Supported by maturing information and communication technology (ICT) technologies, the medical care industry entered its digitization age. However, medical information is highly confidential and involves privacy; even legitimate access can cause privacy infringements. Under the current medical administration system, no established application systems conform to the IRB (Institutional review board) to protect privacy in human subject research. To prevent the privacy of research subjects from being disclosed unintentionally, and to meet the “need-to-know” demands of information management, this study proposes an intelligent privacy-preserving administration tool (iPAT) designed to facilitate human subject research. This study provides the principle and feasibility of iPAT that are introduced and verified through a use case conducted by the head of the research project.

Current intrusion detection systems (IDSs) can only discover single-step attacks but not complicated multi-stage attacks. Therefore, it is not only important, but also challenging for security managers to correlate security alerts with specific patterns to predict a multi-stage attack. In this paper, we propose Judge Evaluation of Attack intensioN (JEAN), which inspects the security alerts in the network and provides a probabilistic approach for the projection of the multi-stage attack by measuring the difference between the stored and the actual multi-stage attack session graphs (ASG). The experimental results show that JEAN is able to project possible attacks with more accuracy than Longest Common Subsequence (LCS) based approaches on DARPA 2000 and DARPA GCP (Grand Challenge Problem) specific attack scenario datasets.

Abstract With limited resource constraints, wireless sensor networks (WSNs) pose unique technical challenges: WSNs are vulnerable to DoS/DDoS attacks that can easily exhaust rare available resources to prevent execution of their expected functions. Reconstructing the attacking path and locating the attacking source are challenging tasks in the traceback research areas. In conventional IP networks, probabilistic packet marking (PPM) schemes are among the widely used traceback algorithms. However, due to their high convergence times, conventional PPM schemes are not enough for the need for quick and accurate traceback in WSNs. Because marking probability assignment schemes have considerable influence on convergence time and performance, we place a particular emphasis on how to improve both easiness and efficiency for the marking probability assignment of the PPM. In this paper, we propose a novel traceback scheme, called fishbone traceback (FBT), which can be deployed in hierarchical WSN environments. FBT is designed based on the two‐layer labelling technique and a smart marking probability distribution function (MPDF). The use of two‐layer FBT labels is to derive the main branch (‘fish spine’) of the attacking path quickly, while the use of MPDF can greatly reduce the convergence time by integrating witha prioriinformation of hierarchical WSN topology. The FBT path reconstruction procedure is able to rebuild the spine path (for inter‐cluster traceback)viacluster head marking packets. It also reforms the details of the micro fishbone path (for intra‐cluster traceback) on‐demand within a cluster. Both numerical analysis and simulation results show that our solution has better performance in terms of shorter traceback convergence time. In particular, the proposed FBT also includes many salient features (such as the enhanced robustness of the traceback algorithm in case of multi‐attack and reusable spine path), which enable FBT to be a practical solution to the traceback problem in hierarchical WSNs. Copyright © 2008 John Wiley & Sons, Ltd.