Enhancing Intrusion Detection System Using Machine Learning and Deep Learning

Intrusion Detection Systems (IDS) are critical components in ensuring the security and integrity of computer networks by identifying and thwarting malicious activities. With the ever-evolving nature of cyber threats, it is imperative to develop IDS models that are capable of accurately detecting and classifying network intrusions. This research aims to enhance the performance of IDS systems by harnessing the power of various machine learning algorithms, namely Random Forest, Neural Networks, and One-Dimensional Convolutional Neural Networks(1D-CNN). The widely used CICIDS2017 dataset is employed to train and evaluate the proposed models, following an extensive preprocessing phase to extract pertinent information and eliminate extraneous noise. The accuracy of each algorithm is thoroughly assessed, and a comparative analysis is conducted to determine the optimal approach for building an effective IDS model. The results unequivocally demonstrate that it exhibits exceptional performance and robust capabilities in accurately detecting and classifying network intrusions. These empirical findings significantly contribute to the advancement of IDS technologies, empowering organizations to bolster network security and effectively mitigate the ever-growing array of cyber threats.