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.
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DOI:
10.17148/IJARCCE.2025.14264
