For safe path planning of unmanned aerial vehicles (UAVs) in a three-dimensional (3D) environment with multiple threats, first, a cost function is introduced according to the terrain constraints and UAV overall performance constraints of the path planning problem. Then, improved nonlinear dynamic inertia weights (INDIW) are introduced into the particle swarm optimization (PSO) algorithm, and when the particles fall into the local optimum, the velocity is perturbed, and the velocity and improved nonlinear dynamic inertia weight PSO (VAINDIWPSO) algorithm are obtained. The algorithm improves the speed of convergence and fitness function value of the PSO algorithm. However, the impact of flyable path optimization is now not obvious. Therefore, to further enhance the overall performance of the VAINDIWPSO algorithm, the adaptive adjustment of the velocity is introduced, the chaotic initialization is carried out, and the improved logistic chaotic map is introduced into the algorithm, and an improved chaotic-VAINDIWPSO (IC-VAINDIWPSO) algorithm is obtained. Then, the corresponding relationship between the algorithm and constraints is used to efficiently search complicated environments and find paths with excessive security and small cost function. The simulation outcomes exhibit that in a complicated environment the IC-VAINDIWPSO algorithm substantially improves the speed of convergence of the algorithm, reduces the fitness function value of the algorithm and the initialization time of the algorithm, and the acquired path is additionally smoother. A near-optimal solution is obtained.

For classified and sensitive electronic documents within the scope of enterprises and organizations, in order to standardize and strengthen the confidentiality management of enterprises and meet the actual needs of secret text classification, a document automatic classification optimization method based on keyword retrieval and the kNN classification algorithm is proposed. The method supports keyword classification management, provides users with keywords of multiple risk levels, and then combines a matching scanning algorithm to label keywords of different levels. The text with labels is used as the training set of the kNN algorithm to classify the target text and realize the classification protection of text data. Aimed at solving the shortcomings of large feature vector dimension, low classification efficiency, and low accuracy in existing kNN text classification methods, an optimization method is proposed using a feature selection algorithm and a kNN algorithm based on an AVX instruction set to realize real-time classification of massive texts. By constructing a keyword dictionary and an optimized feature vector, parallel calculation of the feature vector weight and distance vector is realized, and the accuracy and efficiency of text classification are improved. The experimental results show that the multi-classification effect of the feature selection algorithm used in this paper, tf-DE, is better than that of the traditional tf-idf algorithm, and the classification effect of kNN is comparable to that of the support vector machine (SVM) algorithm. With the increase in feature vector dimensions, the classification effect of the text classification algorithm is improved and the classification time also increases linearly. The AVX-256 acceleration method takes about 55% of the time of the original version, thus verifying the effect of multi-classification of massive texts for document confidentiality management.

In order to cope with the massive scale of traffic and reduce the memory overhead of traffic statistics, the traffic statistics method based on the Sketch algorithm has become a research hotspot for traffic statistics. This paper studies the problem of the top-k flow statistics based on the Sketch algorithm and proposes a method to estimate the flow heat from massive network traffic using the Sketch algorithm and identify the kth flow with the highest heat by using a bitonic sort algorithm. In view of the performance difficulties of applying multiple hash functions in the implementation of the Sketch algorithm, the Single-Instruction-Multiple-Data (SIMD) instruction set is adopted to improve the performance of the Sketch algorithm so that SIMD instructions can process multiple fragments of data in a single step, implement multiple hash operations at the same time, compare and sort multiple flow tables at the same time. Thus, the throughput of the execution task is improved. Firstly, the elements of data flow are described and stored in the form of vectors, while the construction, analysis, and operation of data vectors are realized by SIMD instructions. Secondly, the multi-hash operation is simplified into a single vector operation, which reduces the CPU computing resource consumption of the Sketch algorithm. At the same time, the SIMD instruction set is used to optimize the parallel comparison operation of the flow table in a bitonic sort algorithm. Finally, the SIMD instruction set is used to optimize the functions in the Sketch algorithm and top-k sorting algorithm program, and the optimized code is tested and analyzed. The experimental results show that the time consumed by the advanced vector extensions (AVX)-instructions-optimized version has a significant reduction compared to the original version. When the length of KEY is 96 bytes, the instructions consumed by multiple hash functions account for less in the entire Sketch algorithm, and the time consumed by the optimized version of AVX is about 67.2% of that in the original version. As the length of KEY gradually increases to 256 bytes, the time consumed by the optimized version of AVX decreases to 53.8% of the original version. The simulation results show that the AVX optimization algorithm is effective in improving the measurement efficiency of network flow.

Previous network feature extraction methods used for network anomaly detection have some problems, such as being unable to extract features from the original network traffic, or that they can only extract coarse-grained features, as well as that they are highly dependent on manual analysis. To solve these problems, this paper proposes a fine-grained and highly practical dynamic application fingerprint extraction method. By putting forward a fine-grained high-utility dynamic fingerprinting (Huf) algorithm to build a Huf-Tree based on the N-gram (every substring of a larger string, of a fixed length n) model, combining it with the network traffic segment-IP address transition (IAT) method to achieve dynamic application fingerprint extraction, and through the utility of fingerprint, the calculation was performed to obtain a more valuable fingerprint, to achieve fine-grained and efficient flow characteristic extraction, and to solve the problem of this method being highly dependent on manual analysis. The experimental results show that the Huf algorithm can realize the dynamic application of fingerprint extraction and solve the existing problems.