Visible to the public Peer to Peer Systems

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Peer to Peer Systems

In a peer-to-peer (P2P) network, tasks such as searching for files or streaming audio or video are shared among multiple interconnected nodes--peers-- who share resources with other network participants without the need for centralized coordination by servers. Peer-to-peer systems pose considerable challenges for computer security. Like other forms of software, P2P applications can contain vulnerabilities, but what makes security particularly dangerous for P2P software is that peer-to-peer applications act as servers as well as clients, making them more vulnerable to remote exploits. The research articles in this bibliography address such topics as a large scale overlay network, unstructured networks, mobile streaming, bit torrent, and traffic identification.

  • "Capturing Connectivity Graphs of a Large-Scale P2P Overlay Network," Salah, H.; Strufe, T., Distributed Computing Systems Workshops (ICDCSW), 2013 IEEE 33rd International Conference on , vol., no., pp.172,177, 8-11 July 2013. (ID#:14-1164) Available at: According to the authors, measuring accurate graph snapshots of peer-to-peer (P2P) overlay networks is essential to understand these systems. Furthermore, the captured graph snapshots can be used, among other important purposes, as traces for simulation studies, to validate existing simulation models, to design and implement targeted attacks, or to detect anomalies. Motivated by the importance of the purposes above as well as the popularity of several Kademlia-like networks, they present a new crawler aiming to capture snapshots of the connectivity graph of the entire KAD network. The crawler's design is generic and adaptable for Kademlia-like and other structured P2P networks. The results show that the crawler is fast and captures high accurate graph snapshots. Furthermore, its design enables it to outperform prior KAD crawlers significantly in terms of the time and the number of crawling messages that are required to download nodes' routing tables. The crawls they conducted at different times between April 2012 and February 2013 show that KAD is still widely-used in terms of total observed users. However, when compared to the results of prior studies, they report a significant drop in the number of its simultaneous online users.
  • "An adaptive membership protocol against sybil attack in unstructured P2P networks," Haowen Liu; Chao Ma; Walshe, R., Information and Communications Technologies (IETICT 2013), IET International Conference on , vol., no., pp.29,34, 27-29 April 2013. (ID#:14-1165) Available at: This paper proposes a solution protocol to combat Sybil, a network attack often found in Peer-to-Peer (P2P) networks, which is difficult to defend due to the anonymous nature of the P2P architecture. In a Sybil attack, a malicious user distributes fake nodes throughout a system of legitimate nodes, enabling the attacker to control a large portion of the system and carry out various malicious exploits, such as DDoS attacks. The protocol proposed by this paper accommodates file sharing in an unstructured P2P architecture, and utilizes communication among peers, as well as neighbor monitoring to secure against malicious nodes.
  • "Neighbour peer selection scheme based on effective capacity for mobile Peer-to-Peer streaming," Hailun, Xia; Ning, Wang; Zhimin, Zeng, Communications China , vol.10, no.5, pp.89,98, May 2013. (ID#:14-1166) Available at: The appropriate selection of neighbor peers within a Peer to Peer (P2P) mobile network is discussed in this paper, with consideration of streaming and improving Quality of Service (QoS). Using the Multiple Attribute Decision Making theory (MADM), this paper details a scheme called Effective Capacity Peer Selection (ECPS), which is designed to improve throughout and packet delivery time.
  • "Exploring and improving BitTorrent topologies," Decker, C.; Eidenbenz, R.; Wattenhofer, R., Peer-to-Peer Computing (P2P), 2013 IEEE Thirteenth International Conference on , vol., no., pp.1,10, 9-11 Sept. 2013. (ID#:14-1167) Available at: BitTorrent, the most popular peer-to-peer (P2P) file-sharing protocol, accounts for a significant fraction of the traffic of the Internet. Using a novel technique, the authors measure live BitTorrent swarms on the Internet and confirm the conjecture that overlay networks formed by BitTorrent are not locality-aware, i.e., they include many unnecessary long distance connections. Attempts to improve the locality have failed because they require a modification of the existing protocol, or interventions by Internet service providers (ISPs). In contrast, the authors propose a lightweight method that improves the locality of active swarms by 6% by suggesting geographically close peers with the Peer Exchange Protocol (PEX), without any modifications to the current system. An improvement of locality not only benefits the ISPs by reducing network transit cost, it also reduces the traffic over long-distance connections, which delays the need to expand the infrastructure, easing the power consumption. They expect that if used on a large scale our method reduces the Internet's energy consumption by 8 TWh a year.
  • "P2P traffic identification based on transfer learning," Cai, Lin; Jing, Xiaojun; Sun, Songlin; Huang, Hai; Chen, Na; Lu, Yueming, Granular Computing (GrC), 2013 IEEE International Conference on , vol., no., pp.22,26, 13-15 Dec. 2013. (ID#:14-1168) Available at: With the rapid development of the Internet, a large number of peer networks (Peer-to-Peer) applications rise and are widely used. Because of this, it is more difficult for network operators to manage and monitor their networks in a proper way. To identify the peer networks applications generating the traffic traveling through networks is necessary and if we can identify them sooner, we control them better. In this work, the authors use the machine learning-based classification method to identify the classes of the flows. They choose transfer learning algorithm to classify the traffic, and improve classified results. Finally they compare and evaluate the classification results in terms of the two metrics such as true positive ratio and time expense. Their experiments show that the machine learning algorithm is an efficient algorithm for traffic identification and is able to build a quick identification system.


Articles listed on these pages have been found on publicly available internet pages and are cited with links to those pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests via Email to SoS.Project (at) for removal of the links or modifications to specific citations. Please include the ID# of the specific citation in your correspondence.