Enhancing Malware Detection through Electromagnetic Side-Channel Analysis Using Random Forest Classifier

The continual increase of cyber dangers necessitates creative techniques to better the identification and mitigation of malware. This research provides a cutting-edge examination of employing the Random Forest Classifier in combination with electromagnetic side-channel analysis for finding malicious software. Electromagnetic side-channel analysis harnesses the accidental information leakage from electronic systems, giving it a formidable tool for studying the underlying workings of gadgets. This study reveals how these electromagnetic side-channel signals may be used to identify subtle and evasive malware activities. The paper goes into the theoretical basis of electromagnetic side-channel analysis and the actual application of the Random Forest Classifier in this setting. By analyzing electromagnetic emissions, a wide range of devices and systems can be scrutinized for the telltale signs of malware-induced behaviors. Experimental results illustrate the effectiveness of this approach, showcasing the model demonstrated high accuracy, with an accuracy rate of up to 97%, demonstrating its ability to effectively leverage electromagnetic side-channel information for malicious program detection for enhanced cybersecurity measures.