Leveraging K-means clustering for enhanced detection of network traffic attacks

With the expansion of network traffic and the increasing experiences of cyber threats, the need for flexible as well as systematic network traffic attack detection systems has become foremost. Conventional signature-based methods often struggle to identify novel or previously unknown attacks, making it essential to explore alternative techniques for enhancing network security. This research presents a novel approach for enhancing network traffic attack detection using K-means clustering, a popular unsupervised machine learning algorithm. The proposed system employs K-means clustering to group network traffic data into clusters based on their similarity. By identifying anomalous patterns within these clusters, the system can effectively detect network attacks. The approach is evaluated using a real-world network traffic dataset, and the results demonstrate its effectiveness in improving the accuracy and efficiency of attack detection. Additionally, the approach shows promise in the detection of zero-day attacks, thus enhancing network security in the face of evolving threats. This research contributes to the field of network security by offering a data-driven and proactive approach to attack detection that can adapt to emerging threats and minimize false positives.