PRIVACY-PRESERVING AND TRUTHFUL DETECTION OF PACKET DROPPING ATTACKS IN WIRELESS AD HOC NETWORKS

PRIVACY-PRESERVING AND TRUTHFUL DETECTION OF PACKET DROPPING ATTACKS IN WIRELESS AD HOC NETWORKS

ABSTRACT

Link error and malicious packet dropping are two sources for packet losses in multi-hop wireless ad hoc network. While observing a sequence of packet losses in the network, whether the losses are caused by link errors only, or by the combined effect of link errors and malicious drop is to be identified. In the insider-attack case, whereby malicious nodes that are part of the route exploit their knowledge of the communication context to selectively drop a small amount of packets critical to the network performance. Because the packet dropping rate in this case is comparable to the channel error rate, conventional algorithms that are based on detecting the packet loss rate cannot achieve satisfactory detection accuracy. To improve the detection accuracy, the correlations between lost packets is identified. Homomorphic linear authenticator (HLA) based public auditing architecture is developed that allows the detector to verify the truthfulness of the packet loss information reported by nodes. This construction is privacy preserving, collusion proof, and incurs low communication and storage overheads.

EXISTING SYSTEM

v The related work can be classified into the following two categories.

v High malicious dropping rates

v The first category aims at high malicious dropping rates, where most (or all) lost packets are caused by malicious dropping. In this case, the impact of link errors is ignored. Most related work falls into this category. Based on the methodology used to identify the attacking nodes, these works can be further classified into four sub-categories.

v Credit systems

v A credit system provides an incentive for cooperation. A node receives credit by relaying packets for others, and uses its credit to send its own packets. As a result, a maliciously node that continuous to drop packets will eventually deplete its credit, and will not be able to send its own traffic.

v Reputation systems

v A reputation system relies on neighbors to monitor and identify misbehaving nodes. A node with a high packet dropping rate is given a bad reputation by its neighbors. This reputation information is propagated periodically throughout the network and is used as an important metric in selecting routes. Consequently, a malicious node will be excluded from any route.

v Disadvantages

v Most of the related works assumes that malicious dropping is the only source of packet loss.

v For the credit-system-based method, a malicious node may still receive enough credits by forwarding most of the packets it receives from upstream nodes.

v In the reputation-based approach, the malicious node can maintain a reasonably good reputation by forwarding most of the packets to the next hop.

v While the Bloom-filter scheme is able to provide a packet forwarding proof, the correctness of the proof is probabilistic and it may contain errors.

v As for the acknowledgement-based method and all the mechanisms in the second category, merely counting the number of lost packets does not give a sufficient ground to detect the real culprit that is causing packet losses.

PROPOSED SYSTEM

}  To develop an accurate algorithm for detecting selective packet drops made by insider attackers.

}  This algorithm also provides a truthful and publicly verifiable decision statistics as a proof to support the detection decision.

}  The high detection accuracy is achieved by exploiting the correlations between the positions of lost packets, as calculated from the auto-correlation function (ACF) of the packet-loss bitmap–a bitmap describing the lost/received status of each packet in a sequence of consecutive packet transmissions.

}  By detecting the correlations between lost packets, one can decide whether the packet loss is purely due to regular link errors, or is a combined effect of link error and malicious drop.

}  The main challenge in our mechanism lies in how to guarantee that the packet-loss bitmaps reported by individual nodes along the route are truthful, i.e., reflect the actual status of each packet transmission.

}  Such truthfulness is essential for correct calculation of the correlation between lost packets, this can be achieved by some auditing.

}  Considering that a typical wireless device is resource-constrained, we also require that a user should be able to delegate the burden of auditing and detection to some public server to save its own resources.

}  Public-auditing problem is constructed based on the homomorphic linear authenticator (HLA) cryptographic primitive, which is basically a signature scheme widely used in cloud computing and storage server systems to provide a proof of storage from the server to entrusting clients.

Advantages

}  High detection accuracy

}  Privacy-preserving: the public auditor should not be able to decern the content of a packet delivered on the route through the auditing information submitted by individual hops

}  Incurs low communication and storage overheads at intermediate nodes

HARDWARE SPECIFICATION

Processor                       : Any Processor above 500 MHz.

Ram                              :  128Mb.

Hard Disk                     :  10 GB.

Input device                 :  Standard Keyboard and Mouse.

Output device              :  VGA and High Resolution Monitor.

SOFTWARE SPECIFICATION

Operating System          : Windows Family.

Pages developed using   :  Java Server Pages and HTML.

Techniques                    : Apache Tomcat Web Server 5.0, JDK 1.5 or higher

Web Browser                 :  Microsoft Internet Explorer.

Data Base                      :  MySQL 5.0