An Optimized Deep Learning Framework for Malware Classification Using Integrated LSTM and CNN Approaches

The swift expansion of computer networks has resulted in security challenges, particularly with the abuse of encryption technologies. Malicious individuals utilize encryption and Tor to evade detection systems, facilitating unlawful actions. Conventional approaches such as signature-based scanning and heuristic analysis are insufficient against advanced obfuscation strategies. This paper presents a deep learning framework that integrates Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks to enhance the accuracy and reliability of virus detection. The framework additionally integrates sophisticated preprocessing methods such as normalization and feature selection. Drawing on earlier successes, this method expands on previous work where biLSTM models achieved high accuracy in cloud-based malware detection and where LSTM-CNN approaches proved effective for insider threat detection. The proposed model advances these techniques by incorporating a filter-based feature selection mechanism (Kbest) into an LSTM-CNN architecture, optimizing it for the evolving complexities of modern malware detection.The rapid proliferation of computer networks has led to security challenges, especially with the misuse of encryption technologies. Malicious actors employ encryption and Tor to circumvent detection measures, enabling illicit activities. Traditional methods like signature-based scanning and heuristic analysis are inadequate against sophisticated obfuscation techniques. This research proposes an intelligent detection system that leverages the combined strengths of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to boost the accuracy and robustness of virus identification. The system is further enhanced by sophisticated preprocessing steps, including data normalization and feature selection.The study highlights the need for innovative cybersecurity solutions, focusing on improving malware detection capabilities to create more resilient network security infrastructures. Its findings have significant implications for future security system design and ongoing cybercrime battle