International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The rapid growth of internet-based services, cloud computing, and interconnected network infrastructures has significantly increased the risk of cyberattacks and unauthorized access. Traditional intrusion detection systems (IDS), which rely primarily on signature-based or rule-based techniques, often fail to detect newly emerging or sophisticated attack patterns. These limitations highlight the need for intelligent and adaptive security mechanisms capable of analyzing large volumes of network traffic and identifying malicious activities in real time. To address this challenge, this study proposes an Artificial Neural Network (ANN)-based approach for intelligent network intrusion detection that enhances the accuracy and efficiency of cybersecurity monitoring systems. The proposed framework utilizes the learning and pattern recognition capabilities of artificial neural networks to analyze network traffic data and classify it into normal or malicious categories. The multilayer neural network architecture is designed to capture complex relationships within network features and detect anomalies that indicate potential cyber threats. The system performs data preprocessing and feature extraction to improve the quality of input data and reduce noise and redundancy. The ANN model is then trained using benchmark intrusion detection datasets containing various types of network attacks, including denial-of-service (DoS), probing attacks, remote-to-local (R2L), and user-to-root (U2R) intrusions. Experimental results demonstrate that the proposed ANN-based intrusion detection model provides improved detection accuracy, higher precision, and lower false alarm rates compared with conventional intrusion detection techniques. The adaptive learning capability of neural networks enables the system to identify previously unseen attack patterns and continuously improve its performance over time. Furthermore, the framework supports real-time monitoring and scalability, making it suitable for deployment in modern network environments such as enterprise networks, cloud computing platforms, and Internet of Things (IoT) systems. This research highlights the effectiveness of artificial neural networks in strengthening network security by providing an intelligent and automated mechanism for detecting and preventing cyber intrusions in next-generation network infrastructures.

Keywords: Artificial Neural Network (ANN); Network Intrusion Detection; Cybersecurity; Intrusion Detection System (IDS); Machine Learning; Network Security; Anomaly Detection; Cyber Attack Detection; Intelligent Security Systems; Deep Learning in Security

Downloads: | DOI: 10.17148/IJARCCE.2026.15231