Network Intrusion Detection System

Due to the extensive use of communication networks and the ease of communicating via wireless networks, these types of networks are increasingly considered. Usability in any environment without the need for monitoring and environmental engineering of these networks has been caused increasing use of it in various fields. It also caused the emergence of security problems in the sending and receiving information that intrusion detection has been raised as the most important issue. Hence, Network intrusion detection system (NIDS) is the process of identifying malicious activity in a network by analyzing the network traffic behavior. A wireless sensor network is composed of sensors that are responsible for collecting information from the environment. These wireless networks, because of the limitation of resources, mobility, and critical tasks, are relatively high vulnerabilities in comparison to other networks. Therefore, forecasting and intrusion detection systems play an important role in providing security in wireless sensor networks that can involve a wide range of attacks. Traffic behavior in the network has many features and dimensions, so dimensionality reduction plays a vital role in IDS, since detecting anomalies from high-dimensional network traffic features is a time-consuming process. Feature selection influences the speed of the analysis and detection. For this purpose, in the current study, a new approach is proposed to predict the intrusion of wireless networks using firefly based feature selection and fast learning network. Selected features in the feature selection phase are used as inputs to the fast learning network to analyze the intrusion of the network in real-time. According to the simulation results, it can be said that the fast neural network method continues training so as to avoid overfitting error. While neural networks further learn training set features until the training process is completed. Thus, the occurrence of overfitting phenomenon in neural networks is common. Therefore, the proposed method grants better performance than the neural network method in predicting new attacks on the network.

Securing computer networks against malicious attacks requires an efficient Network Intrusion Detection System (IDS). While machine learning techniques are commonly used for anomaly-based intrusion detection, data imbalance challenges conventional algorithms, leading to biased predictions and reduced accuracy. This study introduces a novel approach that combines ADASYN and Tomek links to address this issue, along with specific machine learning algorithms. ADASYN generates synthetic samples for the minority class to achieve dataset balance, and Tomek links eliminate redundant instances from the majority class. Four supervised machine learning algorithms (Random Forest, J48, Multilayer Perceptron, and Bagging) were assessed on both imbalanced and balanced datasets. Results show Random Forest exhibited 99.67% accuracy, while J48 and Bagging yielded 99.30%, and MLP recorded 98.53%. Notably, Random Forest emerges as a highly effective algorithm for Intrusion Detection, demonstrating flawless accuracy with balanced data. These outcomes highlight the proposed approach's ability to enhance prediction accuracy in network intrusion detection compared to imbalanced datasets, validated through a comparative analysis with state-of-the-art solutions.