Peer-to-Peer networks are designed to rely on resources of their own users. Therefore, resource management plays an important role in P2P protocols. Therefore, resource management plays an important role in P2P protocols. Early P2P networks did not use proper mechanisms to manage fairness. However, after seeing difficulties and rise of freeloaders in networks like Gnutella, the importance of providing fairness for users have become apparent. In this paper, we propose an incentive based security model which leads to a network infrastructure that lightens the work of Seeders and makes Leechers to contribute more. This method is able to prevent betrayals in Leecher-to-Leecher transactions and more importantly, helps Seeders to be treated more fairly. This is what other incentive methods such as Bittorrent are incapable of doing. Additionally, by getting help from cryptography and combining it with our method, it is also possible to achieve secure channels, immune to spying, next to a fair network. The simulation results clearly show that how our proposed approach can overcome free-riding issue. In addition, our findings revealed that our approach is able to provide an appropriate level of fairness for the users and can decrease the download time.
Trust management systems are methods to establish word-of-mouth for P2P networks. This means that based on transactions between nodes, they evaluate a degree of trust for each node which is mostly used for establishing a fair network. Finding a malicious user from its neighbors is a selection problem. Methods such as fuzzy decision making @cite_20 @cite_25 and genetic-based algorithms @cite_23 can be used to select the free-riders from multiple criteria. A successful trust management system that uses this approach is @cite_5 . The @cite_18 @cite_4 are other examples of trust management systems. EigenTrust is the name of an approach proposed In @cite_1 . This method uses an algorithm to decrease the number of downloads of inauthentic files in a P2P network that assigns each peer a unique global trust value, based on the peer's history of uploads. For this purpose, the authors proposed a distributed and secure method to compute global trust values, based on power iteration. These values are then used by peers to select the peers from whom they download.
@cite_18 @cite_4 @cite_1 @cite_23 @cite_5 @cite_25 @cite_20
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The Peer-to-Peer (P2P) architecture has been successfully used to reduce costs and increase the scalability of Internet live streaming systems. However, the effectiveness of these applications depends largely on user (peer) cooperation. In this article we use data collected from SopCast, a popular P2P live application, to show that there is high correlation between peer centrality--out-degree, out-closeness, and betweenness--in the P2P overlay graph and peer cooperation. We use this finding to propose a new regression-based model to predict peer cooperation from its past centrality. Our model takes only peer out-degrees as input, as out-degree has the strongest correlation with peer cooperation. Our evaluation shows that our model has good accuracy and does not need to be trained too often (e.g., once each 16 min). We also use our model to sketch a mechanism to detect malicious peers that report artificially inflated cooperation aiming at, for example, receiving better quality of service. Peer-to-peer file-sharing networks are currently receiving much attention as a means of sharing and distributing information. However, as recent experience shows, the anonymous, open nature of these networks offers an almost ideal environment for the spread of self-replicating inauthentic files.We describe an algorithm to decrease the number of downloads of inauthentic files in a peer-to-peer file-sharing network that assigns each peer a unique global trust value, based on the peer's history of uploads. We present a distributed and secure method to compute global trust values, based on Power iteration. By having peers use these global trust values to choose the peers from whom they download, the network effectively identifies malicious peers and isolates them from the network.In simulations, this reputation system, called EigenTrust, has been shown to significantly decrease the number of inauthentic files on the network, even under a variety of conditions where malicious peers cooperate in an attempt to deliberately subvert the system. Wireless sensor networks consist of a large number of nodes which are distributed sporadically in a geographic area. The energy of all nodes on the network is limited. For this reason, providing a method of communication between nodes and network administrator to manage energy consumption is crucial. For this purpose, one of the proposed methods with high performance, is clustering methods. The big challenge in clustering methods is dividing network into several clusters that each cluster is managed by a cluster head (CH). In this paper, a centralized genetic-based clustering (CGC) protocol using onion approach is proposed. The CGC protocol selects the appropriate nodes as CHs according to three criteria that ultimately increases the network life time. This paper investigates the genetic algorithm (GA) as a dynamic technique to find optimum CHs. Furthermore, an innovative fitness function according to the specified parameters is presented. Each chromosome which minimizes fitness function, is selected by base station (BS) and its nodes are introduced to the whole network as proper CHs. After the selection of CHs and cluster formation, for upper level routing between CHs, we define a novel concept which is called Onion Approach. We divide the network into several onion layers in order to reduce the communication overhead among CH nodes. Simulation results show that the implementation of the proposed method by GA and using onion approach, presents better efficiency compared with other previous methods. Conducted simulation results show that the CGC protocol has done significant improvement in terms of running time of the algorithm, the number of nodes alive, first node death, last node death, the number of packets received by the BS, and energy consumption of the network. An important issue in Peer-to-Peer networks is to encourage users to share with others as they use the resources of the network. However, some nodes may only consume from users without giving anything in return. To fix this problem, we can incorporate trust management systems with network infrastructures. Current trust managements are usually made for unstructured overlays and have several shortcomings. They are made to be very similar to e-commerce scoring websites which may not be the best design for fairness in P2P networks. Several problems may arise with their designs such as difficulties to provide a complete history of freeloaders or lack of an autonomous removal mechanism in case of severe attacks. In this paper, we argue that such systems can be deployed more efficiently by using a structured paradigm. For this purpose, we propose C-Trust, a trust management system which is focused on fairness for P2P networks. This is done by getting help from current circular structured designs. This method is able to mark freeloaders, identify their severity of abusion and punish them accordingly. We are also able to effectively protect both Seeder-to-Leecher and Leecher-to-Leecher transactions. This feature is specially important for fairness which other trust systems have not considered so far. The most important aim of Automated Intrusion Response Systems (AIRSs) is selecting responses that impose less cost on the protected system and which are able to neutralize intrusions progress effectively. Cost-sensitive AIRSs use different methods to launch efficient responses. In this regard, risk assessment as a component for assessing intrusion danger on the system is introduced in many papers. However, most available risk assessment methods produce ambiguous results. Fuzzy logic is known as an effective method to be used in the process of risk assessment. This is mainly because fuzzy approach reduces the level of uncertainty of risk factors. To assess risk by fuzzy methods, risk parameters which are extracted from the traffic patterns are used as inputs of fuzzy systems. The aim of this paper is to introduce an AIRS based on fuzzy risk assessment to evaluate the risk of each intrusion in real time and apply a suitable response for protecting web applications. We also introduce a method for applying responses retroactively. The results of applied method show the effective performance of the proposed method in terms of cost-sensitivity and time to response. In this paper we propose a novel relay selection method for cooperation communication networks using fuzzy logic. Many efforts have been made in the literature to select the superior relay based on relay's SNR SER and or relay's reputation (in cooperation stimulation methods). We jointly consider four criteria for the process of relay selection, relay's SNR SER, relay's reputation, relaying strategy and relay location. We consider the condition in which network users employ different relaying strategies, i.e., some relay nodes employ decode-and-forward strategy, some nodes employ amplify-and-forward strategy, and the other ones employ compress-and-forward strategy. Also, we categorize the network users into LM clusters and take into account the relative distance of potential relay nodes from the source and destination nodes in the process of relay selection. This relative distance has significant impact on the average achievable rate in destination node. Finally, by using a fuzzy logic decision making method, we select the “best relay” based on these four criteria.