Malware Detection and Classification on Different Dataset by Hybridization of CNN and Machine Learning - Hashmi , S. Arshad (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Malware has long been employed in cyberattacks. Due to their widespread usage, malicious software developers target Android smartphones, which may store a lot of sensitive data. As the main mobile OS, Android has always attracted malware developers. Thus, several Android malware species target susceptible people everyday, making manual malware analysis unfeasible. ML and DL methods for malware identification and categorization might help cyber forensic investigators curb the spread of malicious software. Applying DL methods helps safeguard applications. Cybersecurity issues including intrusion detection, malware classification and identification, phishing and spam detection, and spam recognition have been addressed using DL approaches. ECNN uses the BP (Back Propagation) model for every layer between several intermediate layers, making it faster and more accurate than other methods. SVM Learning with Weighted Features and CNN with SGD optimization for static analysis of mobile apps are presented in this research. The ECNN model has the highest accuracy of 96.92, 96.14, and 95.8 for Android Malware Dataset-1, 2, and 3. On the three datasets, the ECNN model has 96%, 94%, and 94% precision. Smartphone malware analysis is faster and more accurate using this method.