FRINK Linked Data Fragments server

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• W2077463265 abstract "The fact that security is a critical problem when implementing mobile ad hoc networks (MANETs) is widely acknowledged. One of the different kinds of misbehavior a node may exhibit is selfishness. A selfish node wants to preserve its resources while using the services of others and consuming their resources. One way of preventing selfishness in a MANET is a detection and exclusion mechanism. In this paper, we focus on the detection and present different kinds of sensors that will find selfish nodes. First we present simulations that show the negative effects which selfish nodes cause in MANET. In the related work section we will analyze the detection mechanisms proposed by others. Our new detection mechanisms that we describe in this paper are called activity-based overhearing, iterative probing, and unambiguous probing. Simulation-based analysis of these mechanisms show that they are highly effective and can reliably detect a multitude of selfish behaviors. 1 Selfish nodes in MANETs Mobile ad hoc networks (MANETs) rely on the cooperation of all participating nodes. The more nodes cooperate to transfer traffic, the more powerful a MANET gets. But supporting a MANET is a cost-intensive activity for a mobile node. Detecting routes and forwarding packets consumes local CPU time, memory, network-bandwidth, and last but not least energy. Therefore there is a strong motivation for a node to deny packet forwarding to others while at the same time using their services to deliver own data. In table 1, we analyze different possibilities for a selfish node to save its own resources in a MANET based on the DSR routing protocol [JMHJ03, Pe01]. It uses the attack-tree notation proposed by Bruce Schneier [Sc99] that allows the analysis of different ways how an attacker can achieve his goal. Alternatives to reach a certain goal are denoted by OR, multiple steps that are necessary to reach a goal are denoted by AND. Using the numbers in the table, we can easily describe different attacks. For example, attack 3.1 stands for ”Drop data packets”. Whereas most of the attacks based on manipulations of routing data can be detected by the use of a secure routing protocol like Ariadne [HPJ02], SRP [PH02a, PH02b, PH02c, PHS02, PH03], ARAN [SDL02], or SAODV [Gu02, GA02], there remain two attacks in the attack tree that cannot be detected this easily. When nodes simply drop packets (case 1.1 and 3.1 in the attack tree), all of the secure routing protocols fail as they focus only on Attack tree: Save own resources OR 1. Do not participate in routing OR 1. Do not relay routing data OR 1. Do not relay route requests 2. Do not relay route replies 3. Set hop limit or TTL value in route request/reply to smallest possible value 2. Modify routing data/topology OR 1. Modify route request OR 1. Insert additional hops 2. Modify route reply OR 1. Replace own ID in returned route with detour leading through neighboring nodes 2. Return completely wrong route, provoking RERR and salvaging 3. Insert additional hops 4. Declare own ID in source route as external 2. Stop participation in current route AND 1. Provoke route error OR 1. Create arbitrary RERR messages 2. Do not send ACK messages (causing RERRs in other nodes) 2. Do not participate in following route request (A.1) 3. Do not relay data packets OR 1. Drop data packets 2. Set hop limit/TTL to 0/1 (causing a RERR) Table 1: Attack Tree: Save own resources Parameter Value Number of Nodes 50 Area X (m) 1500 Area Y (m) 300 Transmission Range Radius (m) 250 Traffic Model cbr Sending rate (packets/s) 4.0 Max. number of connections 20 Packet size (byte) 512 Simulation time (s) 900 Table 2: Simulation parameters the detection of modifications to routing data but not on the concealment of existing links. We have done a number of simulations that show how this behavior affects a MANET. The simulations were done using ns-2.1b8a and the DSR routing protocol. The scenario included 50 nodes moving in an area of 1500x300m according to the random waypoint model at speeds of 1ms and 20 m s with no pause time. Twenty of the nodes were CBR sources sending 4 packets per second. Details of the simulation parameters are given in table 2. These parameters are used for all following simulations. Figure 1 shows the results of these simulations. We have varied the number of selfish nodes from 0 to 50 (the total number of nodes in the network). It is obvious that the number of selfish nodes has a significant effect on the rate of packets that are successfully delivered in the network. Further the movement rate has a clear effect. The faster nodes move, the lower the delivery ratio becomes. Finally we see that at lower speeds nodes of case 3.1 are more detrimental to the network than those of type case 1.1, whereas at higher speeds there are no big differences. What explanations can be found for this? When the number of case-1.1 nodes rises in a Number of selfish nodes 0 10 20 30 40 50 D el iv er y ra tio % 0 20 40 60 80 100 Attack case 1.1 Node movement: 1 m/s Node movement: 20 m/s Number of selfish nodes 0 10 20 30 40 50 D el iv er y ra tio % 0 20 40 60 80 100 Attack case 3.1 Node movement: 1 m/s Node movement: 20 m/s Figure 1: Selfish attack simulation network, there are less nodes available for building up routes. So if no alternative route can be established, there is no route to the destination which means that packets have to be discarded. That reduces the delivery rate. When movement speed rises, the delivery ratio also diminishes as the network in general gets more fragile. But the network still has a reasonable chance of routing around the selfish nodes. This changes with type case3.1. Here the nodes behave correctly during the route discovery phase. Thus they can be included in regular routes, but then they start to drop all packets. This isn’t detected by DSR and no countermeasures are taken. So at a movement speed of 20ms only 10% of the selfish nodes push the probability of a successful packet delivery below 50%. Our simulations with AODV have revealed a similar behavior. This demonstrates clearly that an effective protection against selfish and malicious nodes is absolutely mandatory for ad hoc networks. 2 Motivation vs. Detection & Exclusion There are two approaches of dealing with selfish nodes. The first approach tries to give a motivation for participating in the network function. A typical system representing this approach is Nuglets by Hubeaux et al. [BH01, BH03]. The authors suggest to introduce a virtual currency called Nuglets that is earned by relaying foreign traffic and spent by sending own traffic. The major drawback of this approach is the demand for trusted hardware to secure the currency. There are arguments that tamper-resistant devices in general might be next to impossible to be realized [AK96, AK97]. A similar approach without the need of tamper-proof hardware has been suggested by Zhong et al. in [ZCY03]. Most of the existing work in this field concentrates on the second approach: detecting and excluding misbehaving nodes. The first to propose a solution to the problem of selfish (or as they call it ”misbehaving”) nodes in an ad hoc network were Marti, Giuli, Lai, and Baker in [MGLB00]. Their system uses a watchdog that monitors the neighboring nodes to check if they actually relay the data the way they should do. Then a component called pathrater will try to prevent paths which contain such misbehaving nodes. As they indicate in their paper, their detection mechanism has a number of severe drawbacks. Relying only on overhearing transmissions in promiscuous mode may fail due to a number of reasons. In case of sensor failure, nodes may be falsely accused of misbehavior. The second drawback is that selfish nodes profit from being recognized as misbehaving. The paths in the network are then routed around them, but there is no exclusion from service. We will later present more advanced sensors that will allow a better detection of selfish nodes. In [ZL00, ZLH03], the authors describe a distributed intrusion detection system (IDS) for MANETs that consists of the local components ”data collection”, ”detection”and ”response”, and of the global components ”cooperative detection”and ”global response”. Their architecture is very promising and similar to the one we use in our project, but they neglect the aspect how their local data collection should find out on incidents like dropped packets, concealed links, etc. Another system is the ”Collaborative Reputation Mechanism”or CORE [MM, MM02]. It is similar to the distributed IDS by Zhang et al. and consists of local observations that are combined and distributed to calculate a reputation value for each node. Based on this reputation, nodes are allowed to participate in the network or are excluded. In their work, the authors specify in detail how the different nodes should cooperate to combine the local reputation values to a global reputation and how they should react to negative reputations of nodes. For the actual detection of selfish nodes, they only refer to the work of Marti. A similar approach is conducted by Buchegger et al. with their system called CONFIDANT [BB02a, BB02b]. Again, they only marginally describe their detection mechanism and rely mostly on promiscuous overhearing." @default.
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• W2077463265 title "Sensors for Detection of Misbehaving Nodes in MANETs" @default.
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