OMD-RAS: Optimizing Malware Detection through Comprehensive Approach to Real-Time and Adaptive Security

Malware continues to pose a significant threat to cybersecurity, with new advanced infections that go beyond traditional detection. Limitations in existing systems include high false-positive rates, slow system response times, and inability to respond quickly to new malware forms. To overcome these challenges, this paper proposes OMD-RAS: Implementing Malware Detection in an Optimized Way through Real-Time and Adaptive Security as an extensive approach, hoping to get good results towards better malware threat detection and remediation. The significant steps in the model are data collection followed by comprehensive preprocessing consisting of feature engineering and normalization. Static analysis, along with dynamic analysis, is done to capture the whole spectrum of malware behavior for the feature extraction process. The extracted processed features are given with a continuous learning mechanism to the Extreme Learning Machine model of real-time detection. This OMD-RAS trains quickly and has great accuracy, providing elite, advanced real-time detection capabilities. This approach uses continuous learning to adapt to new threats—ensuring the effectiveness of detection even as strategies used by malware may change over time. The experimental results showed that OMD-RAS performs better than the traditional approaches. For instance, the OMD-RAS model has been able to achieve an accuracy of 96.23% and massively reduce the rate of false positives across all datasets while eliciting a consistently high rate of precision and recall. The model’s adaptive learning reflected enhancements on other performance measures—for example, Matthews Correlation Coefficients and Log Loss.