Securing Digital Frontiers: A Hybrid LSTM-Transformer Approach for AI-Driven Information Security Frameworks

Abstract

As cyberattacks grow and digital infrastructures spread, having strong and smart information security has become a top priority. This paper introduces a new AI-based framework for improving cybersecurity threat detection in cloud environments that is based on hybrid LSTM-Transformers. Sequential and contextual patterns from network traffic data can be learned by the proposed model by merging the self-attention mechanism of Transformers with the temporal modeling ability of LSTM networks. Training and testing are performed with the CICIDS-2017 dataset, while performance is maximized through impactful preprocessing methods such as feature extraction and normalization. The model thoroughly surpasses simpler deep learning technologies such as CNNs, solitary LSTMs, and Transformers when measured under important performance factors. These performances prove the strength of the hybrid model in further identifying new cyberthreats in addition to those already known but with minimal false positives and optimal precision. As a precursor to future-generation cyber security systems, this work assists in the development of intelligent, scalable, and adaptive intrusion detection systems. Towards enhancing intrusion flexibility, this work introduces a novel hybrid deep model that integrates Transformer models with LSTM networks. Cross-model leverages temporal learning capability of LSTM to learn sequence patterns among network traffic and exploits self-attention mechanism of Transformer to enhance understanding of feature dependencies in order to improve detection of threats. On benchmark datasets such as CICIDS-2017, the proposed framework passes through thorough data preprocessing that involves normalization, feature selection and label encoding.