Efficient Anomaly Detection System for Video Surveillance Application in WVSN with Particle Swarm Optimization

Network intrusion detection system (NIDS) plays a major role in ensuring network security. In this paper, the authors propose a PSO-DNN-based intrusion detection system. The correlation-based feature selection (CFS) applied for feature selection with particle swarm optimization (PSO) as search method and deep neural networks (DNN) for classification of network intrusions. The Adam optimizer is applied for optimizing the learning rate, and softmax classifier is used for classification. The experimentations were duly conducted on the standard benchmark NSL-KDD dataset. The proposed model is validated using 10-fold cross-validation and evaluated using the performance metrics such as accuracy, precision, recall, and F1-score. Also, the results are also compared with DNN and CFS+DNN. The experimental results show that the proposed model performs better compared with other methods considered for comparison.

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An Intrusion Detection System Based on Hybrid of Particle Swarm Optimization (PSO) and Magnetic Optimization Algorithm (MOA)

10.1007/978-3-030-85990-9_46 ◽

2021 ◽

pp. 579-588

Author(s):

Siti Norwahidayah Wahab ◽

Noor Suhana Sulaiman ◽

Noraniah Abdul Aziz ◽

Nur Liyana Zakaria ◽

Ainal Amirah Abd Aziz

Keyword(s):

Particle Swarm Optimization ◽

Intrusion Detection ◽

Optimization Algorithm ◽

Intrusion Detection System ◽

Detection System ◽

Particle Swarm ◽

Swarm Optimization

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A COVID-19 Detection Algorithm Using Deep Features and Discrete Social Learning Particle Swarm Optimization for Edge Computing Devices

ACM Transactions on Internet Technology ◽

10.1145/3453170 ◽

2022 ◽

Vol 22 (3) ◽

pp. 1-17

Author(s):

Chaonan Shen ◽

Kai Zhang ◽

Jinshan Tang

Keyword(s):

Particle Swarm Optimization ◽

Social Learning ◽

Early Detection ◽

Detection System ◽

Particle Swarm ◽

Detection Algorithm ◽

Edge Computing ◽

Internet Technology ◽

Support Vector ◽

Swarm Optimization

COVID-19 has been spread around the world and has caused a huge number of deaths. Early detection of this disease is the most efficient way to prevent its rapid spread. Due to the development of internet technology and edge intelligence, developing an early detection system for COVID-19 in the medical environment of the Internet of Things (IoT) can effectively alleviate the spread of the disease. In this paper, a detection algorithm is developed, which can detect COVID-19 effectively by utilizing the features from Chest X-ray (CXR) images. First, a pre-trained model (ResNet18) is adopted for feature extraction. Then, a discrete social learning particle swarm optimization algorithm (DSLPSO) is proposed for feature selection. By filtering redundant and irrelevant features, the dimensionality of the feature vector is reduced. Finally, the images are classified by a Support Vector Machine (SVM) for COVID-19 detection. Experimental results show that the proposed algorithm can achieve competitive performance with fewer features, which is suitable for edge computing devices with lower computation power.

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