Journal ArticleDOI
An Intelligent Network Intrusion Detection System Using Particle Swarm Optimization (PSO) and Deep Network Networks (DNN)
D Preethi,Neelu Khare +1 more
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TLDR
The experimental results show that the proposed PSO-DNN-based intrusion detection system performs better compared with other methods considered for comparison.Abstract:
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.read more
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Face recognition algorithm based on particle swarm optimization and image feature compensation
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Engineering Cost Prediction Model Based on DNN
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A Deep Q-Network Eith Experience Optimization (DQN-EO) for Atari's Space Invaders and Its Performance Evaluation
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References
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Journal ArticleDOI
A Deep Learning Approach to Network Intrusion Detection
TL;DR: This paper presents a novel deep learning technique for intrusion detection, which addresses concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks and details the proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning.
Journal ArticleDOI
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection
TL;DR: A detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with variousMachine learning techniques in detecting intrusive activities and future directions are provided for attack detection using machinelearning techniques.
Journal ArticleDOI
Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection
TL;DR: Well-known machine learning techniques, namely, SVM, random forest, and extreme learning machine (ELM) are applied and the results indicate that ELM outperforms other approaches in intrusion detection mechanisms.
Journal ArticleDOI
Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection
TL;DR: The proposed STL-IDS approach improves network intrusion detection and provides a new research method for intrusion detection, and has accelerated SVM training and testing times and performed better than most of the previous approaches in terms of performance metrics in binary and multiclass classification.
Journal ArticleDOI
Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis
G. Thippa Reddy,M. Praveen Kumar Reddy,Kuruva Lakshmanna,Dharmendra Singh Rajput,Rajesh Kaluri,Gautam Srivastava,Gautam Srivastava +6 more
TL;DR: Thorough experimental analysis shows that the adaptive genetic algorithm with fuzzy logic (AGAFL) model has outperformed current existing methods in diagnosing heart disease at early stages.