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.
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.
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.
14078437
CLUSEQ: efficient and effective sequence clustering
Analyzing sequence data has become increasingly important recently in the area of biological sequences, text documents, Web access logs, etc. We investigate the problem of clustering sequences based on their sequential features. As a widely recognized technique, clustering has proven to be very useful in detecting unknown object categories and revealing hidden correlations among objects. One difficulty that prevents clustering from being performed extensively on sequence data (in categorical domain) is the lack of an effective yet efficient similarity measure. Therefore, we propose a novel model (CLUSEQ) for sequence cluster by exploring significant statistical properties possessed by the sequences. The conditional probability distribution (CPD) of the next symbol given a preceding segment is derived and used to characterize sequence behavior and to support the similarity measure. A variation of the suffix tree, namely probabilistic suffix tree, is employed to organize (the significant portion of) the CPD in a concise way. A novel algorithm is devised to efficiently discover clusters with high quality and is able to automatically adjust the number of clusters to its optimal range via a unique combination of successive new cluster generation and cluster consolidation. The performance of CLUSEQ has been demonstrated via extensive experiments on several real and synthetic sequence databases.
14240267
Multimedia-Based Interactive Advising Technology for Online Consumer Decision Support
This paper proposes a new method for online consumer decision support: Multimedia-based Interactive Advising (MIA). The method is based on current internet technologies, particularly multimedia, intelligent agent and advising technology. The discussion of the benefits and design of MIA will illuminate particular areas of research and development, with the goal of improving consumers’ choices in e-commerce environments.
5447146
Mining long sequential patterns in a noisy environment
Pattern discovery in long sequences is of great importance in many applications including computational biology study, consumer behavior analysis, system performance analysis, etc. In a noisy environment, an observed sequence may not accurately reflect the underlying behavior. For example, in a protein sequence, the amino acid N is likely to mutate to D with little impact to the biological function of the protein. It would be desirable if the occurrence of D in the observation can be related to a possible mutation from N in an appropriate manner. Unfortunately, the support measure (i.e., the number of occurrences) of a pattern does not serve this purpose. In this paper, we introduce the concept of compatibility matrix as the means to provide a probabilistic connection from the observation to the underlying true value. A new metric match is also proposed to capture the "real support" of a pattern which would be expected if a noise-free environment is assumed. In addition, in the context we address, a pattern could be very long. The standard pruning technique developed for the market basket problem may not work efficiently. As a result, a novel algorithm that combines statistical sampling and a new technique (namely border collapsing) is devised to discover long patterns in a minimal number of scans of the sequence database with sufficiently high confidence. Empirical results demonstrate the robustness of the match model (with respect to the noise) and the efficiency of the probabilistic algorithm.
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.
2992545
Constrained 3D shape reconstruction using a combination of surface fitting and registration
We investigate 3D shape reconstruction from measurement data in the presence of constraints. The constraints may fix the surface type or set geometric relations between parts of an object's surface, such as orthogonality, parallelity and others. It is proposed to use a combination of surface fitting and registration within the geometric optimization framework of squared distance minimization (SDM). In this way, we obtain a quasi-Newton like optimization algorithm, which in each iteration simultaneously registers the data set with a rigid motion to the fitting surface and adapts the shape of the fitting surface. We present examples to show the applicability of our method to constrained 3D shape fitting for reverse engineering of CAD models and to high accuracy fitting with kinematic surfaces, which include surfaces of revolution (reconstructed from fragments of archeological pottery) and spiral surfaces, which are fitted to 3D measurement data of shells. Our optimization algorithm can combine registration of multiple scans of an object and model fitting into a single optimization process which is shown to be superior to the traditional procedure, which first registers the data and then fits a model to it.