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This paper addresses one aspect of the problem of defending Mobile Ad-Hoc Networks (MANETs) against computer attacks, namely, the development of a distributed anomalybased intrusion detection system. In a general sense, the proposed system is a co-located sensor network, in which the monitored variable is the health of the network being monitored. A three level hierarchical system for data collection, processing and transmission is described. Local IDSs (Intrusion Detection Systems) are attached to each node of the MANET, collecting raw data of network operation, and computing a local anomaly index measuring the difference between the current node operation and a baseline of normal operation. Anomaly indexes from nodes belonging to a cluster are periodically transmitted to a cluster head, which fuses the node indexes producing a cluster-level anomaly index. Likewise, cluster heads periodically transmit these cluster-level anomaly indexes to a manager node, which fuses the cluster-level indexes into a network-level anomaly index. Due to network mobility, cluster membership and cluster heads are time varying. The paper describes: (1) Clustering algorithms to update cluster centers; (2) Machine Learning algorithms for computing the local anomaly indexes; (3) A statistical scheme for fusing the anomaly indexes at the cluster heads and at the manager. The statistical scheme is formally shown to increase detection accuracy under idealized assumptions. These algorithms were implemented and tested under the following conditions. Routing schemes: AODV (Ad-Hoc On Demand Distance Vector Routing) and OLSR (Optimized Link State Routing); Mobility patterns: Random Walk Mobility Model and Reference Point Group Mobility at various speeds; Types of attacks: Traffic flooding Denial-of-Service and Black Hole. For Performance Evaluation we determined the ROC (Receiver Operating Characteristics) for various operational conditions at the nodes, cluster heads and manager. The overall results confirm the effectiveness of the infrastructures and algorithms described in the paper, with detection accuracy generally improving as we move up in the hierarchy, i.e. detection accuracy at the cluster level is higher than at local level, while network-level detection outperforms clusterlevel detection.

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We report on the implementation and features of the Brain/MINDS Marmoset Connectivity Atlas, BMCA, a new resource that provides access to anterograde neuronal tracer data in the prefrontal cortex of a marmoset brain. Neuronal tracers combined with fluorescence microscopy are a key technology for the systematic mapping of structural brain connectivity. We selected the prefrontal cortex for mapping due to its important role in higher brain functions. This work introduces the BMCA standard image preprocessing pipeline and tools for exploring and reviewing the data. We developed the BMCA-Explorer, which is an online image viewer designed for data exploration. Unlike other existing image explorers, it visualizes the data of different individuals in a common reference space at an unprecedented high resolution, facilitating comparative studies. To foster the integration with other marmoset brain image databases and cross-species comparisons, we added fiber tractography data from diffusion MRI, retrograde neural tracer data from the Marmoset Brain Connectivity Atlas project, and tools to map image data between marmoset and the human brain image space. This version of BMCA allows direct comparison between the results of 52 anterograde and 164 retrograde tracer injections in the cortex of the marmoset.