Enhanced DGA Detection in BotNet Traffic: Leveraging N-Gram, Topic Modeling and Attention BiLSTM

Abstract

Abstract This abstract introduces a novel approach for detecting Domain Generation Algorithm (DGA) in BotNet traffic through the integration of N-Gram analysis, Topic Modeling, and Attention-based Bidirectional Long Short-Term Memory (BiLSTM) networks. The escalating sophistication of cyber threats necessitates advanced methods to identify malicious activities, particularly those involving DGAs in BotNet communication. The proposed model begins with N-Gram analysis, capturing sequential patterns in domain names, thereby enhancing the detection of algorithmically generated domains. Topic Modeling is employed to extract latent themes within the network traffic data, providing a deeper understanding of the semantic context associated with potentially malicious domains. To harness the contextual nuances, an Attention mechanism is integrated into a BiLSTM network, allowing the model to selectively focus on critical segments of the input data. This attention-driven BiLSTM network proves effective in capturing long-range dependencies and intricate temporal dynamics inherent in BotNet communication. Experimental evaluations on diverse datasets demonstrate the efficacy of the proposed approach in outperforming existing methods, showcasing its ability to adapt to evolving adversarial strategies. The fusion of N-Gram, Topic Modeling, and Attention BiLSTM offers a comprehensive solution for DGA detection, providing a robust defense against sophisticated cyber threats in the continually evolving landscape of network security. This research contributes to advancing the field of intrusion detection and cyber threat mitigation by presenting a holistic and adaptive approach tailored to the challenges posed by modern BotNet traffic.