Comprehensive Classification and Analysis of Malware Samples Using Feature Selection and Bayesian Optimized Logistic Regression for Cybersecurity Applications

Abstract

Cyberattacks are serious threats not only to individuals but also to corporations due to their rising frequency and financial impact. Malware is the main tool of cybercriminals, and is always changing, making its detection and mitigation more complicated. To counter these threats, this work proposes a Logistic Regression approach that is based on Bayesian Optimization. By leveraging advanced techniques like a hybrid feature selection model, the study enhances malware detection and classification accuracy and efficiency. Bayesian Optimization fine-tunes the logistic regression model's hyperparameters, improving performance in identifying malware. The integration of a hybrid feature selection algorithm reduces dataset dimensionality, focusing on relevant features for more accurate classification and efficient resource use, which is suitable for real-time applications. The experimental results show amazing accuracy rates of 99.94% for the Ransomware Dataset and 99.98% on the CIC-Obfuscated Malware dataset. This proposed model performs better than the conventional detection techniques. With its flexible feature selection and optimization techniques, it can keep pace with the dynamic landscape of cyber threats. It, therefore, produces a robust and scalable answer to the current cybersecurity issues.