The Experts below are selected from a list of 1566 Experts worldwide ranked by ideXlab platform

Sanjay Jha - One of the best experts on this subject based on the ideXlab platform.

  • A Survey of Securing Networks Using Software Defined Networking
    IEEE Transactions on Reliability, 2015
    Co-Authors: Syed Taha Ali, Adam Radford, Vijay Sivaraman, Sanjay Jha
    Abstract:

    © 2015 IEEE. Software Defined Networking (SDN) is rapidly emerging as a new paradigm for managing and controlling the operation of Networks ranging from the data center to the core, enterprise, and home. The logical centralization of Network Intelligence presents exciting challenges and opportunities to enhance security in such Networks, including new ways to prevent, detect, and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. In this paper, we undertake a comprehensive survey of recent works that apply SDN to security, and identify promising future directions that can be addressed by such research.

  • Securing Networks Using Software Defined Networking : A Survey
    IEEE Transactions on Reliability, 2013
    Co-Authors: Syed Taha Ali, Alec Radford, Adam Radford, Vijay Sivaraman, Sanjay Jha
    Abstract:

    Software Defined Networking (SDN) is rapidly emerging as a new paradigm for managing and controlling the operation of Networks ranging from the data center to core, enterprise and home. The logical centralization of Network Intelligence presents exciting challenges and opportunities to enhance security in such Networks, including new ways to prevent, detect and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. In this paper we undertake a comprehensive survey of recent works that apply SDN to security, and identify promising future directions that can be addressed by such research.

Ariel Felner - One of the best experts on this subject based on the ideXlab platform.

  • Target oriented Network Intelligence collection: effective exploration of social Networks
    World Wide Web, 2019
    Co-Authors: Rami Puzis, Roni Stern, Liron Kachko, Barak Hagbi, Ariel Felner
    Abstract:

    Target Oriented Network Intelligence Collection (TONIC) is a crawling process whose goal is to find social Network profiles that contain information about a given target. Such profiles are called leads and the TONIC problem is how to minimize crawling costs incurred while finding them. We model this problem as a search problem in an unknown graph and present a best-first search approach for solving it. Three key challenges are (1) which profiles to consider crawling to, (2) how to prioritize the crawling order, and (3) when additional crawling is not worthwhile. For the first challenge, we propose two frameworks: the Restricted TONIC Framework (RTF), that restricts the search to immediate neighbors of previously found leads, and the Extended TONIC Framework (ETF), that extends the scope of the search to a wider neighborhood. Guidelines for when to choose which framework are provided. For the second challenge, we propose a set of effective topology-based heuristics that guide the search towards profiles that are more likely to be leads. For the third challenge, we propose to use data collected in previously executed crawls to learn when additional crawling is expected to be useful.

  • SOCS - Extended Framework for Target Oriented Network Intelligence Collection
    2014
    Co-Authors: Liron Samama-kachko, Roni Stern, Rami Puzis, Ariel Felner
    Abstract:

    The Target Oriented Network Intelligence Collection (TONIC) problem is the problem of finding profiles in a social Network that contain publicly available information about a given target profile via automated crawling. Such profiles are called leads. Leads can be found by crawling the Network using the profiles' friend lists (immediate neighborhood) in order to decide which profile will be crawled next. Assuming that leads tend to cluster together, prior work limited the search for new leads only to immediate neighbors of the leads previously found. In this paper we relax this limitation, and extend the scope of the search to a wider neighborhood, including the possibility of crawling to non-leads, i.e., profiles that have no publicly available information about the target. We propose a set of heuristics that guide this search. Experimental results show that with the new setting more leads can be found and leads are found faster. In addition, we perform a cost benefit analysis of the search, weighing the reward of finding leads with the costs of the search.

  • tonic target oriented Network Intelligence collection for the social web
    National Conference on Artificial Intelligence, 2013
    Co-Authors: Roni Stern, Liron Samama, Rami Puzis, Tal Beja, Zahy Bnaya, Ariel Felner
    Abstract:

    In this paper we introduce the Target Oriented Network Intelligence Collection (TONIC) problem, which is the problem of finding profiles in a social Network that contain information about a given target via automated crawling. We formalize TONIC as a search problem and a best-first approach is proposed for solving it. Several heuristics are presented to guide this search. These heuristics are based on the topology of the currently known part of the social Network. The efficiency of the proposed heuristics and the effect of the graph topology on their performance is experimentally evaluated on the Google+ social Network.

  • AAAI - TONIC: target oriented Network Intelligence collection for the social web
    2013
    Co-Authors: Roni Stern, Liron Samama, Rami Puzis, Tal Beja, Zahy Bnaya, Ariel Felner
    Abstract:

    In this paper we introduce the Target Oriented Network Intelligence Collection (TONIC) problem, which is the problem of finding profiles in a social Network that contain information about a given target via automated crawling. We formalize TONIC as a search problem and a best-first approach is proposed for solving it. Several heuristics are presented to guide this search. These heuristics are based on the topology of the currently known part of the social Network. The efficiency of the proposed heuristics and the effect of the graph topology on their performance is experimentally evaluated on the Google+ social Network.

Syed Taha Ali - One of the best experts on this subject based on the ideXlab platform.

  • A Survey of Securing Networks Using Software Defined Networking
    IEEE Transactions on Reliability, 2015
    Co-Authors: Syed Taha Ali, Adam Radford, Vijay Sivaraman, Sanjay Jha
    Abstract:

    © 2015 IEEE. Software Defined Networking (SDN) is rapidly emerging as a new paradigm for managing and controlling the operation of Networks ranging from the data center to the core, enterprise, and home. The logical centralization of Network Intelligence presents exciting challenges and opportunities to enhance security in such Networks, including new ways to prevent, detect, and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. In this paper, we undertake a comprehensive survey of recent works that apply SDN to security, and identify promising future directions that can be addressed by such research.

  • Securing Networks Using Software Defined Networking : A Survey
    IEEE Transactions on Reliability, 2013
    Co-Authors: Syed Taha Ali, Alec Radford, Adam Radford, Vijay Sivaraman, Sanjay Jha
    Abstract:

    Software Defined Networking (SDN) is rapidly emerging as a new paradigm for managing and controlling the operation of Networks ranging from the data center to core, enterprise and home. The logical centralization of Network Intelligence presents exciting challenges and opportunities to enhance security in such Networks, including new ways to prevent, detect and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. In this paper we undertake a comprehensive survey of recent works that apply SDN to security, and identify promising future directions that can be addressed by such research.

Yuan-chu Hwang - One of the best experts on this subject based on the ideXlab platform.

  • Mining Proximal Social Network Intelligence for Quality Decision Support
    2009 International Conference on Advances in Social Network Analysis and Mining, 2009
    Co-Authors: Yuan-chu Hwang
    Abstract:

    The concepts of proximity have been utilized for exploring both psychological and geographical incentives for users within social Networks to collaborate with others for mutual goals. The massive information does not facilitate quality decision support. In this paper, we focus on mining the proximal social Network Intelligence for quality decision support. The utilization of investigating both the context and the content of the application domain from social Network relationships would highly improve the information quality for better decisions. Mining proximal social Network Intelligence from both context and content enable quality decision making. We illustrate a case of leisure recommendation e-service for bicycle exercise entertainment in Taiwan. We introduce the proximity e-service as well as its theoretical support.The most recent personalized experience according to its context provides remarkable perceptual data from unique information sources. Moreover, the social Network relationships extend the power of the unique perceptual information to converge as the collective social Network Intelligence.

  • ASONAM - Mining Proximal Social Network Intelligence for Quality Decision Support
    2009 International Conference on Advances in Social Network Analysis and Mining, 2009
    Co-Authors: Yuan-chu Hwang
    Abstract:

    The concepts of proximity have been utilized for exploring both psychological and geographical incentives for users within social Networks to collaborate with others for mutual goals. The massive information does not facilitate quality decision support. In this paper, we focus on mining the proximal social Network Intelligence for quality decision support. The utilization of investigating both the context and the content of the application domain from social Network relationships would highly improve the information quality for better decisions.Mining proximal social Network Intelligence from both context and content enable quality decision making. We illustrate a case of leisure recommendation e-service for bicycle exercise entertainment in Taiwan. We introduce the proximity e-service as well as its theoretical support.The most recent personalized experience according to its context provides remarkable perceptual data from unique information sources. Moreover, the social Network relationships extend the power of the unique perceptual information to converge as the collective social Network Intelligence.

Vijay Sivaraman - One of the best experts on this subject based on the ideXlab platform.

  • A Survey of Securing Networks Using Software Defined Networking
    IEEE Transactions on Reliability, 2015
    Co-Authors: Syed Taha Ali, Adam Radford, Vijay Sivaraman, Sanjay Jha
    Abstract:

    © 2015 IEEE. Software Defined Networking (SDN) is rapidly emerging as a new paradigm for managing and controlling the operation of Networks ranging from the data center to the core, enterprise, and home. The logical centralization of Network Intelligence presents exciting challenges and opportunities to enhance security in such Networks, including new ways to prevent, detect, and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. In this paper, we undertake a comprehensive survey of recent works that apply SDN to security, and identify promising future directions that can be addressed by such research.

  • Securing Networks Using Software Defined Networking : A Survey
    IEEE Transactions on Reliability, 2013
    Co-Authors: Syed Taha Ali, Alec Radford, Adam Radford, Vijay Sivaraman, Sanjay Jha
    Abstract:

    Software Defined Networking (SDN) is rapidly emerging as a new paradigm for managing and controlling the operation of Networks ranging from the data center to core, enterprise and home. The logical centralization of Network Intelligence presents exciting challenges and opportunities to enhance security in such Networks, including new ways to prevent, detect and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. In this paper we undertake a comprehensive survey of recent works that apply SDN to security, and identify promising future directions that can be addressed by such research.