Ransomware detection using process mining and classification algorithms The fast growing of ransomware attacks has become a serious threat for companies, governments and internet users, in recent years. The increasing of computing power, memory and etc. and the advance in cryptography has caused the complicating the ransomware attacks. Therefore, effective methods are required to deal with ransomwares. Although, there are many methods proposed for ransomware detection, but these methods are inefficient in detection ransomwares, and more researches are still required in this field. In this paper, we have proposed a novel method for identify ransomware from benign software using process mining methods. The proposed method uses process mining to discover the process model from the events logs, and then extracts features from this process model and using these features and classification algorithms to classify ransomwares. This paper shows that the use of classification algorithms along with the process mining can be suitable to identify ransomware. The accuracy and performance of our proposed method is evaluated using a study of 21 ransomware families and some benign samples. The results show j48 and random forest algorithms have the best accuracy in our method and can achieve to 95% accuracy in detecting ransomwares.

Analysis of Machine Learning Techniques for Ransomware Detection In parallel with the increasing growth of the Internet and computer networks, the number of malwares has been increasing every day. Today, one of the newest attacks and the biggest threats in cybersecurity is ransomware. The effectiveness of applying machine learning techniques for malware detection has been explored in much scientific research, however, there is few studies focused on machine learningbased ransomware detection. In this paper, the effectiveness of ransomware detection using machine learning methods applied to CICAndMal2017 dataset is examined in two experiments. First, the classifiers are trained on a single dataset containing different types of ransomware. Second, different classifiers are trained on datasets of 10 ransomware families distinctly. Our findings imply that in both experiments random forest outperforms other tested classifiers and the performance of the classifiers are not changed significantly when they are trained on each family distinctly. Therefore, the random forest classification method is very effective in ransomware detection.