Integrating AI in security information and event management for real time cyber defense

The increasing frequency and sophistication of cyber-attacks have exposed significant shortcomings in conventional detection systems, emphasizing the urgent need for more advanced Cyber Threat Intelligence (CTI) capabilities. While Open-Source Intelligence (OSINT) has become a cornerstone for early threat identification, the manual analysis of such unstructured data remains labor-intensive and susceptible to error, limiting its effectiveness. To address these challenges, this study introduces an AI-driven system designed for real-time detection and analysis of cyber threat information on Twitter. The approach integrates a hybrid feature extraction technique that combines Bidirectional Encoder Representations from Transformers (BERT) with Iterated Dilated Convolutional Neural Networks (ID-CNNs). To enhance feature relevance and remove noise and redundant features, the Binary Tree Growth (BTG) algorithm is employed as feature selection. Classification is performed using a bi-directional temporal convolutional network (BiTCN), which is well-suited for modeling sequential data. Experimental evaluations show that the proposed model achieves strong performance, with accuracy rates of 99.01% on dataset 1 and 97.63% on dataset 2 using 10-fold cross-validation. The proposed ID-BERT-BiTCN outperforms other machine learning (ML) and existing models. These results highlight the model's potential to enhance the effectiveness of CTI by enabling timely and accurate threat detection from social media sources.