The Experts below are selected from a list of 1512 Experts worldwide ranked by ideXlab platform
Gregory R. Ganger - One of the best experts on this subject based on the ideXlab platform.
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USENIX Security Symposium - Storage-based intrusion detection: watching storage activity for Suspicious Behavior
2003Co-Authors: Adam G. Pennington, John D. Strunk, John Linwood Griffin, Craig A. N. Soules, Garth R Goodson, Gregory R. GangerAbstract:Storage-based intrusion detection allows storage systems to watch for data modifications characteristic of system intrusions. This enables storage systems to spot several common intruder actions, such as adding backdoors, inserting Trojan horses, and tampering with audit logs. Further, an intrusion detection system (IDS) embedded in a storage device continues to operate even after client systems are compromised. This paper describes a number of specific warning signs visible at the storage interface. Examination of 18 real intrusion tools reveals that most (15) can be detected based on their changes to stored files. We describe and evaluate a prototype storage IDS, embedded in an NFS server, to demonstrate both feasibility and efficiency of storage-based intrusion detection. In particular, both the performance overhead and memory required (152KB for 4730 rules) are minimal.
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storage based intrusion detection watching storage activity for Suspicious Behavior
USENIX Security Symposium, 2003Co-Authors: Adam G. Pennington, John D. Strunk, John Linwood Griffin, Craig A. N. Soules, Garth R Goodson, Gregory R. GangerAbstract:Storage-based intrusion detection allows storage systems to watch for data modifications characteristic of system intrusions. This enables storage systems to spot several common intruder actions, such as adding backdoors, inserting Trojan horses, and tampering with audit logs. Further, an intrusion detection system (IDS) embedded in a storage device continues to operate even after client systems are compromised. This paper describes a number of specific warning signs visible at the storage interface. Examination of 18 real intrusion tools reveals that most (15) can be detected based on their changes to stored files. We describe and evaluate a prototype storage IDS, embedded in an NFS server, to demonstrate both feasibility and efficiency of storage-based intrusion detection. In particular, both the performance overhead and memory required (152KB for 4730 rules) are minimal.
Gerhard Rigoll - One of the best experts on this subject based on the ideXlab platform.
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a hierarchical approach for visual Suspicious Behavior detection in aircrafts
International Conference on Digital Signal Processing, 2009Co-Authors: D Arsic, Benedikt Hornler, Bjorn Schuller, Gerhard RigollAbstract:Recently great interest has been shown in the visual surveillance of public transportation systems. The challenge is the automated analysis of passenger's Behaviors with a set of visual low-level features, which can be extracted robustly. On a set of global motion features computed in different parts of the image, here the complete image, the face and skin color regions, a classification with Support Vector Machines is performed. Test-runs on a database of aggressive, cheerful, intoxicated, nervous, neutral and tired Behavior.
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Suspicious Behavior detection in public transport by fusion of low level video descriptors
International Conference on Multimedia and Expo, 2007Co-Authors: D Arsic, Bjorn Schuller, Gerhard RigollAbstract:Great interest has been shown in the visual surveillance of public transportation systems. The challenge is the automated analysis of passengers' Behaviors with a set of visual low-level features, which can be extracted robustly. On a set of global motion features computed in different parts of the image, here the complete image, the face and skin color regions, a classification with support vector machines is performed. Test-runs on a database of aggressive, cheerful, intoxicated, nervous, neutral and tired Behavior in an airplane situation show promising results.
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ICME - Suspicious Behavior Detection in Public Transport by Fusion of Low-Level Video Descriptors
Multimedia and Expo 2007 IEEE International Conference on, 2007Co-Authors: D Arsic, Bjorn Schuller, Gerhard RigollAbstract:Great interest has been shown in the visual surveillance of public transportation systems. The challenge is the automated analysis of passengers' Behaviors with a set of visual low-level features, which can be extracted robustly. On a set of global motion features computed in different parts of the image, here the complete image, the face and skin color regions, a classification with support vector machines is performed. Test-runs on a database of aggressive, cheerful, intoxicated, nervous, neutral and tired Behavior in an airplane situation show promising results.
Alain Cagnati - One of the best experts on this subject based on the ideXlab platform.
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vizpicious a visual user adaptive tool for communication logs analysis and Suspicious Behavior detection
Web Intelligence, 2012Co-Authors: Amyn Bennamane, Hakim Hacid, Arnaud Ansiaux, Alain CagnatiAbstract:Extracting useful facts from large datasets has always been a challenging and critical issue for both research and industry. We present Vizpicious, a tool which borrows some ideas from social network analysis and semantic web to help investigators with such tasks, with a simple to use interface supporting them from the data integration phase until the analysis and the extraction of useful facts, and then provides more complex querying-based analysis capabilities.
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Web Intelligence - Vizpicious: A Visual User-Adaptive Tool for Communication Logs Analysis and Suspicious Behavior Detection
2012 IEEE WIC ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2012Co-Authors: Amyn Bennamane, Hakim Hacid, Arnaud Ansiaux, Alain CagnatiAbstract:Extracting useful facts from large datasets has always been a challenging and critical issue for both research and industry. We present Vizpicious, a tool which borrows some ideas from social network analysis and semantic web to help investigators with such tasks, with a simple to use interface supporting them from the data integration phase until the analysis and the extraction of useful facts, and then provides more complex querying-based analysis capabilities.
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visual analysis of implicit social networks for Suspicious Behavior detection
Database Systems for Advanced Applications, 2011Co-Authors: Amyn Bennamane, Hakim Hacid, Arnaud Ansiaux, Alain CagnatiAbstract:In this paper we show how social networks, implicitly built from communication data, can serve as a basis for Suspicious Behavior detection from large communications data (landlines and mobile phone calls) provided by communication services providers for criminal investigators following two procedures: lawful interception and data retention. We propose the following contributions: (i) a data model and a set of operators for querying this data in order to extract Suspicious Behavior and (ii) a user friendly and easy-to-navigate visual representation for communication data with a prototype implementation.
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DASFAA (2) - Visual analysis of implicit social networks for Suspicious Behavior detection
Database Systems for Advanced Applications, 2011Co-Authors: Amyn Bennamane, Hakim Hacid, Arnaud Ansiaux, Alain CagnatiAbstract:In this paper we show how social networks, implicitly built from communication data, can serve as a basis for Suspicious Behavior detection from large communications data (landlines and mobile phone calls) provided by communication services providers for criminal investigators following two procedures: lawful interception and data retention. We propose the following contributions: (i) a data model and a set of operators for querying this data in order to extract Suspicious Behavior and (ii) a user friendly and easy-to-navigate visual representation for communication data with a prototype implementation.
Adam G. Pennington - One of the best experts on this subject based on the ideXlab platform.
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USENIX Security Symposium - Storage-based intrusion detection: watching storage activity for Suspicious Behavior
2003Co-Authors: Adam G. Pennington, John D. Strunk, John Linwood Griffin, Craig A. N. Soules, Garth R Goodson, Gregory R. GangerAbstract:Storage-based intrusion detection allows storage systems to watch for data modifications characteristic of system intrusions. This enables storage systems to spot several common intruder actions, such as adding backdoors, inserting Trojan horses, and tampering with audit logs. Further, an intrusion detection system (IDS) embedded in a storage device continues to operate even after client systems are compromised. This paper describes a number of specific warning signs visible at the storage interface. Examination of 18 real intrusion tools reveals that most (15) can be detected based on their changes to stored files. We describe and evaluate a prototype storage IDS, embedded in an NFS server, to demonstrate both feasibility and efficiency of storage-based intrusion detection. In particular, both the performance overhead and memory required (152KB for 4730 rules) are minimal.
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storage based intrusion detection watching storage activity for Suspicious Behavior
USENIX Security Symposium, 2003Co-Authors: Adam G. Pennington, John D. Strunk, John Linwood Griffin, Craig A. N. Soules, Garth R Goodson, Gregory R. GangerAbstract:Storage-based intrusion detection allows storage systems to watch for data modifications characteristic of system intrusions. This enables storage systems to spot several common intruder actions, such as adding backdoors, inserting Trojan horses, and tampering with audit logs. Further, an intrusion detection system (IDS) embedded in a storage device continues to operate even after client systems are compromised. This paper describes a number of specific warning signs visible at the storage interface. Examination of 18 real intrusion tools reveals that most (15) can be detected based on their changes to stored files. We describe and evaluate a prototype storage IDS, embedded in an NFS server, to demonstrate both feasibility and efficiency of storage-based intrusion detection. In particular, both the performance overhead and memory required (152KB for 4730 rules) are minimal.
D Arsic - One of the best experts on this subject based on the ideXlab platform.
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a hierarchical approach for visual Suspicious Behavior detection in aircrafts
International Conference on Digital Signal Processing, 2009Co-Authors: D Arsic, Benedikt Hornler, Bjorn Schuller, Gerhard RigollAbstract:Recently great interest has been shown in the visual surveillance of public transportation systems. The challenge is the automated analysis of passenger's Behaviors with a set of visual low-level features, which can be extracted robustly. On a set of global motion features computed in different parts of the image, here the complete image, the face and skin color regions, a classification with Support Vector Machines is performed. Test-runs on a database of aggressive, cheerful, intoxicated, nervous, neutral and tired Behavior.
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Suspicious Behavior detection in public transport by fusion of low level video descriptors
International Conference on Multimedia and Expo, 2007Co-Authors: D Arsic, Bjorn Schuller, Gerhard RigollAbstract:Great interest has been shown in the visual surveillance of public transportation systems. The challenge is the automated analysis of passengers' Behaviors with a set of visual low-level features, which can be extracted robustly. On a set of global motion features computed in different parts of the image, here the complete image, the face and skin color regions, a classification with support vector machines is performed. Test-runs on a database of aggressive, cheerful, intoxicated, nervous, neutral and tired Behavior in an airplane situation show promising results.
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ICME - Suspicious Behavior Detection in Public Transport by Fusion of Low-Level Video Descriptors
Multimedia and Expo 2007 IEEE International Conference on, 2007Co-Authors: D Arsic, Bjorn Schuller, Gerhard RigollAbstract:Great interest has been shown in the visual surveillance of public transportation systems. The challenge is the automated analysis of passengers' Behaviors with a set of visual low-level features, which can be extracted robustly. On a set of global motion features computed in different parts of the image, here the complete image, the face and skin color regions, a classification with support vector machines is performed. Test-runs on a database of aggressive, cheerful, intoxicated, nervous, neutral and tired Behavior in an airplane situation show promising results.
