Affiliation:
1. Faculty of Computers and Informatics, Suez Canal University, Egypt
Abstract
The increase of security threats and hacking the computer networks are one of the most dangerous issues should treat in these days. Intrusion Detection Systems (IDSs), are the most appropriate methods to prevent and detect the attacks of networks and computer systems. This study presents several techniques to discover network anomalies using data mining tasks, Machine learning technology and dependence of artificial intelligence techniques. In this research, the smart hybrid model was developed to explore any penetrations inside the network. The model divides into two basic stages. The first stage includes the Genetic Algorithm (GA) in selecting the characteristics with depends on a process of extracting, Discretize And dimensionality reduction through Proportional K-Interval Discretization (PKID) and Fisher Linear Discriminant Analysis (FLDA) on respectively. At the end of the first stage combining Naïve Bayes classifier (NB) and Decision Table (DT) using NSL-KDD data set divided into two separate groups for training and testing. The second stage completely depends on the first stage outputs (predicted class) and reclassified with multilayer perceptrons using Deep Learning4J (DL) and the use of algorithm Stochastic Gradient Descent (SGD). In order to improve the performance in terms of the accuracy in classification of penetrations, raising the average of discovering and reducing the false alarms. The comparison of the proposed model and conventional models show the superiority of the proposed model and the previous conventional hybrid models. The result of the proposed model is 99.9325 of classification accuracy, the rate of detection is 99.9738 and 0.00093 of false alarms
Cited by
20 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Intrusion Outlier Neutralizer;Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology;2024-04-12
2. Advancing IoT Security Posture K-Means Clustering for Malware Detection;Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology;2024-04-12
3. Intrusion detection based on ensemble learning for big data classification;Cluster Computing;2023-11-07
4. Harnessing the Power and Simplicity of Decision Trees to Detect IoT Malware;Transformational Interventions for Business, Technology, and Healthcare;2023-10-16
5. An Intrusion Detection Method based on Fusion Neural Network;Frontiers in Computing and Intelligent Systems;2023-06-26