Publications

Abstract : Currently the whole world is going digitalization, using handheld device like smartphones and evolution of Internet, due to pandemic, all the transactions are going online. The security at end devices is an important issue to everyone. We believe that the, data is in transit is more secure, but in reality is not true. The data are in hands of bad actors for malicious activities. Android Ransomware is one of the most widely distributed assaults throughout the world. It is a type of virus that prevents users from accessing the operating system and encrypts essential data saved on their device. The majority of this work focuses on two goals: the first is to offer an introduction of ransomware and machine learning techniques, and the second part focussed on thorough assessment of detection of Android ransomware application using machine learning methods. After a thorough analysis of existing mechanisms of android ransomware detection, we found that the combination of static behaviour analysis of application and machine learning techniques gives good accuracy of android ransomware applications. In this research used, proposed a static based feature selection technique and applied machine learning algorithms for prediction of ransomware applications. For classification, the Decision Tree, Extra Tree classifier, Light Gradient Boosting Machine methods are employed in conjunction with the random forest tree. The dataset used was obtained from Kaggle and consists of 331 Android application permissions, 199 of which are Ransomware. The suggested model outperforms with a detection accuracy of 98.05 percent. Based on its best performance, we believe our suggested approach will be useful in malware and forensic investigation.