Abstract-Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims' computers and requests a ransom payment to reinstantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately system logs can be mined to hunt abnormalities and stop the evil. In this paper we first setup an environment to collect activity logs of 517 Locky ransomware samples, 535 Cerber ransomware samples and 572 samples of TeslaCrypt ransomware. We utilize Sequential Pattern Mining to find Maximal Frequent Patterns (MFP) of activities within different ransomware families as candidate features for classification using J48, Random Forest, Bagging and MLP algorithms. We could achieve 99% accuracy in detecting ransomware instances from goodware samples and 96.5% accuracy in detecting family of a given ransomware sample. Our results indicate usefulness and practicality of applying pattern mining techniques in detection of good features for ransomware hunting. Moreover, we showed existence of distinctive frequent patterns within different ransomware families which can be used for identification of a ransomware sample family for building intelligence about threat actors and threat profile of a given target.

Ransomware, a malware designed to encrypt data for ransom payments, is a threat to fog layer nodes as such nodes typically contain considerably amount of sensitive data. The capability to efficiently hunt abnormalities relating to ransomware activities is crucial in timely detection of ransomware. In this paper, we present our Deep Ransomware Threat Hunting and Intelligence System (DRTHIS) to distinguish ransomware from goodware and identify their families. Specifically, DRTHIS utilizes Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), two deep learning techniques, for classification using the softmax algorithm. We then use 220 Locky, 220 Cerber and 220 TeslaCrypt ransomware samples, and 219 goodware samples, to train DRTHIS. Findings from our evaluations demonstrate that the proposed system achieves an F-measure of 99.6% with a true positive rate of 97.2% in the classification of ransomware instances. Additionally, we demonstrate that DRTHIS is capable of detecting previously unseen ransomware samples from new ransomware families in a timely and accurate manner using ransomware from the CryptoWall, TorrentLocker and Sage families. The findings show that 99% of CryptoWall samples, 75% of TorrentLocker samples and 92% of Sage samples are correctly classified.

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In this paper a marking system for Java programming is presented which has been developed as a plug-in for a widely used editor, Eclipse. This system runs student submitted programs against previously defined test cases. Depending on the percentage of correct running code, a proper mark is awarded. Since this program was implemented in order to be used in a principles of programming course, we require students to practice coding with a correct style. Therefore, this system checks the style of the code and produces messages when a better style is expected. In some cases penalty marks are considered for improper code style. For this system to play an educational role, we allow students to submit more than once. With this we aim to help them learn from their mistakes. The number of submissions differs from one exercise to another and is defined dynamically by our system administration.We call this system JavaMarker.