered into the training process of deep learning to improve the performance. Meanwhile, taking the performance of the differential-neural distinguisher of Simon 32/64 as an entry point, we investigate the impact of input difference on the performance of the hybrid distinguishers to choose the proper input difference. Eventually, we improve the accuracy of the neural distinguishers of Simon 32/64, Simon 64/128, Simeck 32/64 and Simeck 64/128. We also obtain related-key differential-based neural distinguishers on round-reduced versions of Simon 32/64, Simon 64/128, Simeck 32/64 and Simeck 64/128 for the first time. [ABSTRACT FROM AUTHOR]

The undirected graph, exchanged hypercube |$EH(s,t)$|⁠ , is a variant of hypercube proposed by Loh et al. It is obtained by removing some links from |$(s+t+1)$| -dimensional hypercube. It retains many excellent properties, so many people have studied its reliability and fault tolerance. In this paper, combining the structure connectivity and substructure connectivity of graphs proposed not long ago, we obtain its |$P_k$| -path, |$C_{2l}$| -cycle and |$K_{1,r}$| -star structure connectivity and substructure connectivity where |$2\le k,r\le s-1\le t-1$| and |$6\le 2l\le s-1\le t-1$|⁠ ; we also establish |$\kappa ^s(EH(s,t);C_4)$| for |$5\le s\le t$| and the upper bound of |$\kappa (EH(s,t);C_4)$| for |$4\le s\le t$|⁠. [ABSTRACT FROM AUTHOR]

Deep neural network model extraction attack is the process of retraining a surrogate model based on the outputs of a target model with a given set of inputs. Such attacks are hard to defend for the sake of model owners' interest. Recently, some work propose model watermarking scheme for image processing networks, which is able to prove the intellectual property of deep models even after the model extraction attack. This scheme makes sure that, once the target model (an image processing network) is watermarked, we can extract the watermark from the output of the surrogate model. In this paper, we propose a new model extraction attack scheme to fight against the latest method. Instead of directly using the output images of a target model, we propose to use their reconstructed versions for model retraining, where an asymmetrical UNet is proposed for image reconstruction. To thoroughly remove the watermarking traces, we propose and incorporate a referenced subspace attention module in the asymmetrical UNet, which removes the watermark by projecting the outputs of the target model into the subspaces of the reference image. Various experiments demonstrate the effectiveness of our attack. [ABSTRACT FROM AUTHOR]

Pattern detection for revealing the patterns of users' behavior is an important analysis-assisting tool toward the understanding and prediction of their attitudes, manners, activities and habits. In this paper, two novel query operators applied to transactional data are introduced to ease the query processing, strengthening query capabilities and revealing valuable patterns for data analysis and mining. The operators are named as PeriodicTransactions and SimilarTransactions, and as their names imply, they measure periodicity and similarity, respectively, in a set of transactions. The operators are formally defined and the corresponding algorithms are also provided. To show the expediency of the operators, the proposed algorithms are implemented and a set of experiments were conducted with real data from the Ethereum blockchain. The results show the feasibility and usefulness of the proposal for identifying these patterns that help to understand user behavior and reveal a rich interaction between senders and recipients, where periodic and similar transactions occur. [ABSTRACT FROM AUTHOR]

Continuous data scale growth increases energy consumption and operating cost that cannot be ignored in cloud storage systems. Previous studies have shown that analyzing the characteristics of I/O access and mining data features is effective for reasonable data distribution in storage systems. The granularity and criterion of classification are the key factors in determining the data distribution. To decrease energy consumption and operating cost, this paper puts forward a fine-grained framework of the climatic-season-based energy-aware in cloud storage system called CSEA. The framework concludes the following three aspects: (i) data feature mining. CSEA discovers potential data features by analyzing data access to provide help with data classification. (ii) K-means clustering algorithm. CSEA uses an unsupervised data classification algorithm in machine learning to divide data into categories based on seasonal characteristics by gathering real I/O access. (iii) data distribution of fine-grained. On the basis of seasonal features, CSEA fuses regional features to further refine the data distribution granularity to save on energy consumption and operating cost. Simulation experiments using extended CloudSimDisk and the constructed mathematical models indicate that CSEA reduces the energy consumption and operating cost compared with the single data classification standard and coarse-grained data distribution. [ABSTRACT FROM AUTHOR]

Copy-move is a common image forgery operation, which copies and moves a block of an image from one position to another place. Image hashing refers to extracting a unique number sequence from the image by using various image features. In practical application, image hashing is used to replace the image itself, which effectively reduces the cost of image storage and computational complexity. In this paper, we propose a novel image hash extraction scheme: constructing image hashing by combining local feature based on Canny operator and global feature based on tensor. In addition, instead of using the traditional correlation coefficient or Hamming distance, a novel method is proposed to calculate the hash distances. A large number of experiments have proved that our image hashing can achieve a better balance between robustness and discrimination with a shorter hash length. What's more, we can directly locate the forgery areas from the hashing for copy-move forged images. [ABSTRACT FROM AUTHOR]

Community structure over social media (SM) is the collaborative group of globally spread users with identical characteristics and ideologies. The collective features of SM are inherent with both the implicit and explicit nature of end-users. This paper presents an analytical and methodological community detection framework to bind passive users' implicit and explicit nature after scrutinizing graphical data to identify seed nodes and communities. Moreover, this work provides the concept of the unsupervised machine learning approach over the graphical perspective of SM to identify the trade-off between similarity of nodes attributes and density of connections for social theories. Subsequently, this paper evaluates a comprehensive analysis of the benchmark community detection algorithm (CDA) Label Propagation Algorithm (LPA), Clique Percolation Method (CPM), Democratic Estimate of the Modular Organization of a Network (DEMON) and Non-Negative Matrix Factorization (NMF). The evaluation has been carried out over modularity and normalized mutual information of resultant structured community on six real-time SM data set. The performance of benchmark CDAs is significantly increased after incorporating social theories. NMF, DEMON, CPM and LPA gained the highest improvement over Zachary's Karate Club data sets, i.e. approximate 26.91%, 21.68%, 18.79%, 19.96%, respectively. [ABSTRACT FROM AUTHOR]

An alternator is a network of concurrent nodes, which satisfies the following conditions: (i) if one node enters its critical section, no neighboring nodes could enter their critical sections at the same computing phase; (ii) along any infinite computing phases, each node enters its critical section infinitely often. An alternator is said to exhibit strong fairness if condition (ii) is changed to: between any two consecutive critical steps of a node, all neighbors enter their critical sections exactly once. In this paper, we propose a state-optimal alternator algorithm for uniform rings with n nodes, where n is any positive integer greater than one. The proposed algorithm operates correctly for synchronous mode. Every node maintains only a two-state variable. The proposed algorithm has the snap property that the system always satisfies condition (i) of the alternator even if some transient fault occurs. The stabilization time of the proposed alternator algorithm is O(n) phases. After stabilization, the system satisfies the strong fairness property and achieves the maximal performance. We say that an alternator achieves the maximal performance if a node is allowed to enter its critical section when both its neighbors do not enter their critical sections. In the worst case, the proposed algorithm allows each node to enter its critical section every three phases. [ABSTRACT FROM AUTHOR]

A multirepeat in a string is a substring (factor) that appears a predefined number of times. A multirepeat is maximal if it cannot be extended either to the right or to the left and produce a multirepeat. In this paper, we present algorithms for two different versions of the problem of finding maximal multirepeats in a set of strings. In the case of arbitrary gaps, we propose an algorithm with O(σN2n + α) time complexity. When the gap is bounded in a small range c, we propose an algorithm with O((c2 + σ2)mN2n log(Nn) + α) time complexity. Here, N is the number of strings, n the mean length of each string, m the multiplicity of the multirepeat and α the number of reported occurrences. Our results extend previous work by considering sets of strings as well as by generalizing pairs to multirepeats. [ABSTRACT FROM AUTHOR]

The presence of a schema for eXtensible Markup Language (XML) documents has numerous advantages. Unfortunately, many XML documents in practice are not accompanied by a (valid) schema. Therefore, it is essential to devise algorithms to infer schemas from XML documents, where the fundamental task is learning regular expressions. In this paper, we focus on the learning of disjunctive multiplicity expressions (DMEs), a subclass of regular expressions that are particularly suitable to specify unordered models and have been used as the foundation of the schemas for unordered XML. Previous work for learning DME lacks inference algorithms that support positive and negative examples. Further, presently there has been no algorithm can learn DMEs extended with numeric occurrences. We address these challenges in the present paper and first propose a novel algorithm to learn DMEs from positive and negative examples by using genetic algorithms and parallel techniques. Then we extend DMEs to disjunctive generalized multiplicity expressions (DGMEs), which allow numeric occurrences and develop an algorithm to learn DGMEs from positive and negative examples. Finally, experimental results show that with only positive examples, our algorithm can generate a DME with an acceptable learning time, which can accept all positive examples, and when given both positive and negative examples, we can learn DMEs or DGMEs with high accuracy. [ABSTRACT FROM AUTHOR]

In current century, big number of people adopting social media very fastly because social media is very reliable, fast and easy to use. So, many big industries adopting social media also, like Facebook to analyze their customers' reviews, analyze customers' emotions and to understand their behavior, which is very helpful in providing facilities to customers. Nowadays bulk of data available is on social media. To increase competitive benefits and assess competitive environment of industries, companies need to analyze social media data as well as customer generated data. To guide large industries in understanding how they may use social media to understand customer emotion and use social media marketing approaches into their marketing strategies to get customer engagement. This paper describes in-depth analysis of pizza industry of United Kingdom to understand their customers' emotions and get data for analysis from Facebook. We are using January 2018–December 2018 data of five largest pizza chains in United Kingdom: Papa John, Prezzo, Pizza Express, Pizza Hut and Dominos. The results disclose the power of sentimental analysis and social media competitive analysis will put positive impact on business value. At the end of this paper we will provide some recommendations to enhance marketing strategies. [ABSTRACT FROM AUTHOR]

Global efforts such as the National Institute of Standards and Technology (NIST)'s post-quantum standardization center on cryptographic primitives like public-key encryption and signature schemes that are secure even in the presence of quantum adversaries. In addition, one must also consider efficient certificate management as new technologies like the Internet of Things and 5G wireless networks rely on them. For example, the IEEE Standard for vehicle-to-vehicle communication depends on implicit certificates. However, the only efficient construction available is over elliptic curves, and hence not quantum-secure. This paper investigates approaches to construct implicit certificate schemes from lattices, employing the NIST Round 3 signature schemes Dilithium and Falcon. We consider emulation of the existing implicit certificate scheme and proceed to more innovative techniques like combining the two schemes or pairing them with encryption. Unfortunately, we encounter problems with each design, due to recurring causes like conflicting secret key and signature sizes, unique sampler requirements and the rigidity of the parameter sets. By explaining each of these issues, this paper will hopefully spark ideas for more successful constructions. [ABSTRACT FROM AUTHOR]

A self-stabilizing distributed algorithm is guaranteed eventually to reach and stay at a legitimate configuration regardless of the initial configuration of a distributed system. In this paper, we propose the generalized dominating set problem, which is a generalization of the dominating set and |$k$| -redundant dominating set problems. In the generalized dominating set we propose in this paper, each node |$P_{i}$| is given its set of domination wish sets, and a generalized dominating set is a set of nodes such that each node is contained in the set or has a wish set in which all its members are in the set. We propose a self-stabilizing distributed algorithm for finding a minimal generalized dominating set in an arbitrary network under the unfair distributed daemon. The proposed algorithm converges in |$O(n^{3}m)$| steps and |$O(n)$| rounds, where |$n$| (resp. |$m$|⁠) is the number of nodes (resp. edges). Furthermore, it has the safe convergence property with safe convergence time in |$O(1)$| rounds. The space complexity of the proposed algorithm is |$O(\Delta \log n)$| bits per node, where |$\Delta $| is the maximum degree of nodes. [ABSTRACT FROM AUTHOR]

Two-party signing can be used to provide a high level of key protection especially in the blockchain systems where the safety of money relies on the safety of the signing key. With a two-party signing protocol, the signing key is distributed among two devices, thus the funds are safe as long as one device remains uncorrupted. In this paper, we study the two-party signing protocols for all ISO/IEC signature standards. The mechanisms based on elliptic curve discrete logarithm in ISO/IEC can be divided into three types: Schnorr-type, Elliptic Curve Digital Signature Algorithm (ECDSA)-type and SM2-type. There have already been efficient two-party protocols based on Schnorr signature scheme which can be easily extended into all Schnorr-type standards. However, it is particularly hard to construct efficient distributed SM2-type and ECDSA-type protocols due to their nonlinear signing equations. In this paper, we present the first secure and efficient two-party protocol over SM2-type signature standard. We prove its security in the generic group model. We then construct a more efficient two-party ECDSA protocol that is secure in the generic group model and outperforms all previous works. [ABSTRACT FROM AUTHOR]

'Laser Beam Welding (LBW) is a welding technique used to join pieces of metal or thermoplastics with the aid of laser'. The beam offers a concerted heat source, which enabled higher, deeper welds and narrower welding rates. The procedure is commonly exploited in higher volume appliances using mechanization. It is dependent on penetration or keyhole mode welding. This paper intends to design a novel prediction model on LBW using the Optimized Neural Network (NN) framework. The input to the optimized NN is the welding properties like 'Laser power, welding speed, offset, shielding gas, flow/pressure, focal distance and frequency (where power, speed and offset gets varied)' that directly predict the hardness and tensile strength of welds since the NN is already trained with the provided data. In order to make the prediction model more accurate, this paper aims to train the NN using a new improved Trial Integer-based Whale Optimization Algorithm (TI-WOA) via updating the weight. Finally, the betterment of the suggested scheme is validated with respect to error analysis. Accordingly, from the analysis, it is observed that the proposed methods are 50%, 13.33%, 6.67% and 4% better than ANN-BP, RBF, ANN-GA and NN-WOA models, respectively, at 70th learning percentage. [ABSTRACT FROM AUTHOR]

The Block Tree is a data structure for representing repetitive sequences in compressed space, which reaches space comparable with that of Lempel–Ziv compression while retaining fast direct access to any position in the sequence. In this paper, we generalize Block Trees to two dimensions, in order to exploit repetitive patterns in the representation of images, matrices and other kinds of bidimensional data. We demonstrate the practicality of the two-dimensional Block Trees (2D-BTs) in representing the adjacency matrices of Web graphs, and raster images in GIS applications. For this purpose, we integrate our 2D-BT with the |$k^2$| -tree—an efficient structure that exploits clustering and sparseness to compress adjacency matrices—so that it also exploits repetitive patterns. Our experiments show that this structure uses 60–80% of the space of the original |$k^2$| -tree, while being 30% faster to three times slower when accessing cells. [ABSTRACT FROM AUTHOR]

In this paper, initially, message intensities are used to create two encoding tables: an index and a code sequence. The table is then updated with |$n$| secret data encoded into it. If the code sequence of the second encoding table matches the code sequence of the preceding or succeeding half of the two encoding tables, the second encoding table is folded. The folding is done when maximum intensity occurs in the utmost succeeding part of the two encoding tables. Finally, using the combination orientation approach, the encoded indices are embedded in the stego images. In the extraction phase, using the two stego images and the encoding with the combination orientation method, the encoded indices are obtained. The decoding is performed to extract the secret message as well as the cover image and there is no occurrence of overflow and underflow problems. It has a high visual quality and a high embedding capacity. The proposed technique achieved a maximum |$\mathrm{PSNR}$| of |$49.49$| dB and a |$648\ 242$| -bit embedding capacity. When compared to traditional techniques, the experimental results show that the proposed method outperforms them. [ABSTRACT FROM AUTHOR]

The National Institute of Standards and Technology (NIST) has been working on standardization of post-quantum cryptography and is approaching the end of round-3 evaluation of algorithms. Key reuse security evaluation is an important part of algorithm evaluation. In order to evaluate the key reuse security of candidate IND-CPA PKEs, at Eurocrypt'19, B |$\breve{\text{a}}$| etu et al. proposed a classical key recovery under plaintext checking attack (KR-PCA) which can recover the reused secret keys by querying an oracle thousands of times. However, the method does not work for cryptosystems which shorten ciphertexts by rounding off the low bits, such as round-3 finalists Kyber and Saber. Subsequently, Dumittan and Vaudenay (ACNS'20) and Qin et al. (ASIACRYPT'21) came up with new effective methods, which require carefully constructed queries. In this paper, we propose an automatic method to recover the reused secret keys of IND-CPA PKEs in Kyber and Saber. Instead of constructing queries carefully, our method uses automated search combined with an optimized bruteforce. The effect and cost of the method depend on the specific parameters. In particular, we can recover the secret keys after thousands of queries in all parameter sets, which is comparable with the current best result. [ABSTRACT FROM AUTHOR]

Simultaneous compression and encryption of images using a novel chaotic map is proposed in this paper. Both compression and encryption of images are carried out by the theory of compressive sensing (CS). A novel chaotic map with a high degree of chaos that is extremely sensitive to its initial parameters is proposed. A measurement matrix for the CS framework is designed based on the proposed map. The compression and recovery of images with different compression/sampling ratios are tested using the designed measurement matrix. Encryption of the compressed data is carried out using the proposed chaotic map and a novel user name-based encryption scheme. The entire encryption/decryption process proposed is completely dependent on the sequence obtained from the proposed chaotic map as well as the authorized user name. Thus, by this process, only authorized people with a valid user name will be able to decrypt the encrypted data and recover the actual underlying image. Simulation results on the proposed scheme with different images show that the average peak signal-to-noise ratio and structural similarity index values of about 32 dB and 0.861 are obtained for a sampling ratio of 0.5. Validations on the proposed map and the encryption process that were carried out using various standard tests prove the efficiency of the system in successfully compressing and encrypting the images. Also, the qualitative evaluation of the proposed compression–encryption process outperforms some of the existing works in the literature. [ABSTRACT FROM AUTHOR]

Social networks have an important role in the distribution of ideas. With the rapid development of the social networks, identifying the influential nodes provides a chance to turn the new potential of global information spread into reality. The measurement of the spreading capabilities of nodes is an attractive challenge in social networks analysis. In this paper, a novel method is proposed to identify the influential nodes in complex networks. The proposed method determines the spreading capability of a node based on its local and global positions. The degree centrality is improved by the Shannon entropy to measure the local influence of nodes. The k-shell method is improved by the clustering coefficient to measure the global influence of nodes. To rank the importance of nodes, the entropy weighting method is used to calculate the weight for the local and global influences. The Vlsekriterijumska Optimizacija I Kompromisno Resenje method is used to integrate the local and global influences of a node and obtain its importance. The experiments are conducted on 13 real-world networks to evaluate the performance of the proposed method. The experimental results show that the proposed method is more powerful and accurate to identify influential nodes than other methods. [ABSTRACT FROM AUTHOR]

To guarantee the security of symmetric key schemes against quantum adversary, developing quantum cryptanalytic techniques becomes a major worldwide challenge in the post-quantum world. In this paper, we present a general framework of classical guess and determine attack on stream ciphers, and then convert it into quantum guess and determine attack. It shows that, for a given stream cipher with a key size of |$k$| bits and an internal state size of |$n$| bits, if a basic guess and determine attack with a time complexity below |$O ({{{2}^{{3k}/{2}}}}/{n})$| is available, there is a quantum guess and determine attack with multiple data that can recover all |$n$| internal state bits of the cipher with complexity below |$O ({{2^{k / 2}}})$|⁠. As applications, we present quantum guess and determine attacks on the SNOW-like stream ciphers. The results show that all of SNOW 1.0 with 128-bit key, SNOW 2.0 with 128-bit key and SOSEMANUK are insecure against quantum guess and determine attack. The resource requirements for implementing a quantum guess and determine attack on SNOW 3G are evaluated as a case study. To the best of our knowledge, this is the first time that the general quantum guess and determine attack is formally proposed and applied to the SNOW-like stream ciphers. [ABSTRACT FROM AUTHOR]

Identification and extraction of Tactics, Techniques and Procedures (TTPs) for Cyber Threat Intelligence (CTI) restore the full picture of cyber attacks and guide the analysts to assess the system risk. Existing frameworks can hardly provide uniform and complete processing mechanisms for TTPs information extraction without adequate knowledge background. A multi-instance learning approach named SeqMask is proposed in this paper as a solution. SeqMask extracts behavior keywords from CTI evaluated by the semantic impact, and predicts TTPs labels by conditional probabilities. Still, the framework has two mechanisms to determine the validity of keywords. One using expert experience verification. The other verifies the distortion of the classification effect by blocking existing keywords. In the experiments, SeqMask reached 86.07% and 73.99% in F1 scores for TTPs classifications. For the top 20% of keywords, the expert approval rating is 92.20%, where the average repetition of keywords whose scores between 100% and 90% is 60.02%. Particularly, when the top 65% of the keywords were blocked, the F1 decreased to about 50%; when removing the top 50%, the F1 was under 31%. Further, we also validate the possibility of extracting TTPs from full-size CTI and malware whose F1 are improved by 2.16% and 0.81%. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction is valuable for satisfying citizens' travel needs and alleviating urban traffic pressure. However, it is highly challenging due to the complexity of the urban geospatial structure and the highly nonlinear temporal and spatial dependence on human mobility. Most existing works proposed to rely on strict periods (e.g. daily and weekly) and separate the extraction of temporal and spatial features. Besides, most Recurrent Neural Network (RNN)-based models either fail to capture variations of spatial–temporal features in adjacent timestamps or ignore details of closeness. In this paper, we propose a M ulti-attention based H ybrid-convolution S patial-temporal R ecurrent N etwork (MHSRN) for region-based traffic flow prediction. In MHSRN, we leverage a hybrid-convolution module to capture both shifting features and rich information at the nearest timestamps, and we apply the downsampling procedure to reduce the computation of RNN-based model. Furthermore, we propose to adopt a space-aware multi-attention module to re-perceive global and local spatial–temporal features. We conduct extensive experiments based on three real-world datasets. The results show that the MHSRN outperforms other challenging baselines by approximately 0.2–8.1% in mean absolute error on all datasets. On datasets other than TaxiBJ, the MHSRN reduces the root mean square error by at least 2.8% compared with the RNN-based model. [ABSTRACT FROM AUTHOR]

Previous key node identification approaches assume that the transmission of information on a path always ends positively, which is not necessarily true. In this paper, we propose a new centrality index called Information Rank (IR for short) that associates each path with a score specifying the probability that such path successfully conveys a message. The IR method generates all the shortest paths of any arbitrary length coming out from a node |$u$| and defines the centrality of u as the sum of the scores of all the shortest paths exiting |$u$|⁠. The IR algorithm is more robust than other centrality indexes based on shortest paths because it uses alternative paths in its computation, and it is computationally efficient because it relies on a Beadth First Search-BFS to generate all shortest paths. We validated the IR algorithm on nine real networks and compared its ability to identify super-spreaders (i.e. nodes capable of spreading an infection in a real network better than others) with five popular centrality indices such as Degree, Betweenness, K-Shell, DynamicRank and PageRank. Experimental results highlight the clear superiority of IR over all considered competitors. [ABSTRACT FROM AUTHOR]

In this paper, a system is proposed which uses blockchain technology in healthcare. In this system, patients can access their health records anytime from anywhere. Moreover, the patients' health records are put into the blockchain anonymously. Whenever a patient visits a healthcare professional, the authorized entity filters patients' medical report out by eliminating the patients' sensitive information. Then, the filtered medical data are put into an off-chain database, while the address of the data is put into the blockchain with an assigned pseudo random identity of the patient. Thus, there are multi pseudo random identities for each patient. Unlike previous studies where the patients' identities/reports were linkable, in the proposed protocol the patients' identities are not linkable. The proposed system can also be used to show patients' health status to some entities when a pandemic happens (e.g. COVID-19). During the COVID-19 pandemic, the patients are required to show their series of vaccinations before they travel internationally/nationally or participate in some social events. To travel or join some events, the patient needs to show only a partial medical history to the security guard without leaking any private information. Furthermore, once the anonymous medical data are put into the off-chain database, the data can be used for data mining and machine learning. [ABSTRACT FROM AUTHOR]

Differential attack is one of the most important methods in cryptanalysis. When finding a high-probability differential trail, the effect of constant has long been ignored. In this paper, we focus on the effect of constants on the differential attack against |$\mu ^2$|⁠. |$\mu ^2$| is a newly proposed block cipher based on a Type-II generalized Feistel structure. Its 16-bit F function (denoted as F-box) is an ultra-lightweight permutation equipped with different constants. The designer applied the minimum number of active S-boxes to determine |$\mu ^2$| ' security margin in the design document. However, the F-boxes use different round constants in different rounds; the constants may lead to incompatibility of differential trails of F-boxes. Therefore, to provide a more precise differential attack on |$\mu ^2$|⁠ , we construct an model based on STP (Simple Theorem Prover) constraint solver to search for the valid differential trails with a more precise probability of |$\mu ^2$| for different starting rounds. Finally, the related-key differential trail covers one more round than the existing methods. Analyzing the effect of constants on the validity and the probability of the differential trail reminds the designers and the attackers to have a more comprehensive analysis of specific ciphers. [ABSTRACT FROM AUTHOR]

Small-state stream ciphers (SSCs) idea is based on using key bits not only in the initialization but also continuously in the keystream generation phase. A time-memory-data tradeoff (TMDTO) distinguishing attack was successfully applied against all SSCs in 2017 by Hamann et al. They suggested using not only key bits but also initial value (IV) bits continuously in the keystream generation phase to strengthen SSCs against TMDTO attacks. Then, Hamann and Krause proposed a construction based on using only IV bits continuously in the packet mode. They suggested an instantiation of an SSC and claimed that it is resistant to TMDTO attacks. We point out that accessing IV bits imposes an overhead on cryptosystems that might be unacceptable in some applications. More importantly, we show that the proposed SSC remains vulnerable to TMDTO attacks 1. To resolve this security threat, the current paper proposes constructions based on storing key or IV bits that are the first to provide full security against TMDTO attacks. Five constructions are proposed for different applications by considering efficiency. Designers can obtain each construction's minimum volatile state length according to the desirable keystream, key and IV lengths. [ABSTRACT FROM AUTHOR]

Desktop as a Service (DaaS) provides users with flexible, customizable and highly secure cloud based virtual desktop access. As the major carrier of execution results in DaaS, screen updates play an important role in users' quality of experience. In order to bring users the same feelings like manipulating a local device, timeliness and reliability should be balanced. However, a timely but unreliable transmission scheme (i.e. UDP) or a completely reliable transmission scheme (i.e. TCP) is inappropriate for such a transmission scenario, especially under a high-loss network. In this paper, we propose a Prediction and Network Coding based Transmission Scheme (PNCTS) for efficient screen updates delivery in DaaS. As an end-to-end partially reliable transmission scheme, it prioritizes different data obtained by partitioning screen updates and employs network coding and TFRC (TCP Friendly Rate Control) to compensate for data loss and adjust the sending rate of screen updates, respectively. To reduce the overhead of network coding, PNCTS uses a Hidden Markov Model to predict the reliability level of network and makes different encoding strategies for the data with different priorities. Simulation results show that PNCTS can improve display quality and instantaneous goodput effectively while maintaining end-to-end delay and jitter at a relatively low level under the static and time-varying network conditions. [ABSTRACT FROM AUTHOR]