Intrusion Detection System by Integrating Mod K- Means C Algorithm and T-SNE Dimensionality Reduction
Keywords:
Intrusion Detection System, Modified K-Means Clustering, t-SNE Dimensionality Reduction, Cyber security, Network Security, Parallel Processing, and Machine Learning.Abstract
This research presents a new methodology to enhance the precision of Intrusion Detection Systems (IDS) by integrating the Modified K-Means Clustering (ModKMeansC) algorithm as a classifier along with t-Distributed Stochastic Neighbor Embedding (t-SNE) a reduction technique of dimensionality. In the realm of cyber security, conventional IDS face challenges in accurately discerning abnormal network behavior due to the dynamic and intricate nature of cyber threats. The ModKMeansC algorithm, intricately designed to address issues stemming from abnormal network connections, introduces parallelism into centroid and distance calculation update operations. This concurrent execution, performed asynchronously for each data point, facilitates real-time analysis of network traffic, thereby bolstering efficiency and responsiveness. Leveraging the CICIDS2017 dataset, encompassing both normal and abnormal network traffic patterns, the study implements and fine-tunes the ModKMeansC algorithm for optimal performance. t-SNE is applied to preprocess the data before feeding it into the classifier. The proposed system's performance is meticulously assessed using key performance metrics. A proportional analysis against traditional intrusion detection algorithms underscores the ModKMeansC algorithm's advantages in accurately categorizing abnormal network behavior as 92%. Results and ensuing discussions highlight the algorithm's adeptness in efficiently handling abnormal network connections and its prowess in parallel processing. This examination significantly supports to the dynamic field of cyber security by presenting a more effective and responsive methodology for identifying abnormal network behavior. The amalgamation of the ModKMeansC algorithm with t-SNE holds considerable promise in elevating the accuracy of IDS as 95%. Future research directions may encompass adapting the proposed system to real-world cyber security scenarios and further optimizing the algorithm for scalability in large-scale networks.
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