scholarly journals Malicious Code Detection Method Based on WIG-GA Feature Selection Algorithm

2018 ◽  
Vol 08 (03) ◽  
pp. 266-274
Author(s):  
冠 王
Keyword(s):  
Feature Selection ◽  
Detection Method ◽  
Malicious Code ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Malicious Code Detection

scholarly journals Semi-Supervised K-Means DDoS Detection Method Using Hybrid Feature Selection Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 64351-64365 ◽  
Author(s):  
Yonghao Gu ◽  
Kaiyue Li ◽  
Zhenyang Guo ◽  
Yongfei Wang
Keyword(s):  
Feature Selection ◽  
Detection Method ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Ddos Detection

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

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