Enhancing Cybersecurity Architectures with Artificial Intelligence (AI): A Framework for Automated Threat Intelligence Detection System

Abstract

In order to execute cyber-security, intrusion detection systems (IDS) are developed to identify threats and irregularities in computer networks. An efficient data-driven intrusion detection system has been developed as a result of the use of artificial intelligence, particularly machine learning techniques. The proposed security model utilizes BGOTSVM to develop IDS systems starting from a security feature ranking process followed by model development using crucial features. The increasing sophistication of cyber threats necessitates robust and intelligent threat detection systems. This study uses the UNSW-NB15 dataset to demonstrate a Convolutional Neural Network (CNN)-based approach for financial fraud detection. To improve model performance, the suggested methodology incorporates data preparation techniques such as feature selection, one-hot encoding, and managing missing values. The CNN model, optimized through hyperparameter tuning, is compared against traditional machine learning (ML) models, including AdaBoost and naïve biased. Experimental findings show that CNN performs better than any baseline model, reaching the maximum accuracy (93.40%), precision (94.63%), recall (93.40%), and F1-score (92.81%). Performance evaluation metrics, classification reports, and confusion matrices further validate the CNN model’s ability to identify fraudulent activity. Comparative analysis indicates that deep learning techniques, particularly CNN, offer superior threat detection capabilities by effectively identifying complex trends in communication over network information.