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Attribute Normalization in Network Intrusion Detection
2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks, 2009Anomaly intrusion detection is an important issue in computer network security. As a step of data preprocessing, attribute normalization is essential to detection performance. However, many anomaly detection methods do not normalize attributes before training and detection.
Wei Wang 0012 +3 more
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Kernels on Attributed Networks for Community Detection
2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA), 2022Community detection is a primary problem in the study of complex networks. When graphs are enriched with attributes, it has been found that this additional information can help in better understanding the ties among the actors composing the network and provides a deeper insight into group organization.
Pizzuti Clara, Annalisa Socievole
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Anomaly detection in dynamic attributed networks
Neural Computing and Applications, 2020Graph anomaly detection plays a central role in many emerging network applications, ranging from cloud intrusion detection to online payment fraud detection. It has been studied under the contexts of dynamic graphs and attributed graphs separately.
Ruizhi Zhou +4 more
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The pairwise attribute noise detection algorithm
Knowledge and Information Systems, 2006Analyzing the quality of data prior to constructing data mining models is emerging as an important issue. Algorithms for identifying noise in a given data set can provide a good measure of data quality. Considerable attention has been devoted to detecting class noise or labeling errors.
Jason Van Hulse +2 more
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The effect of attribute pairings in intrusion detection
2014 14th UK Workshop on Computational Intelligence (UKCI), 2014As Network Intrusions have become larger and more pervasive the methods of detection have changed, a number of systems use ensemble methods to improve upon results from single classifiers or algorithms. The solutions proposed in the literature achieve good results, which primarily focus on classification of Network Intrusions by tailoring ...
Michael Milliken, Yaxin Bi, Leo Galway
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Local Pattern Detection in Attributed Graphs
2016We propose to mine the topology of a large attributed graph by finding regularities among vertex descriptors. Such descriptors are of two types: (1) the vertex attributes that convey the information of the vertices themselves and (2) some topological properties used to describe the connectivity of the vertices. These descriptors are mostly of numerical
Boulicaut, Jean-François +2 more
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Detecting spatial outliers with multiple attributes
Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence, 2003A 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.
Chang-Tien Lu, Dechang Chen, Yufeng Kou
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Detecting changes in sequences of attributed graphs
2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017In several application domains, one deals with data described by elements and their pair-wise relations. Accordingly, graph theory offers a sound framework to cast modeling and analysis tasks. However, due to a multitude of reasons, real-world systems change operating conditions over time, hence calling for time-dependent, graph-based representations ...
Zambon, Daniele +2 more
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Community detection in subspace of attribute
Information Sciences, 2022Haoran Chen +3 more
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Face Attributes and Detection of Drug Addicts
2019 Eighth International Conference on Emerging Security Technologies (EST), 2019This paper addresses the problem of detecting the prolonged drug abuse marks using “soft” face biometrics. We propose a two-stage approach that integrates both the machine learning approach and the probabilistic reasoning. We identified “drug affect facial attributes” for attribute classification, and then performed probabilistic inference for ...
Sudarsini Tekkam Gnanasekar +1 more
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