scholarly journals PSOFuzzer: A Target-Oriented Software Vulnerability Detection Technology Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (3) ◽  
pp. 1095
Author(s):  
Chen Chen ◽  
Han Xu ◽  
Baojiang Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Target Point ◽  
Vulnerability Detection ◽  
High Quality ◽  
Swarm Optimization ◽  
Detection Technology ◽  
Test Process ◽  
Quality Features ◽  
Historical Samples

Coverage-oriented and target-oriented fuzzing are widely used in vulnerability detection. Compared with coverage-oriented fuzzing, target-oriented fuzzing concentrates more computing resources on suspected vulnerable points to improve the testing efficiency. However, the sample generation algorithm used in target-oriented vulnerability detection technology has some problems, such as weak guidance, weak sample penetration, and difficult sample generation. This paper proposes a new target-oriented fuzzer, PSOFuzzer, that uses particle swarm optimization to generate samples. PSOFuzzer can quickly learn high-quality features in historical samples and implant them into new samples that can be led to execute the suspected vulnerable point. The experimental results show that PSOFuzzer can generate more samples in the test process to reach the target point and can trigger vulnerabilities with 79% and 423% higher probability than AFLGo and Sidewinder, respectively, on tested software programs.

XGRouter: high-quality global router in X-architecture with particle swarm optimization

2015 ◽  
Vol 9 (4) ◽  
pp. 576-594 ◽  
Author(s):  
Genggeng Liu ◽  
Wenzhong Guo ◽  
Rongrong Li ◽  
Yuzhen Niu ◽  
Guolong Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
High Quality ◽  
Swarm Optimization ◽  
X Architecture

scholarly journals Using Particle Swarm Optimization as Pathfinding Strategy in a Space with Obstacles

2021 ◽  
Author(s):  
David
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Population Based ◽  
Target Point ◽  
Adaptive Optimization ◽  
Swarm Optimization ◽  
Algorithm Performance ◽  
Long Time

Particle swarm optimization (PSO) is a search algorithm based on stochastic and population-based adaptive optimization. In this paper, a pathfinding strategy is proposed to improve the efficiency of path planning for a broad range of applications. This study aims to investigate the effect of PSO parameters (numbers of particle, weight constant, particle constant, and global constant) on algorithm performance to give solution paths. Increasing the PSO parameters makes the swarm move faster to the target point but takes a long time to converge because of too many random movements, and vice versa. From a variety of simulations with different parameters, the PSO algorithm is proven to be able to provide a solution path in a space with obstacles.

Particle swarm optimization as alternative tool to sensory evaluation to produce high-quality low-sodium fish sauce via electrodialysis

2018 ◽  
Vol 228 ◽  
pp. 84-90
Author(s):  
San Ratanasanya ◽  
Nathamol Chindapan ◽  
Jumpol Polvichai ◽  
Booncharoen Sirinaovakul ◽  
Sakamon Devahastin
Keyword(s):  
Particle Swarm Optimization ◽  
Sensory Evaluation ◽  
Particle Swarm ◽  
Fish Sauce ◽  
High Quality ◽  
Swarm Optimization ◽  
Low Sodium

scholarly journals A point cloud registration algorithm based on normal vector and particle swarm optimization

2019 ◽  
Vol 53 (3-4) ◽  
pp. 265-275 ◽  
Author(s):  
Xu Zhan ◽  
Yong Cai ◽  
Heng Li ◽  
Yangmin Li ◽  
Ping He
Keyword(s):  
Particle Swarm Optimization ◽  
Point Cloud ◽  
Particle Swarm ◽  
Target Point ◽  
Normal Vector ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Swarm Optimization ◽  
Registration Algorithm ◽  
The Right

Based on normal vector and particle swarm optimization (NVP), a point cloud registration algorithm is proposed by searching the corresponding points. It provides a new method for point cloud registration using feature point registration. First, in order to find the nearest eight neighbor nodes, the k-d tree is employed to build the relationship between points. Then, the normal vector and the distance between the point and the center gravity of eight neighbor points can be calculated. Second, the particle swarm optimization is used to search the corresponding points. There are two conditions to terminate the search in particle swarm optimization: one is that the normal vector of node in the original point cloud is the most similar to that in the target point cloud, and the other is that the distance between the point and the center gravity of eight neighbor points of node is the most similar to that in the target point cloud. Third, after obtaining the corresponding points, they are tested by random sample consensus in order to obtain the right corresponding points. Fourth, the right corresponding points are registered by the quaternion method. The experiments demonstrate that this algorithm is effective. Even in the case of point cloud data lost, it also has high registration accuracy.

RFID Anti-Collision Method Based on Particle Swarm Optimization and Support Vector Machine

2012 ◽  
Vol 170-173 ◽  
pp. 3398-3401
Author(s):  
Wen Ge Zhao
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Collision Detection ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Support Vector ◽  
Detection Accuracy ◽  
Swarm Optimization ◽  
Detection Technology

RFID anti-collision method based on particle swarm optimization and support vector machine is presented in the paper. Support vector machine is a new detection technology,which is applied to RFID anti-collision detection. Particle swarm optimization algorithm is applied to choose the appropriate parameters of support vector machine. Particle swarm optimization algorithm can make the particle move toward the optimal resolution based on the history best experiences of each particle and global best position in swarm.The proposed RFID anti-collision structure is mainly composed of protocol processing module, interface module, RFID anti-collision method and serial-parallel conversion.The testing results show that RFID collision detection accuracy of particle swarm optimization and support vector machine than that of traditional support vector machine and BP neural network.

scholarly journals Swarm Intelligence Algorithms for Weapon-Target Assignment in a Multilayer Defense Scenario: A Comparative Study

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 824 ◽  
Author(s):  
Ming Cao ◽  
Weiguo Fang
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Large Scale ◽  
Particle Swarm ◽  
Small Scale ◽  
Benchmark Problem ◽  
High Quality ◽  
Swarm Optimization ◽  
Target Assignment ◽  
And Performance

Weapon-target assignment (WTA) is a kind of NP-complete problem in military operations research. To solve the multilayer defense WTA problems when the information about enemy’s attacking plan is symmetric to the defender, we propose four heuristic algorithms based on swarm intelligence with customizations and improvements, including ant colony optimization (ACO), binary particle swarm optimization (BPSO), integer particle swarm optimization (IPSO) and sine cosine algorithm (SCA). Our objective is to assess and compare the performance of different algorithms to determine the best algorithm for practical large-scale WTA problems. The effectiveness and performance of various algorithms are evaluated and compared by means of a benchmark problem with a small scale, the theoretical optimal solution of which is known. The four algorithms can obtain satisfactory solutions to the benchmark problem with high quality and high robustness, while IPSO is superior to BPSO, ACO and SCA with respect to the solution quality, algorithmic robustness and computational efficiency. Then, IPSO is applied to a large-scale WTA problem, and its effectiveness and performance are further assessed. We demonstrate that IPSO is capable of solving large-scale WTA problems with high efficiency, high quality and high robustness, thus meeting the critical requirements of real-time decision-making in modern warfare.

An improved denoise method based on EEMD and optimal wavelet threshold for model building of OPAX

2021 ◽  
pp. 095440702110125
Author(s):  
Ke Chen ◽  
Xiaodong Zhang ◽  
Yubo Liu ◽  
Jun Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Noise Reduction ◽  
Model Building ◽  
Particle Swarm ◽  
Sample Entropy ◽  
Threshold Function ◽  
Target Point ◽  
Swarm Optimization ◽  
Operating Condition ◽  
Soft Threshold

To improve the accuracy of Operational Path Analysis with Exogeneous Inputs (OPAX) model by excluding the noise interference sufficiently in the vehicle operating condition data (time-domain vibration signal), the combined noise reduction method of Ensemble Empirical Mode Decomposition (EEMD) and wavelet threshold was used. Since the noise content of each noisy intrinsic mode functions (IMFs) decomposed by EEMD is uncertain, the effective signal element in the less noisy IMFs affects the accuracy of the first-layer wavelet coefficients to estimate the noise variance, the EEMD and wavelet particle swarm optimization sample entropy threshold denoising (EEMD-WPSE) method is presented in terms of information entropy. In this method, the sample entropy of the eliminated noise is used as the information cost function, together with the particle swarm optimization algorithm to find the optimal wavelet threshold of each high-frequency noisy IMFs. After denoising the simulation signal, it is found that the combination of EEMD-WPSE threshold with hard threshold function, soft threshold function and half-soft threshold function identifying higher SNR and lower RMSE, are given to demonstrate the higher universality of the proposed method. The method is applied to the noise reduction processing of the automobile operating condition data for constructing the OPAX model, and the degree of similarity between the synthesized responses of the care-target point obtained by the OPAX model and the measured responses under the second order operational condition are observed, as it turned out, the calculation results of SNR and RMSE indicated that EEMD-WPSE can better promote the accuracy of OPAX model in terms of noise reduction.

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