83220
Defending against wormhole attacks in mobile ad hoc networks
In ad hoc networks, malicious nodes can deploy wormhole attacks to fabricate a false scenario on the proximity relationship among mobile nodes. A classification of the attacks according to the format of the wormholes is proposed. This forms a basis to identify the detection capability of various approaches. An analysis shows that earlier approaches focus on the prevention of wormholes among neighbors that trust each other. As a more generic approach, we present an end-to-end scheme that can detect wormholes on a multi-hop route. Only the trust between the source and the destination is assumed. The mechanism uses geographic information to detect anomalies in neighbor relations and node movements. To reduce the computation and storage overhead, we present a scheme called cell-based open tunnel avoidance (COTA) to manage the information. COTA requires a constant space for every node on the path and the computation overhead increases linearly to the number of detection packets. We prove that the savings do not deteriorate the detection capability. Various schemes to control communication overhead are studied. The simulation and experiments on real devices show that the proposed mechanism can be combined with existent routing protocols to defend against wormhole attacks. Copyright © 2006 John Wiley & Sons, Ltd.
1177051
Visualization assisted detection of sybil attacks in wireless networks
In wireless networks,the authenticity and uniqueness of node identities are essential to the fundamental operations such as routing, resource allocation, and intrusion detection. In this paper, we investigate Sybil attack, an attack in which a malicious node illegitimately acquires multiple identities and performs as these nodes simultaneously. We propose an effective approach to monitoring and detecting such attacks by integrating network security and visualization methods. The security component explores the time-varying network topology and its statistical and geometry information to detect the existence of Sybil attacks. The visualization component incorporates the detection results and provides an effective mechanism to illustrate abnormal topology patterns and locate fake identities. These two components are integrated into a practical system that takes advantage of both interactive visualization and intelligent security methods. Experimental studies are conducted to investigate the impacts of the network parameters such as node connectivity on the detection capability of the proposed mechanism.
1103015
Visualization of wormholes in sensor networks
Several protocols have been proposed to defend against wormholes in ad hoc networks by adopting positioning devices, synchronized clocks, or directional antennas. In this paper, we propose a mechanism, MDS-VOW, to detect wormholes in a sensor network. MDS-VOW first reconstructs the layout of the sensors using multi-dimensional scaling. To compensate the distortions caused by distance measurement errors, a surface smoothing scheme is adopted. MDS-VOW then detects the wormhole by visualizing the anomalies introduced by the attack. The anomalies, which are caused by the fake connections through the wormhole, bend the reconstructed surface to pull the sensors that are faraway to each other. Through detecting the bending feature, the wormhole is located and the fake connections are identified. The contributions of MDS-VOW are: (1) it does not require the sensors to be equipped with special hardware, (2) it adopts and combines the techniques from social science, computer graphics, and scientific visualization to attack the problem in network security. We examine the accuracy of the proposed mechanism when the sensors are deployed in a circle area and one wormhole exists in the network. The results show that MDS-VOW has a low false alarm ratio when the distance measurement errors are not large.
270863
Study of distance vector routing protocols for mobile ad hoc networks
We investigate the performance issues of destination-sequenced distance vector (DSDV) and ad-hoc on-demand distance vector (AODV) routing protocols for mobile ad hoc networks. Four performance metrics are measured by varying the maximum speed of mobile hosts, the number of connections, and the network size. The correlation between network topology change and mobility is investigated by using linear regression analysis. The simulation results indicate that AODV outperforms DSDV in less stressful situations, while DSDV is more scalable with respect to the network size. It is observed that network congestion is the dominant reason for packet drop for both protocols. We propose a new routing protocol, congestion-aware distance vector (CADV), to address the congestion issues. CADV outperforms AODV in delivery ratio by about 5%, while introduces less protocol load. The result demonstrates that integrating congestion avoidance mechanisms with proactive routing protocols is a promising way to improve performance.
47778
Key distribution and update for secure inter-group multicast communication
Group communication has become an important component in wireless networks. In this paper, we focus on the environments in which multiple groups coexist in the system, and both intra and inter group multicast traffic must be protected by secret keys. We propose a mechanism that integrates polynomials with flat tables to achieve personal key share distribution and efficient key refreshment during group changes. The proposed mechanism distributes keys via true broadcast. The contributions of the research include: (1) By switching from asymmetric algorithms to symmetric encryption methods, the proposed mechanism avoids heavy computation, and improves the processing efficiency of multicast traffic and the power usage at the wireless nodes. The group managers do not have to generate public-private key pairs when the group member changes. (2) It becomes more difficult for an attacker to impersonate another node since personal key shares are adopted. The additional storage overhead at the wireless nodes and the increased broadcast traffic during key refreshment are justified. In addition, we describe techniques to improve the robustness of the proposed mechanism under the complicated scenarios such as collusive attacks and batch group member changes.
10575596
Trust-based privacy preservation for peer-to-peer data sharing
Privacy preservation in a peer-to-peer (P2P) system tries to hide the association between the identity of a participant and the data that it is interested in. This paper proposes a trust-based privacy-preservation method for P2P data sharing. It adopts the trust relation between a peer and its collaborators (buddies). The buddy works as a proxy to send the request and acquire the data. This provides a shield under which the identity of the requester and the accessed data cannot be linked. A privacy measuring method is presented to evaluate the proposed mechanism. Dynamic trust assessment and the enhancement to supplier's privacy are discussed
15806898
Market-driven bandwidth allocation in selfish overlay networks
Selfish overlay networks consist of autonomous nodes that develop their own strategies by optimizing towards their local objectives and self-interests, rather than following prescribed protocols. It is thus important to regulate the behavior of selfish nodes, so that system-wide properties are optimized. In this paper, we investigate the problem of bandwidth allocation in overlay networks, and propose to use a market-driven approach to regulate the behavior of selfish nodes that either provide or consume services. In such markets, consumers of services select the best service providers, taking into account both the performance and the price of the service. On the other hand, service providers are encouraged to strategically decide their respective prices in a pricing game, in order to maximize their economic revenues and minimize losses in the long run. In order to overcome the limitations of previous models towards similar objectives, we design a decentralized algorithm that uses reinforcement learning to help selfish nodes to incrementally adapt to the local market, and to make optimized strategic decisions based on past experiences. We have simulated our proposed algorithm in randomly generated overlay networks, and have shown that the behavior of selfish nodes converges to their optimal strategies, and resource allocations in the entire overlay are near-optimal, and efficiently adapts to the dynamics of overlay networks.
18265885
Identifying intrusions in computer networks with principal component analysis
Most current anomaly intrusion detection systems (IDSs) detect computer network behavior as normal or abnormal but cannot identify the type of attacks. Moreover, most current intrusion detection methods cannot process large amounts of audit data for real-time operation. In this paper, we propose a novel method for intrusion identification in computer networks based on principal component analysis (PCA). Each network connection is transformed into an input data vector. PCA is employed to reduce the dimensionality of the data vectors and identification is handled in a low dimensional space with high efficiency and low use of system resources. The normal behavior is profiled based on normal data for anomaly detection and models of each type of attack are built based on attack data for intrusion identification. The distance between a vector and its reconstruction onto those reduced subspaces representing the different types of attacks and normal activities is used for identification. The method is tested with network data from MIT Lincoln labs for the 1998 DARPA intrusion detection evaluation program and testing results show that the model is promising in terms of identification accuracy and computational efficiency for real-time intrusion identification.
44902530
The 2-dipath chromatic number of Halin graphs
2396831
Mining coherent patterns from heterogeneous microarray data
Microarray technology is a powerful tool for geneticists to monitor interactions among tens of thousands of genes simultaneously. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. However, these methods assume that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data. We compare our model with previously proposed models. We analyze the search space of the problem and give a naïve solution for it.
8724139
News Content Highlight via Fast Caption Text Detection on Compressed Video
Captions present in video frames play an important role in understanding video content. This paper presents a fast algorithm to automatically detect captions in MPEG compressed video. It is based on statistics features of caption text's chrominance components. The paper also discusses its principle and speed-up mechanism in detail. We have successfully exploited the technique to automatically construct the pictorial catalogue, a new content representation. Experiment results show the proposed caption detection algorithm has not only the ideal accuracy 96.6% and recall 100%, but also a detection speed of faster than real time.