Security risks/vulnerability in a RFID system and possible defenses

Remote technologies are changing our way of life. The radio frequency identification (RFJD) system is a new technology which uses the open air to transmit information. This information transmission needs to be protected to provide user safety and privacy. Business will look for a 5ystem that hasfraud resilience to prevent the misuse of information to take dishonest advantage. The business and the user need to be assured that the transmitted information has no content which is capable of undertaking malicious activities. Public awareness of RFID security will help users and organizations to understand the need for security protection. Publishing a security guideline from the regulating body and monitoring implementation of that guideline in RFID 5ystems will ensure that businesses and users are protected. This chapter explains the importance of security in a RFID system and will outline the protective measures. It also points out the research direction of RFID 5ystems.

Performance evaluation of multi-tier ensemble classifiers for phishing websites

This article is devoted to large multi-tier ensemble classifiers generated as ensembles of ensembles and applied to phishing websites. Our new ensemble construction is a special case of the general and productive multi-tier approach well known in information security. Many efficient multi-tier classifiers have been considered in the literature. Our new contribution is in generating new large systems as ensembles of ensembles by linking a top-tier ensemble to another middletier ensemble instead of a base classifier so that the top~ tier ensemble can generate the whole system. This automatic generation capability includes many large ensemble classifiers in two tiers simultaneously and automatically combines them into one hierarchical unified system so that one ensemble is an integral part of another one. This new construction makes it easy to set up and run such large systems. The present article concentrates on the investigation of performance of these new multi~tier ensembles for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of multi~level ensembles with three tiers. The results presented here demonstrate that new three-tier ensemble classifiers performed better than the base classifiers and standard ensembles included in the system. This example of application to the classification of phishing websites shows that the new method of combining diverse ensemble techniques into a unified hierarchical three-tier ensemble can be applied to increase the performance of classifiers in situations where data can be processed on a large computer.

Unsupervised DRG upcoding detection in healthcare databases

Diagnosis Related Group (DRG) upcoding is an anomaly in healthcare data that costs hundreds of millions of dollars in many developed countries. DRG upcoding is typically detected through resource intensive auditing. As supervised modeling of DRG upcoding is severely constrained by scope and timeliness of past audit data, we propose in this paper an unsupervised algorithm to filter data for potential identification of DRG upcoding. The algorithm has been applied to a hip replacement/revision dataset and a heart-attack dataset. The results are consistent with the assumptions held by domain experts.