The Experts below are selected from a list of 210042 Experts worldwide ranked by ideXlab platform
Yedid Hoshen - One of the best experts on this subject based on the ideXlab platform.
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VAIN: Attentional Multi-agent Predictive Modeling
arXiv: Learning, 2017Co-Authors: Yedid HoshenAbstract:Multi-agent Predictive Modeling is an essential step for understanding physical, social and team-play systems. Recently, Interaction Networks (INs) were proposed for the task of Modeling multi-agent physical systems, INs scale with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce VAIN, a novel attentional architecture for multi-agent Predictive Modeling that scales linearly with the number of agents. We show that VAIN is effective for multi-agent Predictive Modeling. Our method is evaluated on tasks from challenging multi-agent prediction domains: chess and soccer, and outperforms competing multi-agent approaches.
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vain attentional multi agent Predictive Modeling
Neural Information Processing Systems, 2017Co-Authors: Yedid HoshenAbstract:Multi-agent Predictive Modeling is an essential step for understanding physical, social and team-play systems. Recently, Interaction Networks (INs) were proposed for the task of Modeling multi-agent physical systems. One of the drawbacks of INs is scaling with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce VAIN, a novel attentional architecture for multi-agent Predictive Modeling that scales linearly with the number of agents. We show that VAIN is effective for multi-agent Predictive Modeling. Our method is evaluated on tasks from challenging multi-agent prediction domains: chess and soccer, and outperforms competing multi-agent approaches.
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Multi-agent Predictive Modeling with Attentional CommNets
2017Co-Authors: Yedid HoshenAbstract:Multi-agent Predictive Modeling is an essential step for understanding physical, social and team-play systems. Recently, Interaction Networks (INs) were proposed for the task of Modeling multi-agent physical systems, INs scale with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce VAIN, an Attentional CommNet for multi-agent Predictive Modeling that scales linearly with the number of agents. We show that VAIN is effective for multi-agent Predictive Modeling and the representation learned is transferable to learning new data-poor tasks. Our method is evaluated on tasks from challenging multi-agent prediction domains: chess and soccer, and outperforms competing multi-agent approaches.
John R Williams - One of the best experts on this subject based on the ideXlab platform.
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a malicious activity detection system utilizing Predictive Modeling in complex environments
Consumer Communications and Networking Conference, 2014Co-Authors: Abdullah Almaatouq, Ahmad Alabdulkareem, Mariam Nouh, Mansour Alsaleh, Abdulrahman Alarifi, Abel Sanchez, Anas Alfaris, John R WilliamsAbstract:Complex enterprise environments consist of globally distributed infrastructure with a variety of applications and a large number of activities occurring on a daily basis. This increases the attack surface and narrows the view of ongoing intrinsic dynamics. Thus, many malicious activities can persist under the radar of conventional detection mechanisms long enough to achieve critical mass for full-fledged cyber attacks. Many of the typical detection approaches are signature-based and thus are expected to fail in the face of zero-day attacks. In this paper, we present the building-blocks for developing a Malicious Activity Detection System (MADS). MADS employs Predictive Modeling techniques for the detection of malicious activities. Unlike traditional detection mechanisms, MADS includes the detection of both network-based intrusions and malicious user behaviors. The system utilizes a simulator to produce holistic replication of activities, including both benign and malicious, flowing within a given complex IT environment. We validate the performance and accuracy of the simulator through a case study of a Fortune 500 company where we compare the results of the simulated infrastructure against the physical one in terms of resource consumption (i.e., CPU utilization), the number of concurrent users, and response times. In addition to an evaluation of the detection algorithms with varying hyper-parameters and comparing the results.
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CCNC - A malicious activity detection system utilizing Predictive Modeling in complex environments
2014 IEEE 11th Consumer Communications and Networking Conference (CCNC), 2014Co-Authors: Abdullah Almaatouq, Ahmad Alabdulkareem, Mariam Nouh, Mansour Alsaleh, Abdulrahman Alarifi, Abel Sanchez, Anas Alfaris, John R WilliamsAbstract:Complex enterprise environments consist of globally distributed infrastructure with a variety of applications and a large number of activities occurring on a daily basis. This increases the attack surface and narrows the view of ongoing intrinsic dynamics. Thus, many malicious activities can persist under the radar of conventional detection mechanisms long enough to achieve critical mass for full-fledged cyber attacks. Many of the typical detection approaches are signature-based and thus are expected to fail in the face of zero-day attacks. In this paper, we present the building-blocks for developing a Malicious Activity Detection System (MADS). MADS employs Predictive Modeling techniques for the detection of malicious activities. Unlike traditional detection mechanisms, MADS includes the detection of both network-based intrusions and malicious user behaviors. The system utilizes a simulator to produce holistic replication of activities, including both benign and malicious, flowing within a given complex IT environment. We validate the performance and accuracy of the simulator through a case study of a Fortune 500 company where we compare the results of the simulated infrastructure against the physical one in terms of resource consumption (i.e., CPU utilization), the number of concurrent users, and response times. In addition to an evaluation of the detection algorithms with varying hyper-parameters and comparing the results.
Abdullah Almaatouq - One of the best experts on this subject based on the ideXlab platform.
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a malicious activity detection system utilizing Predictive Modeling in complex environments
Consumer Communications and Networking Conference, 2014Co-Authors: Abdullah Almaatouq, Ahmad Alabdulkareem, Mariam Nouh, Mansour Alsaleh, Abdulrahman Alarifi, Abel Sanchez, Anas Alfaris, John R WilliamsAbstract:Complex enterprise environments consist of globally distributed infrastructure with a variety of applications and a large number of activities occurring on a daily basis. This increases the attack surface and narrows the view of ongoing intrinsic dynamics. Thus, many malicious activities can persist under the radar of conventional detection mechanisms long enough to achieve critical mass for full-fledged cyber attacks. Many of the typical detection approaches are signature-based and thus are expected to fail in the face of zero-day attacks. In this paper, we present the building-blocks for developing a Malicious Activity Detection System (MADS). MADS employs Predictive Modeling techniques for the detection of malicious activities. Unlike traditional detection mechanisms, MADS includes the detection of both network-based intrusions and malicious user behaviors. The system utilizes a simulator to produce holistic replication of activities, including both benign and malicious, flowing within a given complex IT environment. We validate the performance and accuracy of the simulator through a case study of a Fortune 500 company where we compare the results of the simulated infrastructure against the physical one in terms of resource consumption (i.e., CPU utilization), the number of concurrent users, and response times. In addition to an evaluation of the detection algorithms with varying hyper-parameters and comparing the results.
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CCNC - A malicious activity detection system utilizing Predictive Modeling in complex environments
2014 IEEE 11th Consumer Communications and Networking Conference (CCNC), 2014Co-Authors: Abdullah Almaatouq, Ahmad Alabdulkareem, Mariam Nouh, Mansour Alsaleh, Abdulrahman Alarifi, Abel Sanchez, Anas Alfaris, John R WilliamsAbstract:Complex enterprise environments consist of globally distributed infrastructure with a variety of applications and a large number of activities occurring on a daily basis. This increases the attack surface and narrows the view of ongoing intrinsic dynamics. Thus, many malicious activities can persist under the radar of conventional detection mechanisms long enough to achieve critical mass for full-fledged cyber attacks. Many of the typical detection approaches are signature-based and thus are expected to fail in the face of zero-day attacks. In this paper, we present the building-blocks for developing a Malicious Activity Detection System (MADS). MADS employs Predictive Modeling techniques for the detection of malicious activities. Unlike traditional detection mechanisms, MADS includes the detection of both network-based intrusions and malicious user behaviors. The system utilizes a simulator to produce holistic replication of activities, including both benign and malicious, flowing within a given complex IT environment. We validate the performance and accuracy of the simulator through a case study of a Fortune 500 company where we compare the results of the simulated infrastructure against the physical one in terms of resource consumption (i.e., CPU utilization), the number of concurrent users, and response times. In addition to an evaluation of the detection algorithms with varying hyper-parameters and comparing the results.
Anas Alfaris - One of the best experts on this subject based on the ideXlab platform.
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a malicious activity detection system utilizing Predictive Modeling in complex environments
Consumer Communications and Networking Conference, 2014Co-Authors: Abdullah Almaatouq, Ahmad Alabdulkareem, Mariam Nouh, Mansour Alsaleh, Abdulrahman Alarifi, Abel Sanchez, Anas Alfaris, John R WilliamsAbstract:Complex enterprise environments consist of globally distributed infrastructure with a variety of applications and a large number of activities occurring on a daily basis. This increases the attack surface and narrows the view of ongoing intrinsic dynamics. Thus, many malicious activities can persist under the radar of conventional detection mechanisms long enough to achieve critical mass for full-fledged cyber attacks. Many of the typical detection approaches are signature-based and thus are expected to fail in the face of zero-day attacks. In this paper, we present the building-blocks for developing a Malicious Activity Detection System (MADS). MADS employs Predictive Modeling techniques for the detection of malicious activities. Unlike traditional detection mechanisms, MADS includes the detection of both network-based intrusions and malicious user behaviors. The system utilizes a simulator to produce holistic replication of activities, including both benign and malicious, flowing within a given complex IT environment. We validate the performance and accuracy of the simulator through a case study of a Fortune 500 company where we compare the results of the simulated infrastructure against the physical one in terms of resource consumption (i.e., CPU utilization), the number of concurrent users, and response times. In addition to an evaluation of the detection algorithms with varying hyper-parameters and comparing the results.
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CCNC - A malicious activity detection system utilizing Predictive Modeling in complex environments
2014 IEEE 11th Consumer Communications and Networking Conference (CCNC), 2014Co-Authors: Abdullah Almaatouq, Ahmad Alabdulkareem, Mariam Nouh, Mansour Alsaleh, Abdulrahman Alarifi, Abel Sanchez, Anas Alfaris, John R WilliamsAbstract:Complex enterprise environments consist of globally distributed infrastructure with a variety of applications and a large number of activities occurring on a daily basis. This increases the attack surface and narrows the view of ongoing intrinsic dynamics. Thus, many malicious activities can persist under the radar of conventional detection mechanisms long enough to achieve critical mass for full-fledged cyber attacks. Many of the typical detection approaches are signature-based and thus are expected to fail in the face of zero-day attacks. In this paper, we present the building-blocks for developing a Malicious Activity Detection System (MADS). MADS employs Predictive Modeling techniques for the detection of malicious activities. Unlike traditional detection mechanisms, MADS includes the detection of both network-based intrusions and malicious user behaviors. The system utilizes a simulator to produce holistic replication of activities, including both benign and malicious, flowing within a given complex IT environment. We validate the performance and accuracy of the simulator through a case study of a Fortune 500 company where we compare the results of the simulated infrastructure against the physical one in terms of resource consumption (i.e., CPU utilization), the number of concurrent users, and response times. In addition to an evaluation of the detection algorithms with varying hyper-parameters and comparing the results.
Ahmad Alabdulkareem - One of the best experts on this subject based on the ideXlab platform.
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a malicious activity detection system utilizing Predictive Modeling in complex environments
Consumer Communications and Networking Conference, 2014Co-Authors: Abdullah Almaatouq, Ahmad Alabdulkareem, Mariam Nouh, Mansour Alsaleh, Abdulrahman Alarifi, Abel Sanchez, Anas Alfaris, John R WilliamsAbstract:Complex enterprise environments consist of globally distributed infrastructure with a variety of applications and a large number of activities occurring on a daily basis. This increases the attack surface and narrows the view of ongoing intrinsic dynamics. Thus, many malicious activities can persist under the radar of conventional detection mechanisms long enough to achieve critical mass for full-fledged cyber attacks. Many of the typical detection approaches are signature-based and thus are expected to fail in the face of zero-day attacks. In this paper, we present the building-blocks for developing a Malicious Activity Detection System (MADS). MADS employs Predictive Modeling techniques for the detection of malicious activities. Unlike traditional detection mechanisms, MADS includes the detection of both network-based intrusions and malicious user behaviors. The system utilizes a simulator to produce holistic replication of activities, including both benign and malicious, flowing within a given complex IT environment. We validate the performance and accuracy of the simulator through a case study of a Fortune 500 company where we compare the results of the simulated infrastructure against the physical one in terms of resource consumption (i.e., CPU utilization), the number of concurrent users, and response times. In addition to an evaluation of the detection algorithms with varying hyper-parameters and comparing the results.
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CCNC - A malicious activity detection system utilizing Predictive Modeling in complex environments
2014 IEEE 11th Consumer Communications and Networking Conference (CCNC), 2014Co-Authors: Abdullah Almaatouq, Ahmad Alabdulkareem, Mariam Nouh, Mansour Alsaleh, Abdulrahman Alarifi, Abel Sanchez, Anas Alfaris, John R WilliamsAbstract:Complex enterprise environments consist of globally distributed infrastructure with a variety of applications and a large number of activities occurring on a daily basis. This increases the attack surface and narrows the view of ongoing intrinsic dynamics. Thus, many malicious activities can persist under the radar of conventional detection mechanisms long enough to achieve critical mass for full-fledged cyber attacks. Many of the typical detection approaches are signature-based and thus are expected to fail in the face of zero-day attacks. In this paper, we present the building-blocks for developing a Malicious Activity Detection System (MADS). MADS employs Predictive Modeling techniques for the detection of malicious activities. Unlike traditional detection mechanisms, MADS includes the detection of both network-based intrusions and malicious user behaviors. The system utilizes a simulator to produce holistic replication of activities, including both benign and malicious, flowing within a given complex IT environment. We validate the performance and accuracy of the simulator through a case study of a Fortune 500 company where we compare the results of the simulated infrastructure against the physical one in terms of resource consumption (i.e., CPU utilization), the number of concurrent users, and response times. In addition to an evaluation of the detection algorithms with varying hyper-parameters and comparing the results.
