Abstract: Malware detection plays a crucial role in cybersecurity by identifying and mitigating threats posed by malicious software. Traditional detection methods rely heavily on signature-based approaches, which are often ineffective against new, evolving malware. This paper presents a deep learning-based model for malware detection, leveraging advanced neural network architectures to classify files as either benign or malicious based on their characteristics. By training the model on a comprehensive dataset, it learns to identify subtle patterns that distinguish harmful files from legitimate ones. Enhancing the accessibility and usability of the detection system, the model is integrated into a web-based interface where users can upload files and receive real-time analysis results. Experimental results demonstrate the effectiveness of the deep learning approach in achieving high accuracy and detection speed, showcasing its potential as a proactive tool for modern cybersecurity defence.

Keywords: Malware detection, Deep Learning, Cyber Security, Neural Networks, Malicious files.


Downloads: PDF | DOI: 10.17148/IJARCCE.2025.14264

How to Cite:

[1] Mr. O.T Gopi Krishna, D. Dheeraj Sai, V. Manohar Naidu, L. Deepthi Sai Archana, B. Rakesh Babu, "Advanced Malware Detection Using Deep Learning in EDR System," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.14264

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