Analysis and Comparison of P2P Search Methods

The popularity and bandwidth consumption attributed to current Peer-to-Peer file-sharing applications makes the operation of these distributed systems very important for the Internet community. Efficient object discovery is the first step towards the realization of distributed resource-sharing. In this work, we present a detailed overview of recent and existing search methods for unstructured Peer-to-Peer networks. We analyze the performance of the algorithms relative to various metrics, giving emphasis on the success rate, bandwidth-efficiency and adaptation to dynamic network conditions. Simulation results are used to empirically evaluate the behavior of nine representative schemes under a variety of different environments

Analysis and Comparison of P2P Search Methods

The popularity and bandwidth consumption attributed to current Peer-to-Peer file-sharing applications makes the operation of these distributed systems very important for the Internet community. Efficient object discovery is the first step towards the realization of distributed resource-sharing. In this work, we present a detailed overview of recent and existing search methods for unstructured Peer-to-Peer networks. We analyze the performance of the algorithms relative to various metrics, giving emphasis on the success rate, bandwidth-efficiency and adaptation to dynamic network conditions. Simulation results are used to empirically evaluate the behavior of nine representative schemes under a variety of different environments. (UMIACS-TR-2003-107

Storytelling Security: User-Intention Based Traffic Sanitization

Malicious software (malware) with decentralized communication infrastructure, such as peer-to-peer botnets, is difficult to detect. In this paper, we describe a traffic-sanitization method for identifying malware-triggered outbound connections from a personal computer. Our solution correlates user activities with the content of outbound traffic. Our key observation is that user-initiated outbound traffic typically has corresponding human inputs, i.e., keystroke or mouse clicks. Our analysis on the causal relations between user inputs and packet payload enables the efficient enforcement of the inter-packet dependency at the application level. We formalize our approach within the framework of protocol-state machine. We define new application-level traffic-sanitization policies that enforce the inter-packet dependencies. The dependency is derived from the transitions among protocol states that involve both user actions and network events. We refer to our methodology as storytelling security. We demonstrate a concrete realization of our methodology in the context of peer-to-peer file-sharing application, describe its use in blocking traffic of P2P bots on a host. We implement and evaluate our prototype in Windows operating system in both online and offline deployment settings. Our experimental evaluation along with case studies of real-world P2P applications demonstrates the feasibility of verifying the inter-packet dependencies. Our deep packet inspection incurs overhead on the outbound network flow. Our solution can also be used as an offline collect-and-analyze tool

Effectiveness of landmark analysis for establishing locality in p2p networks

Locality to other nodes on a peer-to-peer overlay network can be established by means of a set of landmarks shared among the participating nodes. Each node independently collects a set of latency measures to landmark nodes, which are used as a multi-dimensional feature vector. Each peer node uses the feature vector to generate a unique scalar index which is correlated to its topological locality. A popular dimensionality reduction technique is the space filling Hilbert’s curve, as it possesses good locality preserving properties. However, there exists little comparison between Hilbert’s curve and other techniques for dimensionality reduction. This work carries out a quantitative analysis of their properties. Linear and non-linear techniques for scaling the landmark vectors to a single dimension are investigated. Hilbert’s curve, Sammon’s mapping and Principal Component Analysis have been used to generate a 1d space with locality preserving properties. This work provides empirical evidence to support the use of Hilbert’s curve in the context of locality preservation when generating peer identifiers by means of landmark vector analysis. A comparative analysis is carried out with an artificial 2d network model and with a realistic network topology model with a typical power-law distribution of node connectivity in the Internet. Nearest neighbour analysis confirms Hilbert’s curve to be very effective in both artificial and realistic network topologies. Nevertheless, the results in the realistic network model show that there is scope for improvements and better techniques to preserve locality information are required