Enhancing Cybersecurity Defense Using AI-Driven Threat Detection Systems

Abstract

Cybersecurity threats are rising in both complexity and frequency so that traditional signature-based intrusion detection systems are less effective in recognizing sophisticated and novel attack patterns. In this study, we present a deep learning-based intrusion detection system that employs a one-dimensional Convolutional Neural Network (1D-CNN) model trained on the NSL-KDD dataset for classifying network traffic data and detecting anomaly behavior in the network. The proposed approach includes a thorough multi-stage preprocessing pipeline of the data called normalization, label encoding, and one-hot encoding to ensure the best possible training process. With an accuracy of over 93%, the CNN model largely surpassed traditional Machine Learning approaches like Support Vector Machines (SVM) and Random Forests and outperformed natural deep learning models like Long Short-Term Memory (LSTM) models. The proposed model also indicated a reasonable degree of generalization over the categories of attacks, with very low rates of False Positives. This work illustrates the potential of AI-algorithm-enabled security solutions for intelligent, adaptive, and scalable threat detection. The lightweight design of our model is also capable of allowing real-time deployment in situations of limited compute power and resources, which is an important objective in meeting the needs of today's Security Operations Centers dealing with internet-based attack vectors.