Published Paper Details:

With the increasing complexity and volume of network traffic, ensuring the security and reliability of computer networks has become a critical challenge. This research focuses on leveraging machine learning techniques for the analysis of network traffic and the detection of anomalies, aiming to enhance the proactive identification of potential security threats. The study begins by exploring the current landscape of network security, emphasizing the limitations of traditional rule-based approaches and the need for more adaptive and intelligent solutions. Machine learning algorithms, specifically supervised and unsupervised learning models, are then applied to analyze historical network traffic data, enabling the system to learn patterns of normal behavior and identify deviations indicative of potential security breaches. The research employs feature engineering to extract relevant information from network data, enhancing the effectiveness of machine learning models in distinguishing between normal and anomalous activities. Various algorithms, such as Support Vector Machines, Random Forests, and Neural Networks, are implemented and compared to determine their suitability for different types of network anomalies. Furthermore, the study investigates the scalability and real-time processing capabilities of the proposed machine learning-based anomaly detection system. Special attention is given to the integration of these models into existing network infrastructures, ensuring seamless deployment and minimal disruption to network performance. The evaluation of the system's performance involves the use of benchmark datasets and simulated attack scenarios, allowing for a comprehensive assessment of its accuracy, precision, recall, and false positive rates. The results demonstrate the potential of machine learning in enhancing network security by efficiently detecting and mitigating anomalous behavior. Ultimately, this research contributes to the advancement of network security methodologies by providing insights into the practical application of machine learning for analyzing network traffic and proactively identifying security threats, thereby bolstering the resilience of modern computer networks against evolving cyber threats.