2136742
Exploiting efficient data mining techniques to enhance intrusion detection systems
Security is becoming a critical part of organizational information systems. Intrusion detection system (IDS) is an important detection that is used as a countermeasure to preserve data integrity and system availability from attacks. Data mining is being used to clean, classify, and examine large amount of network data to correlate common infringement for intrusion detection. The main reason for using data mining techniques for intrusion detection systems is due to the enormous volume of existing and newly appearing network data that require processing. The amount of data accumulated each day by a network is huge. Several data mining techniques such as clustering, classification, and association rules are proving to be useful for gathering different knowledge for intrusion detection. This paper presents the idea of applying data mining techniques to intrusion detection systems to maximize the effectiveness in identifying attacks, thereby helping the users to construct more secure information systems.
8614779
AITVS: Advanced Interactive Traffic Visualization System
Transportation and the highway network form the backbone of the total public infrastructure system. As such, planning and monitoring for an effective transportation system is crucial in the building and maintenance of a region’s economy and safety. However, demand for road travel continues to expand as population increases (particularly in the metropolitan areas) while new constructions have not kept pace. According to the Federal Highway Administration, it is forecasted that the volume of freight movement alone is to nearly double by 2020 [1]. Congestion and looming gridlock crises seriously threaten any region’s mobility, safety and economic vitality. A crucial component in addressing these concerns is the development of specific technologies to monitor, model, and optimize traffic flow.
6432483
Evaluating GML support for spatial databases
This work presents a study on Geography Markup Language (GML), the issues that arise from using GML for spatial databases and solutions that have been proposed. We concentrate on three aspects of GML, including storage, parsing, and querying. GML is an XML encoding for storing geographic data. It has been developed by the OpenGIS as a medium for uniform geographic data storage and exchange among diverse applications. The underlying concepts of XML therefore can also be applied to GML data. This results in both advantages and disadvantages, which are discussed In the work. GML is a comparatively new language in the field of geographic information systems and still in its developmental stage. Most of the data processing techniques need to be further developed in order for GML to be an efficient medium for geographic data storage and processing.
1895171
Data mining for selective visualization of large spatial datasets
Data mining is the process of extracting implicit, valuable, and interesting information from large sets of data. Visualization is the process of visually exploring data for pattern and trend analysis, and it is a common method of browsing spatial datasets to look for patterns. However the growing volume of spatial datasets make it difficult for humans to browse such datasets in their entirety, and data mining algorithms are needed to filter out large uninteresting parts of spatial datasets. We construct a web-based visualization software package for observing the summarization of spatial patterns and temporal trends. We also present data mining algorithms for filtering out vast parts of datasets for spatial outlier patterns. The algorithms were implemented and tested with a real-world set of Minneapolis-St. Paul (Twin Cities) traffic data.
18415730
Multivariate Spatial Outlier Detection
A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective detection algorithms, and analyze their computation complexity. In addition, using a real-world census data, we demonstrate that our approaches can effectively identify local abnormality in large spatial data sets.
3228880
Wavelet fuzzy classification for detecting and tracking region outliers in meteorological data
In this paper, a wavelet fuzzy classification approach is proposed to detect and track region outliers in meteorological data. First wavelet transform is applied to meteorological data to bring up distinct patterns that might be hidden within the original data. Then a powerful image processing technique, edge detection with competitive fuzzy classifier, is extended to identify the boundary of region outlier. After that, to determine the center of the region outlier, the fuzzy-weighted average of the longitudes and latitudes of the boundary locations is computed. By linking the centers of the outlier regions within consecutive frames, the movement of a region outlier can be captured and traced. Experimental evaluation was conducted on a real-world meteorological data to examine the effectiveness of the proposed approach. This work will help discover interesting and implicit information for large volume of meteorological data.
5184285
Design and Analysis of Spatiotemporal Multicast Protocols for Wireless Sensor Networks
AbstractWe propose a new multicast communication paradigm called “spatiotemporal multicast” for supporting applications which require spatiotemporal coordination in wireless sensor networks. In this paper we focus on a special class of spatiotemporal multicast called “mobicast” featuring a message delivery zone that moves at a constant velocity 
$$\vec v$$
. The key contributions of this work are: (1) the specification of mobicast and its performance metrics, (2) the introduction of four different mobicast protocols along with the analysis of their performance, (3) the introduction of two topological network compactness metrics for facilitating the design and analysis of spatiotemporal protocols, and (4) an experimental evaluation of compactness properties for random sensor networks and their effect on routing protocols.
15346536
Distance-two labelings of graphs
For given positive integers j ≥ k, an L(j,k)-labeling of a graph G is a function f : V (G) → {0, 1, 2,...} such that |f(u) - f(v)| ≥ j when dG(u, v) = 1 and |f(u) - f(v)| ≥ k when dG(u, v) = 2. The L(j, k)-labeling number λj,k(G) of G is defined as the minimum m such that there is an L(j, k)-labeling f of G with f(V(G)) ⊆ {0, 1, 2,....,m}. For a graph G of maximum degree Δ ≥ 1 it is the case that λj,k(G) ≥ j + (Δ - 1)k. The purpose of this paper is to study the structures of graphs G with maximum degree Δ ≥ 1 and λj,k(G) = j + (Δ - 1)k.
30370540
On (d, 2)-dominating numbers of binary undirected de Bruijn graphs
Abstract In this paper, we show that: (i) For n-dimensional undirected binary de Bruijn graphs, UB(n), n⩾4, there is a vertex x=x 1 x 1 x 1 ⋯ x 1 x 1 (x1=1 or 0) such that for any other vertex t there exist at least two internally disjoint paths of length at most n−1 between x and t in UB(n), i.e., the (n−1,2)-dominating number of UB(n) is equal to one. (ii) For n⩾5, let S={100⋯01,011⋯10}. For any other vertex t there exist at least two internally disjoint paths of length at most n−2 between t and S in UB(n), i.e., the (n−2,2)-dominating number of UB(n) is no more than 2.
6462272
Intrinsic Images by Entropy Minimization
A method was recently devised for the recovery of an invariant image from a 3-band colour image. The invariant image, originally 1D greyscale but here derived as a 2D chromaticity, is independent of lighting, and also has shading removed: it forms an intrinsic image that may be used as a guide in recovering colour images that are independent of illumination conditions. Invariance to illuminant colour and intensity means that such images are free of shadows, as well, to a good degree. The method devised finds an intrinsic reflectivity image based on assumptions of Lambertian reflectance, approximately Planckian lighting, and fairly narrowband camera sensors. Nevertheless, the method works well when these assumptions do not hold. A crucial piece of information is the angle for an “invariant direction” in a log-chromaticity space. To date, we have gleaned this information via a preliminary calibration routine, using the camera involved to capture images of a colour target under different lights. In this paper, we show that we can in fact dispense with the calibration step, by recognizing a simple but important fact: the correct projection is that which minimizes entropy in the resulting invariant image. To show that this must be the case we first consider synthetic images, and then apply the method to real images. We show that not only does a correct shadow-free image emerge, but also that the angle found agrees with that recovered from a calibration. As a result, we can find shadow-free images for images with unknown camera, and the method is applied successfully to remove shadows from unsourced imagery.
1071507
On greedy geographic routing algorithms in sensing-covered networks
Greedy geographic routing is attractive in wireless sensor networks due to its efficiency and scalability. However, greedy geographic routing may incur long routing paths or even fail due to routing voids on random network topologies. We study greedy geographic routing in an important class of wireless sensor networks that provide sensing coverage over a geographic area (e.g., surveillance or object tracking systems). Our geometric analysis and simulation results demonstrate that existing greedy geographic routing algorithms can successfully find short routing paths based on local states in sensing-covered networks. In particular, we derive theoretical upper bounds on the network dilation of sensing-covered networks under greedy geographic routing algorithms. Furthermore, we propose a new greedy geographic routing algorithm called Bounded Voronoi Greedy Forwarding (BVGF) that allows sensing-covered networks to achieve an asymptotic network dilation lower than 4:62 as long as the communication range is at least twice the sensing range. Our results show that simple greedy geographic routing is an effective routing scheme in many sensing-covered networks.