Robust Intelligence Malware Detection Using Deep Learning
Malware remains a significant security concern in today's digital landscape, with traditional detection methods often proving ineffective against evolving threats. Recent approaches leverage machine learning algorithms, particularly deep learning, to analyze malware effectively. However, existing research is often biased due to training data limitations. To address this, this study evaluates classical machine learning and deep learning models for malware detection using diverse datasets. A novel image processing technique is also proposed to enhance detection accuracy. Results show deep learning outperforming traditional methods, paving the way for scalable and real-time malware detection systems.
Robust Intelligence Malware Detection Using Deep Learning Malware remains a significant security concern in today's digital landscape, with traditional detection methods often proving ineffective against evolving threats. Recent approaches leverage machine learning algorithms, particularly deep learning, to analyze malware effectively. However, existing research is often biased due to training data limitations. To address this, this study evaluates classical machine learning and deep learning models for malware detection using diverse datasets. A novel image processing technique is also proposed to enhance detection accuracy. Results show deep learning outperforming traditional methods, paving the way for scalable and real-time malware detection systems.
![Screenshot 2024-06-24 114532](https://github.com/charann29/cmr_opensource/assets/173147775/218b79df-cd19-46a4-ad89-9b5bf78713bf)