The Boolean SATisfiability problem (SAT)-based automated search methods can directly describe logical operations such as AND, OR, NOT, XOR, and establish more efficient search models. In order to efficiently evaluate the ability of GRANULE cipher to resist impossible differential attacks, firstly, the SAT model described by the S-box differential property was optimized based on the S-box differential distribution table property. Then, the SAT model of bit-oriented impossible differential distinguisher was established for GRANULE cipher, and multiple 10-round impossible differential distinguishers of GRANULE cipher were obtained by solving the SAT model. Furthermore, an improved SAT automated verification method was given, by which the impossible differential distinguishers were verified. Finally, 16-round impossible differential attack was performed on GRANULE-64/80 cipher, where the impossible differential distinguisher was further extended forward 3-round and backward 3-round respectively. As a result, 80-bit master key was recovered with the time complexity of 251.8 16-round encryptions and the data complexity of 241.8 chosen-plaintexts. Compared with the suboptimal results for impossible differential cryptanalysis of the GRANULE cipher, the number of distinguisher rounds and key recovery attack rounds obtained are improved by 3 rounds, and the time complexity and data complexity are further reduced.
As one of the basic tasks of natural language processing, new word identification provides theoretical support for the establishment of Chinese dictionary and analysis of word sentiment tendency. However, the current new word identification methods do not consider the homophonic neologism identification, resulting in low precision of homophonic neologism identification. To solve this problem, a Chinese homophonic neologism discovery method based on Pinyin similarity was proposed, and the precision of homophonic neologism identification was improved by introducing the phonetic comparison of new and old words in this method. Firstly, the text was preprocessed, the Average Mutual Information (AMI) was calculated to determine the degree of internal cohesion of candidate words, and the improved branch entropy was used to determine the boundaries of candidate new words. Then, the retained words were transformed into Chinese Pinyin with similar pronunciations and compared to the Chinese Pinyin of the old words in the Chinese dictionary, and the most similar results of comparisons would be retained. Finally, if a comparison result exceeded the threshold, the new word in the result was taken as the homophonic neologism, and its corresponding word was taken as the original word. Experimental results on self built Weibo datasets show that compared with BNshCNs (Blended Numeric and symbolic homophony Chinese Neologisms) and DSSCNN (similarity computing model based on Dependency Syntax and Semantics), the proposed method has the precision, recall and F1 score improved by 0.51 and 5.27 percentage points, 2.91 and 6.31 percentage points, 1.75 and 5.81 percentage points respectively, indicating that the proposed method has better Chinese homophonic neologism identification effect.
The K-Means algorithms typically utilize Euclidean distance to calculate the similarity between data points when dealing with large-scale heterogeneous data. However, this method has problems of low efficiency and high computational complexity. Inspired by the significant advantage of Hamming distance in handling data similarity calculation, a Quantum K-Means Hamming (QKMH) algorithm was proposed to calculate similarity. First, the data was prepared and made into quantum state, and the quantum Hamming distance was used to calculate similarity between the points to be clustered and the K cluster centers. Then, the Grover’s minimum search algorithm was improved to find the cluster center closest to the points to be clustered. Finally, these steps were repeated until the designated number of iterations was reached or the clustering centers no longer changed. Based on the quantum simulation computing framework QisKit, the proposed algorithm was validated on the MNIST handwritten digit dataset and compared with various traditional and improved methods. Experimental results show that the F1 score of the QKMH algorithm is improved by 10 percentage points compared with that of the Manhattan distance-based quantum K-Means algorithm and by 4.6 percentage points compared with that of the latest optimized Euclidean distance-based quantum K-Means algorithm, and the time complexity of the QKMH algorithm is lower than those of the above comparison algorithms.
With the continuous development of big data and cloud computing technology, cloud platforms have become the first choice for massive data storage, and user data privacy and security has become one of the most important issues in cloud computing environment. In order to ensure security of data, users usually encrypt sensitive data and then store it in cloud servers. And how to efficiently retrieve ciphertext data on the cloud becomes a challenge. Searchable encryption technology provides an effective method for solving efficient retrieval of ciphertext by allowing users to directly retrieve ciphertext data through keywords, which protects data privacy while reducing communication and computing overhead. In recent years, in order to cope with different platforms and application scenarios, Public-key Encryption with Keyword Search (PEKS) technology has produced a large number of extension schemes based on different difficult problems, query methods, and changing structures. For security extensions and functional extensions, PEKS extension schemes were reviewed in terms of permission sharing, key management issues, fine-grained search and access control capabilities of current application requirements, and the performance of the specifically described solutions were compared and analyzed in depth, pointing out the advantages and shortcomings. Finally, the development trends of PEKS technology was summarized and prospected.
Focusing on the issue that only one instruction substitution with 5 operators and 13 substitution schemes is supported in Obfuscator Low Level Virtual Machine (OLLVM) at the instruction obfuscation level, an improved instruction obfuscation framework InsObf was proposed. InsObf, including junk code insertion and instruction substitution, was able to enhance the obfuscation effect at the instruction level based on OLLVM. For junk code insertion, firstly, the dependency of the instruction inside the basic block was analyzed, and then two kinds of junk code, multiple jump and bogus loop, were inserted to disrupt the structure of the basic block. For instruction substitution, based on OLLVM, it was expanded to 13 operators, with 52 instruction substitution schemes. The framework prototype was implemented on Low Level Virtual Machine (LLVM). Experimental results show that compared to OLLVM, InsObf has the cyclomatic complexity and resilience increased by almost four times, with a time cost of about 10 percentage points and a space cost of about 20 percentage points higher. Moreover, InsObf can provide higher code complexity compared to Armariris and Hikari, which are also improved on the basis of OLLVM, at the same order of magnitude of time and space costs. Therefore, InsObf can provide effective protection at the instruction level.
The use of Unmanned Aerial Vehicle (UAV) to continuously monitor designated areas can play a role in deterring invasion and damage as well as discovering abnormalities in time, but the fixed monitoring rules are easy to be discovered by the invaders. Therefore, it is necessary to design a random algorithm for UAV flight path. In view of the above problem, a UAV persistent monitoring path planning algorithm based on Value Function Iteration (VFI) was proposed. Firstly, the state of the monitoring target point was selected reasonably, and the remaining time of each monitoring node was analyzed. Secondly, the value function of the corresponding state of this monitoring target point was constructed by combining the reward/penalty benefit and the path security constraint. In the process of the VFI algorithm, the next node was selected randomly based on ε principle and roulette selection. Finally, with the goal that the growth of the value function of all states tends to be saturated, the UAV persistent monitoring path was solved. Simulation results show that the proposed algorithm has the obtained information entropy of 0.905 0, and the VFI running time of 0.363 7 s. Compared with the traditional Ant Colony Optimization (ACO), the proposed algorithm has the information entropy increased by 216%, and the running time decreased by 59%,both randomness and rapidity have been improved. It is verified that random UAV flight path is of great significance to improve the efficiency of persistent monitoring.
In the Service Oriented Architecture (SOA), an improved Krill Herd algorithm PRKH with adaptive crossover and random perturbation operator was proposed to solve the problem of easily falling into local optimum and high time cost in the process of service composition optimization. Firstly, a service composition optimization model was established based on Quality of Service (QoS), and the QoS calculation formulas and normalization methods under different structures were given. Then, based on the Krill Herd (KH) algorithm, the adaptive crossover probability and the random disturbance based on the actual offset were added to achieve a good balance between the global search ability and the local search ability of krill herd. Finally, through simulation, the proposed algorithm was compared with KH algorithm, Particle Swarm Optimization (PSO) algorithm, Artificial Bee Colony (ABC) algorithm and Flower Pollination Algorithm (FPA). Experimental results show that the PRKH algorithm can find better QoS composite services faster.
Since some computation in reachability Query Preserving Graph Compression (QPGC) algorithm are redundant, a high-performance compression strategy was proposed. In the stage of solving the vertex sets of ancestors and descendants, an algorithm named TSB (Topological Sorting Based algorithm for solving ancestor and descendant sets) was proposed for common graph data. Firstly, the vertices of the graph data were topological sorted. Then, the vertex sets were solved in the order or backward order of the topological sequence, avoiding the redundant computation caused by the ambiguous solution order. And an algorithm based on graph aggregation operation was proposed for graph data with short longest path, namely AGGB (AGGregation Based algorithm for solving ancestor and descendant sets), so the vertex sets were able to be solved in a certain number of aggregation operations. In the stage of solving reachability equivalence class, a Piecewise Statistical Pruning (PSP) algorithm was proposed. Firstly, piecewise statistics of ancestors and descendants sets were obtained and then the statistics were compared to achieve the coarse matching, and some unnecessary fine matches were pruned off. Experimental results show that compared with QPGC algorithm: in the stage of solving the vertex sets of ancestors and descendants, TSB and AGGB algorithm have the performance averagely increased by 94.22% and 90.00% respectively on different datasets; and in the stage of solving the reachability equivalence class, PSP algorithm has the performance increased by more than 70% on most datasets. With the increasing of the dataset, using TSB and AGGB cooperated with PSP has the performance improved by nearly 28 times. Theoretical analysis and simulation results show that the proposed strategy has less redundant computation and faster compression speed compared to QPGC.
To solve the time-consuming and error-prone problem in the diagnosis of fundus images by the ophthalmologists, an unsupervised automatic detection method for hard exudates in fundus images was proposed. Firstly, the blood vessels, dark lesion regions and optic disc were removed by using morphological background estimation in preprocessing phase. Then, with the image luminosity channel taken as the initial image, the low rank matrix and sparse matrix were obtained by combining local entropy and Robust Principal Components Analysis (RPCA) based on the locality and sparsity of hard exudates in fundus images. Finally, the hard exudates regions were obtained by the normalized sparse matrix. The performance of the proposed method was tested on the fundus images databases e-ophtha EX and DIARETDB1. The experimental results show that the proposed method can achieve 91.13% of sensitivity and 90% of specificity in the lesional level and 99.03% of accuracy in the image level and 0.5 s of average running time. It can be seen that the proposed method has higher sensitivity and shorter running time compared with Support Vector Machine (SVM) method and K-means method.