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In unstructured peer-to-peer (P2P)

Abstract-Superpeer unstructured P2P systems have been found to be very effective by dividing the peers into two layers, superlayer and leaf-layer, in which message flooding is only conducted among superlayer and all leaf-peers are represented by corresponding superpeers. However, current superpeer systems do not employ any effective layer management schemes, so the transient and lowcapacity peers are allowed to act as superpeers. Moreover, the lack of an appropriate size ratio maintenance mechanism on superlayer to leaf-layer makes the system's search performance far from being optimal. We present one workload model aimed at reducing the weighted overhead of a network. Using our proposed workload model, a network can determine an optimal layer size ratio between leaf-layer and superlayer. We then propose a Dynamic Layer Management algorithm, DLM, which can maintain an optimal layer size ratio and adaptively elect and adjust peers between superlayer and leaf-layer. DLM is completely distributed in the sense that each peer decides to be a superpeer or a leaf-peer independently without global knowledge. DLM could effectively help a superpeer P2P system maintain the optimal layer size ratio and designate peers with relatively long lifetime and large capacities as superpeers, and the peers with short lifetime and low capacities as leaf-peers under highly dynamic network situations. We demonstrate that the quality of a superpeer system is significantly improved under the DLM scheme by comprehensive simulations.

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Abstract:The wide spread of worms poses serious challenges to today's Internet. Various IDSes (Intrusion Detection Systems) have been proposed to identify or prevent such spread. These IDSes can be largely classified as signature-based or anomaly-based ones depending on what type of knowledge the system knows. Signature-based IDSes are unable to detect the outbreak of new and unidentified worms when the worms' characteristic patterns are unknown. In addition, new worms are often sufficiently intelligent to hide their activities and evade anomaly detection. Moreover, modern worms tend to spread more quickly, and the outbreak period lasts in the order of hours or even minutes. Such characteristics render existing detection mechanisms less effective.In this work, we consider the drawbacks of current detection approaches and propose PAIDS, a proximity-assisted IDS approach for identifying the outbreak of unknown worms. PAIDS does not rely on signatures. Instead, it takes advantage of the proximity information of compromised hosts. PAIDS operates on an orthogonal dimension with existing IDS approaches and can thus work collaboratively with existing IDSes to achieve better performance. We test the effectiveness of PAIDS with trace-driven simulations and observe that PAIDS has a high detection rate and a low false positive rate. We also build a proof-of-concept prototype using Google Maps APIs and libpcap library.

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