The Experts below are selected from a list of 135 Experts worldwide ranked by ideXlab platform
Steven R Johnston - One of the best experts on this subject based on the ideXlab platform.
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the impact of privacy and data protection legislation on the sharing of Intrusion Detection information
Recent Advances in Intrusion Detection, 2001Co-Authors: Steven R JohnstonAbstract:The global nature of the information infrastructure presents enormous opportunities to organizations. However, global interconnection also means global risk and implies the need for global defence. A central aspect of global defence is information sharing, and at as early a point in the incident cycle as possible. This implies the sharing of Intrusion Detection Sensor data. The growing recognition of the requirement to respect personal privacy is bearing fruit in the passage of personal privacy and data protection legislation, which generally limit the ability of organizations to share personal information. Based on the broad definitions of personal information found in the legislation, source IP addresses, one of the key elements of information used in tracing malicious activity, may be considered to be personal information, and would therefore fall under the purview of the privacy and data protection legislation. There are, however, exemptions for the sharing of information that could be extended to permit the sharing of Intrusion Detection information while still meeting the intent of the surveyed legislation.
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Recent Advances in Intrusion Detection - The Impact of Privacy and Data Protection Legislation on the Sharing of Intrusion Detection Information
Lecture Notes in Computer Science, 2001Co-Authors: Steven R JohnstonAbstract:The global nature of the information infrastructure presents enormous opportunities to organizations. However, global interconnection also means global risk and implies the need for global defence. A central aspect of global defence is information sharing, and at as early a point in the incident cycle as possible. This implies the sharing of Intrusion Detection Sensor data. The growing recognition of the requirement to respect personal privacy is bearing fruit in the passage of personal privacy and data protection legislation, which generally limit the ability of organizations to share personal information. Based on the broad definitions of personal information found in the legislation, source IP addresses, one of the key elements of information used in tracing malicious activity, may be considered to be personal information, and would therefore fall under the purview of the privacy and data protection legislation. There are, however, exemptions for the sharing of information that could be extended to permit the sharing of Intrusion Detection information while still meeting the intent of the surveyed legislation.
Kenji Inomata - One of the best experts on this subject based on the ideXlab platform.
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SAS - Two-frequency surveillance technique for Intrusion-Detection Sensor with Leaky Coaxial Cables
2014 IEEE Sensors Applications Symposium (SAS), 2014Co-Authors: Kenji Inomata, Wataru Tsujita, Takashi HiraiAbstract:This paper presents a new design and achievement of Leaky Coaxial Cable (LCX)-based Intrusion-sensing techniques. LCX radiates microwaves from slots milled on the outer conductor. This Sensor can detect an Intrusion object by measuring the variation of received signal. LCX has two types of emitting modes, radiation mode and surface mode. The microwave radiates to far field in radiation mode. On the other hand, the microwave exists only around the LCX in surface mode. Although the conventional LCX-based Sensor operates the LCX in only one mode to detect and classify an object, this paper introduces a simultaneous sensing technique using these two modes. Comparing the signals in both modes, the classification of an object can be estimated. The theory of the emitting modes of LCX and the developed Sensor prototype are described. Experimental results are presented to show that the proposed sensing techniques are valid.
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2 in 1 leakey coaxial cable for Intrusion Detection Sensor
2013 IEEE Sensors Applications Symposium Proceedings, 2013Co-Authors: Kenji Inomata, Wataru Tsujita, Takashi HiraiAbstract:Novel leaky coaxial cable (LCX) is developed. It contains both transmitting and receiving LCX for Intrusion Detection Sensor. While the suppression of coupling between transmitter and receiver is major theme in microwave technology, we give a solution of transmitter and receiver antenna combination. We have developed a prototype of 2 in 1 LCX and then evaluated its performance for Intrusion Detection Sensor in field experimentation.
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accuracy of 2 dimensional object location estimation using leaky coaxial cables
IEEE Transactions on Antennas and Propagation, 2011Co-Authors: Kenji Inomata, Yoshio Yamaguchi, H Yamada, W Tsujita, M Shikai, Kazuhiko SumiAbstract:The improvement of position estimation accuracy is a very important factor in various radar applications. This paper focuses on two-dimensional (2-D) location estimation in radar surveillance using leaky coaxial cables. A leaky coaxial cable is a kind of antenna and has recently attracted attention as a powerful tool for vehicle Detection and as an Intrusion Detection Sensor for perimeter security. We derive the Cramer-Rao bound (CRB) for a leaky coaxial cable sensing application and introduce the CRB ellipsoid to evaluate the accuracy of the 2-D location estimation of an object. The diameter and angle of a CRB ellipsoid indicates its error distribution. A leaky coaxial cable has an outer conductor with periodic slots. Its slot pattern determines the radiation characteristics such as the beam angle and harmonic radiation. We focus on the slot pattern to improve the estimation accuracy. Numerical analyses show that a slot pattern generating multi-beams improves the accuracy while single radiation is preferred for general use in communication applications.
Joao Gama - One of the best experts on this subject based on the ideXlab platform.
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data stream clustering a survey
ACM Computing Surveys, 2013Co-Authors: Jonathan De Andrade Silva, Eduardo R Hruschka, Elaine R Faria, Andre C P L F De Carvalho, Rodrigo C Barros, Joao GamaAbstract:Data stream mining is an active research area that has recently emerged to discover knowledge from large amounts of continuously generated data. In this context, several data stream clustering algorithms have been proposed to perform unsupervised learning. Nevertheless, data stream clustering imposes several challenges to be addressed, such as dealing with nonstationary, unbounded data that arrive in an online fashion. The intrinsic nature of stream data requires the development of algorithms capable of performing fast and incremental processing of data objects, suitably addressing time and memory limitations. In this article, we present a survey of data stream clustering algorithms, providing a thorough discussion of the main design components of state-of-the-art algorithms. In addition, this work addresses the temporal aspects involved in data stream clustering, and presents an overview of the usually employed experimental methodologies. A number of references are provided that describe applications of data stream clustering in different domains, such as network Intrusion Detection, Sensor networks, and stock market analysis. Information regarding software packages and data repositories are also available for helping researchers and practitioners. Finally, some important issues and open questions that can be subject of future research are discussed.
Kazuhiko Sumi - One of the best experts on this subject based on the ideXlab platform.
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accuracy of 2 dimensional object location estimation using leaky coaxial cables
IEEE Transactions on Antennas and Propagation, 2011Co-Authors: Kenji Inomata, Yoshio Yamaguchi, H Yamada, W Tsujita, M Shikai, Kazuhiko SumiAbstract:The improvement of position estimation accuracy is a very important factor in various radar applications. This paper focuses on two-dimensional (2-D) location estimation in radar surveillance using leaky coaxial cables. A leaky coaxial cable is a kind of antenna and has recently attracted attention as a powerful tool for vehicle Detection and as an Intrusion Detection Sensor for perimeter security. We derive the Cramer-Rao bound (CRB) for a leaky coaxial cable sensing application and introduce the CRB ellipsoid to evaluate the accuracy of the 2-D location estimation of an object. The diameter and angle of a CRB ellipsoid indicates its error distribution. A leaky coaxial cable has an outer conductor with periodic slots. Its slot pattern determines the radiation characteristics such as the beam angle and harmonic radiation. We focus on the slot pattern to improve the estimation accuracy. Numerical analyses show that a slot pattern generating multi-beams improves the accuracy while single radiation is preferred for general use in communication applications.
Zhiqiu Huang - One of the best experts on this subject based on the ideXlab platform.
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Conceptual modeling rules extracting for data streams
Knowledge-Based Systems, 2008Co-Authors: Xiaodong Zhu, Zhiqiu HuangAbstract:Data take the form of continuous data streams rather than traditional stored databases in a growing number of applications, including network traffic monitoring, network Intrusion Detection, Sensor networks, fraudulent transaction Detection, financial monitoring, etc. People are interested in the potential rules in data streams such as association rules and decision rules. Compared with much work on developing algorithms of data streams mining, there is little attention paid on the modeling data mining and data streams mining. Considering the problem of conceptual modeling data streams mining, we put forward a data streams oriented decision logic language as a granular computing formal approach and a rules extracting model based on granular computing. In this model, we propose the notion of granular drifting, which accurately interpret the concept drifting problem in data streams. This model is helpful to understand the nature of data streams mining. Based on this model, new algorithms and techniques of data streams mining could be developed.
