UTILIZING MACHINE LEARNING-BASED INTRUSION DETECTION TECHNOLOGIES FOR NETWORK SECURITY

Abstract

Effective intrusion detection systems (IDS) are becoming essential for maintaining computer network security due to the growing complexity of cyber-attacks. Machine Learning (ML) can increase the effectiveness of intrusion detection technology, which is an essential resource to safeguard network security. A novel ML technique for intrusion information detection called Stochastic Cat Swarm Optimized Privacy-Preserving Logistic Regression (SCSO-PPLR) is proposed. We assess intrusion detection systems using KDDCup99 dataset. The dataset is pre-processed using Z-score normalization to normalize the features. Next, Features are extracted by Principal Component Analysis (PCA). By comparing the results of the SCSO-PPLR methodology with traditional methods and using assessment criteria including accuracy, precision, recall, and F1-score, the model's performance is extensively evaluated. The study reveals that SCSO-PPLR is an acceptable strategy for intrusion detection in network security and it is effective. These insights broaden IDS and groundwork for further research on reliable cybersecurity remedies.

Keywords

• machine learning
• network security
• network attack
• cybersecurity
• intrusion detection systems (ids)
• stochastic cat swarm optimized privacy-preserving logistic regression (scso-pplr)