Biblio

In recent cyber incidents, Ransom software (ransomware) causes a major threat to the security of computer systems. Consequently, ransomware detection has become a hot topic in computer security. Unfortunately, current signature-based and static detection model is often easily evadable by obfuscation, polymorphism, compress, and encryption. For overcoming the lack of signature-based and static ransomware detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as Random Forest (RF), Support Vector Machine (SVM), Simple Logistic (SL) and Naive Bayes (NB) algorithms for detecting known and unknown ransomware. We monitor the actual (dynamic) behaviors of software to generate API calls flow graphs (CFG) and transfer it in a feature space. Thereafter, data normalization and feature selection were applied to select informative features which are the best for discriminating between various categories of software and benign software. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. Our experimental results show that our proposed system can be more effective to improve the performance for ransomware detection. Especially, the accuracy and detection rate of our proposed system with Simple Logistic (SL) algorithm can achieve to 98.2% and 97.6%, respectively. Meanwhile, the false positive rate also can be reduced to 1.2%.

Radio Frequency Identification (RFID) technology has been applied in many fields, such as tracking product through the supply chains, electronic passport (ePassport), proximity card, etc. Most companies will choose low-cost RFID tags. However, these RFID tags are almost no security mechanism so that criminals can easily clone these tags and get the user permissions. In this paper, we aim at more efficient detection proximity card be cloned and design a real-time intrusion detection system based on one tool of Complex Event Processing (Esper) in the RFID middleware. We will detect the cloned tags through training our system with the user's habits. When detected anomalous behavior which may clone tags have occurred, and then send the notification to user. We discuss the reliability of this intrusion detection system and describes in detail how to work.