The Experts below are selected from a list of 96 Experts worldwide ranked by ideXlab platform
Peter Dolog - One of the best experts on this subject based on the ideXlab platform.
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TABOO: Detecting Unstructured Sensitive Information Using Recursive Neural Networks
2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017Co-Authors: Jan Neerbeky, Ira Assentz, Peter DologAbstract:Leak of sensitive information from unstructured text documents is a costly problem both for government and for industrial institutions. Traditional approaches for Data Leak Prevention are commonly based on the hypothesis that sensitive information is reflected in the presence of distinct sensitive words. However, for complex sensitive information, this hypothesis may not hold. Our TABOO system detects complex sensitive information in text documents by learning the semantic and syntactic structure of text documents. Our approach is based on natural language processing methods for paraphrase detection, and uses recursive neural networks to assign sensitivity scores to the semantic components of the sentence structure. The demonstration of TABOO focuses on interactive detection of sensitive information with the TABOO system. Users may work with real documents, alter documents or prepare free text, and subject it to information detection. TABOO allows users to work with our TABOO engine or with traditional approaches, and to compare results. Users may verify that single words can change sensitivity according to context, thereby giving hands-on experience with complex cases of sensitive information.
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ICDE - TABOO: Detecting Unstructured Sensitive Information Using Recursive Neural Networks
2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017Co-Authors: Jan Neerbeky, Ira Assentz, Peter DologAbstract:Abstract-Leak of sensitive information from unstructured text documents is a costly problem both for government and for industrial institutions. Traditional approaches for Data Leak Prevention are commonly based on the hypothesis that sensitive information is reflected in the presence of distinct sensitive words. However, for complex sensitive information, this hypothesis may not hold.
Jan Neerbeky - One of the best experts on this subject based on the ideXlab platform.
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TABOO: Detecting Unstructured Sensitive Information Using Recursive Neural Networks
2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017Co-Authors: Jan Neerbeky, Ira Assentz, Peter DologAbstract:Leak of sensitive information from unstructured text documents is a costly problem both for government and for industrial institutions. Traditional approaches for Data Leak Prevention are commonly based on the hypothesis that sensitive information is reflected in the presence of distinct sensitive words. However, for complex sensitive information, this hypothesis may not hold. Our TABOO system detects complex sensitive information in text documents by learning the semantic and syntactic structure of text documents. Our approach is based on natural language processing methods for paraphrase detection, and uses recursive neural networks to assign sensitivity scores to the semantic components of the sentence structure. The demonstration of TABOO focuses on interactive detection of sensitive information with the TABOO system. Users may work with real documents, alter documents or prepare free text, and subject it to information detection. TABOO allows users to work with our TABOO engine or with traditional approaches, and to compare results. Users may verify that single words can change sensitivity according to context, thereby giving hands-on experience with complex cases of sensitive information.
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ICDE - TABOO: Detecting Unstructured Sensitive Information Using Recursive Neural Networks
2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017Co-Authors: Jan Neerbeky, Ira Assentz, Peter DologAbstract:Abstract-Leak of sensitive information from unstructured text documents is a costly problem both for government and for industrial institutions. Traditional approaches for Data Leak Prevention are commonly based on the hypothesis that sensitive information is reflected in the presence of distinct sensitive words. However, for complex sensitive information, this hypothesis may not hold.
Stefan Schmid - One of the best experts on this subject based on the ideXlab platform.
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PRI: Privacy Preserving Inspection of Encrypted Network Traffic
2016 IEEE Security and Privacy Workshops (SPW), 2016Co-Authors: Liron Schiff, Stefan SchmidAbstract:Traffic inspection is a fundamental building block of many security solutions today. For example, to prevent the Leakage or exfiltration of confidential insider information, as well as to block malicious traffic from entering the network, most enterprises today operate intrusion detection and Prevention systems that inspect traffic. However, the state-of-the-art inspection systems do not reflect well the interests of the different involved autonomous roles. For example, employees in an enterprise, or a company outsourcing its network management to a specialized third party, may require that their traffic remains confidential, even from the system administrator. Moreover, the rules used by the intrusion detection system, or more generally the configuration of an online or offline anomaly detection engine, may be provided by a third party, e.g., a security research firm, and can hence constitute a critical business asset which should be kept confidential. Today, it is often believed that accounting for these additional requirements is impossible, as they contradict efficiency and effectiveness. We in this paper explore a novel approach, called Privacy Preserving Inspection (PRI), which provides a solution to this problem, by preserving privacy of traffic inspection and confidentiality of inspection rules and configurations, and e.g., also supports the flexible installation of additional Data Leak Prevention (DLP) rules specific to the company.
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IEEE Symposium on Security and Privacy Workshops - PRI: Privacy Preserving Inspection of Encrypted Network Traffic
2016 IEEE Security and Privacy Workshops (SPW), 2016Co-Authors: Liron Schiff, Stefan SchmidAbstract:Traffic inspection is a fundamental building block of many security solutions today. For example, to prevent the Leakage or exfiltration of confidential insider information, as well as to block malicious traffic from entering the network, most enterprises today operate intrusion detection and Prevention systems that inspect traffic. However, the state-of-theart inspection systems do not reflect well the interests of the different involved autonomous roles. For example, employees in an enterprise, or a company outsourcing its network management to a specialized third party, may require that their traffic remains confidential, even from the system administrator. Moreover, the rules used by the intrusion detection system, or more generally the configuration of an online or offline anomaly detection engine, may be provided by a third party, e.g., a security research firm, and can hence constitute a critical business asset which should be kept confidential. Today, it is often believed that accounting for these additional requirements is impossible, as they contradict efficiency and effectiveness. We in this paper explore a novel approach, called Privacy Preserving Inspection (PRI), which provides a solution to this problem, by preserving privacy of traffic inspection and confidentiality of inspection rules and configurations, and e.g., also supports the flexible installation of additional Data Leak Prevention (DLP) rules specific to the company.
Ira Assentz - One of the best experts on this subject based on the ideXlab platform.
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TABOO: Detecting Unstructured Sensitive Information Using Recursive Neural Networks
2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017Co-Authors: Jan Neerbeky, Ira Assentz, Peter DologAbstract:Leak of sensitive information from unstructured text documents is a costly problem both for government and for industrial institutions. Traditional approaches for Data Leak Prevention are commonly based on the hypothesis that sensitive information is reflected in the presence of distinct sensitive words. However, for complex sensitive information, this hypothesis may not hold. Our TABOO system detects complex sensitive information in text documents by learning the semantic and syntactic structure of text documents. Our approach is based on natural language processing methods for paraphrase detection, and uses recursive neural networks to assign sensitivity scores to the semantic components of the sentence structure. The demonstration of TABOO focuses on interactive detection of sensitive information with the TABOO system. Users may work with real documents, alter documents or prepare free text, and subject it to information detection. TABOO allows users to work with our TABOO engine or with traditional approaches, and to compare results. Users may verify that single words can change sensitivity according to context, thereby giving hands-on experience with complex cases of sensitive information.
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ICDE - TABOO: Detecting Unstructured Sensitive Information Using Recursive Neural Networks
2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017Co-Authors: Jan Neerbeky, Ira Assentz, Peter DologAbstract:Abstract-Leak of sensitive information from unstructured text documents is a costly problem both for government and for industrial institutions. Traditional approaches for Data Leak Prevention are commonly based on the hypothesis that sensitive information is reflected in the presence of distinct sensitive words. However, for complex sensitive information, this hypothesis may not hold.
Steven J Simske - One of the best experts on this subject based on the ideXlab platform.
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EDOC - System Call Interception Framework for Data Leak Prevention
2011 IEEE 15th International Enterprise Distributed Object Computing Conference, 2011Co-Authors: Helen Balinsky, David Subiros Perez, Steven J SimskeAbstract:In this paper, we describe the feasibility and practical study of the recently proposed idea for Data Leak Prevention (DLP) based on end-point policy enforcement. The most reassuring way to prevent sensitive Data Leak is to thwart sensitive Data export before it has a chance to occur. Using a System Call Interception (SCI) technique we investigate the possibility of automatically detecting and amending a non-desired, policy breaching behavior at the "intention" stage: as the corresponding system call is called by an application, but before the action has been accomplished. The SCI method is especially valuable for "black box" applications, for which source code is not available. In our system, we catalog the system calls involved in the DLP events, and reduce our SCI to the minimum necessary set of system calls associated with the sensitive, DLP-requiring tasks. We describe the system behavior for several different applications that we have studied to date.
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System Call Interception Framework for Data Leak Prevention
2011 IEEE 15th International Enterprise Distributed Object Computing Conference, 2011Co-Authors: Helen Balinsky, David Subiros Perez, Steven J SimskeAbstract:In this paper, we describe the feasibility and practical study of the recently proposed idea for Data Leak Prevention (DLP) based on end-point policy enforcement. The most reassuring way to prevent sensitive Data Leak is to thwart sensitive Data export before it has a chance to occur. Using a System Call Interception (SCI) technique we investigate the possibility of automatically detecting and amending a non-desired, policy breaching behavior at the "intention" stage: as the corresponding system call is called by an application, but before the action has been accomplished. The SCI method is especially valuable for "black box" applications, for which source code is not available. In our system, we catalog the system calls involved in the DLP events, and reduce our SCI to the minimum necessary set of system calls associated with the sensitive, DLP-requiring tasks. We describe the system behavior for several different applications that we have studied to date.
