The Experts below are selected from a list of 33 Experts worldwide ranked by ideXlab platform
Irwin King - One of the best experts on this subject based on the ideXlab platform.
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IJCNN - Exploit of online social networks with Semi-Supervised Learning
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Co-Authors: Dingyan Wang, Dan Hong, Irwin KingAbstract:With the rapid growth of the Internet, more and more people interact with their friends in online social networks like Facebook1. Current online social networks have designed some strategies to protect users' privacy, but they are not stringent enough. Some public information of profile or relationship can be utilized to infer users' private information. Online social networks usually contain little public available information of users (labeled data) but with a large number of hidden ones (unlabeled data). Recently, Semi-Supervised Learning (SSL), which has the advantage of utilizing fewer labeled data to achieve better performance compared to classical Supervised Learning, attracts much attention from the web research community with a massive set of unlabeled data. In our paper, we focus on the privacy issue of online social networks, which is a hot and dynamic research topic. More specifically, we propose a novel SSL Framework that can be used to Exploit security issues in online social networks. We first introduce the general SSL Framework and outline two Exploit models with associated strategies within it, e.g., graph-based models and co-training model. Finally, we conduct a series of experiments on real-world data from Facebook and StudiVZ2 to evaluate the effectiveness of this SSL Exploit Framework. Experimental results demonstrate that our approaches can accurately infer sensitive information of online users and more effective compared to previous models.
Dave Aitel - One of the best experts on this subject based on the ideXlab platform.
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Case Study 3.1 – InlineEgg I
Buffer Overflow Attacks, 2005Co-Authors: James C. Foster, Vitaly Osipov, Nish Bhalla, Niels Heinen, Dave AitelAbstract:Publisher Summary This chapter discusses a case study involving InlineEgg I. InlineEgg was created by the researchers at CORE Serial Digital Interface (SDI) to help accomplish a dynamic and extendable Exploit Framework for their product suite. It creates shellcode for multiple syscalls on multiple platforms that can be quickly utilized within Python scripts. Hands-down—their implementation of shell creation—is the market leading technology. The example in this chapter is pulled from InlineEgg's documentation, which was analyzed by engineers to help in understanding how Python can be effective in commercial-grade applications.
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Case Study 3.2 – InlineEgg II
Buffer Overflow Attacks, 2005Co-Authors: James C. Foster, Vitaly Osipov, Nish Bhalla, Niels Heinen, Dave AitelAbstract:Publisher Summary This chapter discusses a case study involving InlineEgg II. Embedding shellcode with InlineEgg is described. InlineEgg was created by researchers at CORE Serial Digital Interface (SDI) to help accomplish a dynamic and extendable Exploit Framework for their product suite. It creates shellcode for multiple syscalls on multiple platforms that can be quickly utilized within Python scripts. Hands-down—their implementation of shell creation—is the market leading technology. Having familiarized with the InlineEgg Application Programming Interface (API), this chapter tackles another example that is a bit more complicated. This example uses a combination of techniques to generate the appropriate shellcode embedded within a looping condition.
Dumoulin Benoit F - One of the best experts on this subject based on the ideXlab platform.
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Explore-Exploit: A Framework for Interactive and Online Learning.
arXiv: Learning, 2018Co-Authors: Honglei Liu, Anuj Kumar, Yang Wenhai, Dumoulin Benoit FAbstract:Interactive user interfaces need to continuously evolve based on the interactions that a user has (or does not have) with the system. This may require constant exploration of various options that the system may have for the user and obtaining signals of user preferences on those. However, such an exploration, especially when the set of available options itself can change frequently, can lead to sub-optimal user experiences. We present Explore-Exploit: a Framework designed to collect and utilize user feedback in an interactive and online setting that minimizes regressions in end-user experience. This Framework provides a suite of online learning operators for various tasks such as personalization ranking, candidate selection and active learning. We demonstrate how to integrate this Framework with run-time services to leverage online and interactive machine learning out-of-the-box. We also present results demonstrating the efficiencies that can be achieved using the Explore-Exploit Framework.
Dingyan Wang - One of the best experts on this subject based on the ideXlab platform.
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IJCNN - Exploit of online social networks with Semi-Supervised Learning
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Co-Authors: Dingyan Wang, Dan Hong, Irwin KingAbstract:With the rapid growth of the Internet, more and more people interact with their friends in online social networks like Facebook1. Current online social networks have designed some strategies to protect users' privacy, but they are not stringent enough. Some public information of profile or relationship can be utilized to infer users' private information. Online social networks usually contain little public available information of users (labeled data) but with a large number of hidden ones (unlabeled data). Recently, Semi-Supervised Learning (SSL), which has the advantage of utilizing fewer labeled data to achieve better performance compared to classical Supervised Learning, attracts much attention from the web research community with a massive set of unlabeled data. In our paper, we focus on the privacy issue of online social networks, which is a hot and dynamic research topic. More specifically, we propose a novel SSL Framework that can be used to Exploit security issues in online social networks. We first introduce the general SSL Framework and outline two Exploit models with associated strategies within it, e.g., graph-based models and co-training model. Finally, we conduct a series of experiments on real-world data from Facebook and StudiVZ2 to evaluate the effectiveness of this SSL Exploit Framework. Experimental results demonstrate that our approaches can accurately infer sensitive information of online users and more effective compared to previous models.
James C. Foster - One of the best experts on this subject based on the ideXlab platform.
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Case Study 3.1 – InlineEgg I
Buffer Overflow Attacks, 2005Co-Authors: James C. Foster, Vitaly Osipov, Nish Bhalla, Niels Heinen, Dave AitelAbstract:Publisher Summary This chapter discusses a case study involving InlineEgg I. InlineEgg was created by the researchers at CORE Serial Digital Interface (SDI) to help accomplish a dynamic and extendable Exploit Framework for their product suite. It creates shellcode for multiple syscalls on multiple platforms that can be quickly utilized within Python scripts. Hands-down—their implementation of shell creation—is the market leading technology. The example in this chapter is pulled from InlineEgg's documentation, which was analyzed by engineers to help in understanding how Python can be effective in commercial-grade applications.
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Case Study 3.2 – InlineEgg II
Buffer Overflow Attacks, 2005Co-Authors: James C. Foster, Vitaly Osipov, Nish Bhalla, Niels Heinen, Dave AitelAbstract:Publisher Summary This chapter discusses a case study involving InlineEgg II. Embedding shellcode with InlineEgg is described. InlineEgg was created by researchers at CORE Serial Digital Interface (SDI) to help accomplish a dynamic and extendable Exploit Framework for their product suite. It creates shellcode for multiple syscalls on multiple platforms that can be quickly utilized within Python scripts. Hands-down—their implementation of shell creation—is the market leading technology. Having familiarized with the InlineEgg Application Programming Interface (API), this chapter tackles another example that is a bit more complicated. This example uses a combination of techniques to generate the appropriate shellcode embedded within a looping condition.
