An Advanced Security System for Detecting Cyberattacks in Computer Networks Based on Machine Learning
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Abstract
Computer networks have become a fundamental component of modern digital infrastructures across various sectors, including government institutions, banking systems, telecommunications, and electronic commerce. With the rapid expansion of network usage, cyberattacks have increased significantly in both frequency and complexity, posing serious challenges to traditional security systems. Conventional intrusion detection systems, which rely mainly on predefined rules and signature-based mechanisms, have demonstrated clear limitations in detecting advanced and previously unknown attacks, in addition to high false alarm rates. This research presents the design and implementation of an advanced security system for detecting cyberattacks in computer networks based on machine learning techniques. The proposed system analyzes network traffic data and classifies activities into normal and malicious behaviors using intelligent learning models. Several machine learning algorithms, including Support Vector Machine (SVM), Random Forest, and Artificial Neural Networks (ANN), were applied and evaluated to assess their effectiveness in cyberattack detection. The system was developed following a structured methodology that includes data collection, preprocessing, feature extraction, model training, and performance evaluation. Standard evaluation metrics such as accuracy, precision, recall, and F1-score were used to measure system performance. Experimental results demonstrated that machine learning–based approaches significantly improve detection accuracy and reduce false alarm rates compared to traditional methods. Among the evaluated algorithms, the Random Forest model achieved the best overall performance, particularly in terms of recall and F1-score.
The findings of this study confirm the effectiveness of integrating machine learning techniques into intrusion detection systems and highlight their potential in enhancing cybersecurity capabilities. The proposed system contributes to improving early attack detection, increasing system reliability, and addressing emerging cybersecurity challenges in modern network environments.
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