Intrusion and misuse detection in large-scale systems

Erbacher, Robert F.; Walker, Kirstie L; Frincke, Deborah A.

doi:10.1109/38.974517

Cited by 99 publications

(54 citation statements)

References 6 publications

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“…6B). When examining timesteps [9,10,11], an emergent focal activation point (annotated in Fig. 6C) in the lower-right corner of the electrode view was evident.…”

Section: Spatio-temporal Analysismentioning

confidence: 99%

“…Glidgets [18] depicts temporal changes by segmenting glyphs into time slices, enabling the comparison of attributes over time. Nan Cao et al [6] and Erbacher et al [9] aggregate temporal data to summarize the entire dataset with the overall goal of detecting anomalous behavior in the network. ECoG ClusterFlow utilizes some of the aforementioned concepts to provide unique glyph-based designs and visual analysis methods that show the overall modular changes of the network.…”

Section: Spatio-temporal Datamentioning

confidence: 99%

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Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

Murugesan

Bouchard

Chang

et al. 2016

Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

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We present ECoG ClusterFlow, a novel interactive visual analysis tool for the exploration of high-resolution Electrocorticography (ECoG) data. Our system detects and visualizes dynamic high-level structures, such as communities, using the time-varying spatial connectivity network derived from the high-resolution ECoG data. ECoG ClusterFlow provides a multi-scale visualization of the spatio-temporal patterns underlying the time-varying communities using two views: 1) an overview summarizing the evolution of clusters over time and 2) a hierarchical glyph-based technique that uses data aggregation and small multiples techniques to visualize the propagation of clusters in their spatial domain. ECoG ClusterFlow makes it possible 1) to compare the spatio-temporal evolution patterns across various time intervals, 2) to compare the temporal information at varying levels of granularity, and 3) to investigate the evolution of spatial patterns without occluding the spatial context information. We present case studies done in collaboration with neuroscientists on our team for both simulated and real epileptic seizure data aimed at evaluating the effectiveness of our approach.

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“…6B). When examining timesteps [9,10,11], an emergent focal activation point (annotated in Fig. 6C) in the lower-right corner of the electrode view was evident.…”

Section: Spatio-temporal Analysismentioning

confidence: 99%

Section: Spatio-temporal Datamentioning

confidence: 99%

Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

Murugesan

Bouchard

Chang

et al. 2016

Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

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“…The goal is to identifiable summarization of different activities. For example Erbacher et al [5] introduced a radial glyph that summarizes a web server's activity of connecting to other severs over time. Anemone [6] introduced a glyph showing the statistical information of users' visiting a web page.…”

Section: Related Workmentioning

confidence: 99%

Episogram: Visual Summarization of Egocentric Social Interactions

Cao

Lin

et al. 2016

IEEE Comput. Grap. Appl.

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Abstract-Visualizing social interaction data has been of booming interest as the recent availability of social traces, ranging from the conversations left in social media to groups' collaborations archived in publications. The key challenges of visualizing social interaction data including the difficulties of (1) understanding the general structure of social interactions and (2) representing the data in the context of different user activities for revealing different behavior patterns. In this paper, we present, Episogram, for visualizing social interaction data. Our design is based on an anatomy of social interaction process in which the actors and objects involved can be formally represented as a time-varying tripartite network. In Episogram, we display and aggregate such tripartite networks along multiple temporal dimensions, from different actors' egocentric perspectives. We show the effectiveness of the proposed technique via case studies and user studies. The results indicate that our design provides non-trivial insights from social interaction data.

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“…Anomaly detection refers to statistical knowledge about normal activity. The anomaly detection approach can be categorized into semisupervised and unsupervised anomaly detection [4]. Semisupervised anomaly detection approaches need a set of purely normal training data from which they found the profile of normal behavior.…”

Section: Intrusion Detection Systemmentioning

confidence: 99%

Intrusion Detection in KDD99 Dataset using SVM-PSO and Feature Reduction with Information Gain

Saxena¹,

Richariya²

2014

IJCA

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Intrusion detection is a process of identifying the Attacks in the networks. The main aim of IDS is to identify the Normal and Intrusive activities. In recent years, many researchers are using data mining techniques for building IDS. Due to the nonlinearity and quantitative or qualitative network data traffic IDS is complicated. For making the IDS efficient we have to choose the key features. Support Vector Machine (SVM) gives the potential solution for IDS problem. SVM suffers by selecting the suitable SVM parameters. Here we propose a new approach using data mining technique such as SVM and Particle swarm optimization for attaining higher detection rate. PSO is an Optimization method and has a strong global search capability. The SVM-PSO Method is applied to KDD Cup 99 dataset. Free parameters are obtained by standard PSO for support vector machine and the binary PSO is used to obtain the best possible feature subset at building intrusion detection system. The propose technique has major steps: Preprocessing, Feature Reduction using Information Gain, Training using SVM-PSO. Then based on the subsequent training subsets a vector for SVM classification is formed and in the end, classification using PSO is performed to detect Intrusion has happened or not. The experimental result shows that SVM-PSO acquire high detection rate than regular SVM Method algorithm.

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Intrusion and misuse detection in large-scale systems

Cited by 99 publications

References 6 publications

Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

Episogram: Visual Summarization of Egocentric Social Interactions

Intrusion Detection in KDD99 Dataset using SVM-PSO and Feature Reduction with Information Gain

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