PoK

Author:Cliff Wang,An Liu, Peng Ning

Description:
To support a wide variety of applications ranging from military surveillance to health care clinic monitoring, a wireless sensor network must obtain accurate location for each sensor. A number of localization schemes have been developed to allow each sensor node to acquire its location.However, most of these techniques assume benign environments,and thus cannot survive malicious attacks in hostile environments where external and/or compromised nodes may launch attacks. This paper proposes a new computationally efficient and resilient localization scheme based on the clustering of benign location reference anchors. Moreover, this paper reports both simulation and field experiments using a test bed of MICAz motes performed to compare the proposed approach with several recent secure localization schemes. The experimental results demonstrate that the proposed scheme has the fastest execution time among all resilient localization schemes that can be used for the current generation of sensor platforms (e.g., MICA series of motes)

Author:Donggang Liu,Peng Ning, An Liu,Cliff Wang,Wenliang Kevin Du

Description:
Many sensor network applications require sensors’ locations to function correctly. Despite the recent advances, location discovery for sensor networks in hostile environments has been mostly overlooked. Most of the existing localization protocols for sensor networks are vulnerable in hostile environments. The security of location discovery can certainly be enhanced by authentication. However, the possible node compromises and the fact that location determination uses certain physical features (e.g., received signal strength) of radio signals make authentication not as effective as in traditional security applications. This paper presents two methods to tolerate malicious attacks against range based location discovery in sensor networks. The first method filters out malicious beacon signals on the basis of the “consistency” among multiple beacon signals, while the second method tolerates malicious beacon signals by adopting an iteratively refined voting scheme. Both methods can survive malicious attacks even if the attacks bypass authentication,provided that the benign beacon signals constitute the majority of the beacon signals. This paper also presents the implementation and experimental evaluation (through both field experiments and simulation) of all the secure and resilient location estimation schemes that can be used on the current generation of sensor platforms (e.g., MICA series of motes), including the techniques proposed in this paper, in a network of MICAz motes. The experimental results demonstrate the effectiveness of the proposed methods, and also give the secure and resilient location estimation scheme most suitalbe for the current generation of sensor networks.

Author:Klaus Dieter Althoff, Ralph Bergmann, Stefan Wess, Michel Manago, Eric Auriol, Oleg. I. Larichev, Alexander Bolotov, Yurii I. Zhuravlev, Serge I. Gurov

Description:
We describe an approach for developing knowledge based medical decision support systems based on the rather new technology of case-based reasoning. This work is based on the results of the Inreca European project and preliminary results from the Inreca project which particularly deals with medical applications. One goal was to start from case-based reasoning technology for technical diagnosis, as it was available among the partners, and ‘scale-up’ to more general non-technical decision support tasks as typically given in medical domains. Inreca technology is used to build an initial decision support system at the Russian Toxicology Information and Advisory Center in Moscow for diagnosing poison cases that are caused by psychotropes

Author:Praveen Pathak, Michael Gordon, Weiguo Fan

Description:
Knowledge intensive organizations have vast array of information contained in large document repositories. With the advent of E-commerce and corporate intranets/extranets, these repositories are expected to grow at a fast pace. This explosive growth has led to huge, fragmented, and unstructured document collections. Although it has become easier to collect and store information in document collections, it has become increasingly difficult to retrieve relevant information from these large document collections. This paper addresses the issue of improving retrieval performance (in terms of precision and recall) for retrieval from document collections.There are three important paradigms of research in the area of information retrieval (IR): Probabilistic IR, Knowledge-based IR, and, Artificial Intelligence based techniques like neural networks and symbolic learning. Very few researchers have tried to use evolutionary algorithms like genetic algorithms (GA’s). Previous attempts at using GA’s have concentrated on modifying document representations or modifying query representations. This work looks at the possibility of applying GA’s to dapt various matching functions. It is hoped that such an adaptation of the matching functions will lead to a better retrieval performance than that obtained by using a single matching function. An overall matching function is treated as a weighted combination of scores produced by individual matching functions. This overall score is used to rank and retrieve documents. Weights associated with individual functions are searched using Genetic Algorithm.

Author:Yan Zhai, Peng Ning, Purush Iyer, Douglas S. Reeves

Description:
This paper presents techniques to integrate and reason about complementary intrusion evidence such as alerts generated by intrusion detection systems (IDSs) and reports by system monitoring or vulnerability scanning tools. To facilitate the modeling of intrusion evidence, this paper classifies intrusion evidence into either event-based evidence or state-based evidence. Event-based evidence refers to observations (or detections) of intrusive actions (e.g., IDS alerts),while state-based evidence refers to observations of the effects of intrusions on system states. Based on the interdependency between event-based and state-based evidence, this paper develops techniques to automatically integrate complementary evidence into Bayesian networks, and reasonabout uncertain or unknown intrusion evidence based on verified evidence. The experimental results in this paper demonstrate the potential of the proposed techniques. In particular, additional observations by system monitoring or vulnerability scanning tools can potentially reduce the false alert rate and increase the confidence in alerts corresponding to successful attacks.