SUMMARYRadio-frequency identification (RFID) technology enables the identification and tracking of objects by means of the wireless signals emitted by a tag attached to the objects of interest. Without adequate protection, however, malicious attackers can easily eavesdrop, scan or forge the information within the tag, thereby threatening the integrity of the system. Previous research has shown that the basic security requirements of RFID systems, i.e. identity authentication, information privacy and location privacy, can be satisfied using conventional cryptographic components. However, such components are expensive, and therefore conflict with the general requirement for low-cost tag designs. Accordingly, this paper presents a low-cost challenge-response security protocol designated as the hidden mutual authentication protocol (HMAP) to accomplish both a mutual authentication capability between the tag and the reader and information privacy. The results show that HMAP provides an efficient means of concealing the authentication messages exchanged between the tag and the reader and is robust toward replay attacks. In addition, it is shown that HMAP is easily extended to provide complete location privacy by utilizing a hash function to generate different tag identifiers in each authentication session.

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SUMMARYThe current network-based intrusion detection systems have a very high rate of false alarms, and this phenomena results in significant efforts to gauge the threat level of the anomalous traffic. In this paper, we propose an intrusion detection mechanism based on honeypot log similarity analysis and data mining techniques to predict and block suspicious flows before attacks occur. With honeypot logs and association rule mining, our approach can reduce the false alarm problem of intrusion detection because only suspicious traffic would be present in the honeypots. The proposed mechanism can reduce human effort, and the entire system can operate automatically. The results of our experiments indicate that the honeypot prediction system is practical for protecting assets from attacks or misuse.

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