Proposed Hybrid White Shark Beluga Optimization Algorithm for Hyperparameter Fine-Tuning in Malware Detection

Malware detection has taken a significant leap forward with the implementation of sparse attention combined with residual pyramidal depth-wise separable convolutional based malware detection. This cutting-edge approach utilizes optimization mechanisms to effectively detect and predict malware classes by converting API calls into 2D grayscale images. The process involves multiple stages including pre-processing, feature extraction, and malware detection.

In the pre-processing stage, API calls are converted into grayscale images by extracting files and saving them into a byte matrix. The grayscale representation is achieved by transforming byte values into pixel values ranging from 0 to 255. The removal of noise in the images is done through weighted mean and anisotropic filters to enhance the accuracy of feature extraction.

Feature extraction is carried out using the Ef-DeSMob2 model, which integrates dense networks with MobileNetv2 and squeeze excitation blocks to extract relevant features from the images. The dense network structure allows for efficient information flow, while MobileNetv2 ensures computational efficiency in image classification tasks.

The malware detection phase employs sparse attention with residual pyramidal depth-wise separable convolutional neural networks to accurately categorize malicious software. This approach addresses the challenges of high-dimensional data and diverse behavioral patterns exhibited by malware. The pyramidal design enhances the model’s ability to learn multi-scale representations, while depth-wise separable convolutions ensure computational efficiency.

The hyperparameters are fine-tuned using the Hybrid White Shark Beluga Optimization Algorithm, which combines the behaviors of beluga whales and white sharks to optimize the detection process. The algorithm leverages swarm intelligence to solve optimization problems efficiently.

Overall, this advanced methodology offers a robust and scalable approach to malware detection, improving accuracy and efficiency while adapting rapidly to new threats. The integration of cutting-edge technologies in each stage of the process ensures a comprehensive and effective solution for detecting malware in complex cybersecurity environments.