Android Malware Detection Using Artificial Intelligence

Malware poses a significant global cybersecurity challenge, targeting individuals, businesses, institutions, and nations by compromising sensitive information and causing disruptions, incurring substantial costs. Android devices, with relatively lower security measures allowing installations from unknown sources, face notable malware prevalence, creating opportunities for cybercriminals to engage in illicit activities. To address this issue, numerous research studies have focused on harnessing the power of artificial intelligence (AI) to develop effective solutions. Notably, research utilizing the CICMalDroid2020 dataset has achieved promising results by employing Deep Learning and Machine Learning approaches for Android malware detection. However, to the best of our knowledge, no prior studies utilizing this dataset have explored the potential of the Extra-Tree Machine Learning classifier. In our research, we endeavored to fill this gap by implementing the Extra Tree classifier in conjunction with cross-validation techniques. Additionally, we employed the SelectFrom-Model feature selection method to enhance the accuracy of malware detection. Through our investigation, we found that the ExtraTree classifier exhibited good performances, achieving an accuracy rate of 96.7%.