The Experts below are selected from a list of 501 Experts worldwide ranked by ideXlab platform
Wenhua Wang - One of the best experts on this subject based on the ideXlab platform.
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BIC-TA - The Binary Anti-Collision Algorithm Based on Labeled Packet
Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) 2013, 2013Co-Authors: Jianbin Xue, Lijing Qin, Wenhua WangAbstract:An improved algorithm based on label Packet (PABI) is put forward, which aimed at much times search and the large amount of communication data of current binary anti-collision algorithm. The new algorithm improve decoding accuracy by grouping labels within the field that reader identify by setting a counter on the label, extract conflicts bit then make up conflict block in the recognition process, and introduce the concept of matrix to express the possible decode of conflict block, thereby. The simulation results show that the algorithm have a significant advantage in the number of searches and the amount of communication data in the case of a large number of tags.
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the binary anti collision algorithm based on Labeled Packet
BIC-TA, 2013Co-Authors: Jianbin Xue, Lijing Qin, Wenhua WangAbstract:An improved algorithm based on label Packet (PABI) is put forward, which aimed at much times search and the large amount of communication data of current binary anti-collision algorithm. The new algorithm improve decoding accuracy by grouping labels within the field that reader identify by setting a counter on the label, extract conflicts bit then make up conflict block in the recognition process, and introduce the concept of matrix to express the possible decode of conflict block, thereby. The simulation results show that the algorithm have a significant advantage in the number of searches and the amount of communication data in the case of a large number of tags.
Catherine Helen Crawford - One of the best experts on this subject based on the ideXlab platform.
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IoTDI - DeviceMien: network device behavior modeling for identifying unknown IoT devices
Proceedings of the International Conference on Internet of Things Design and Implementation, 2019Co-Authors: Jorge Ortiz, Catherine Helen Crawford, Franck LeAbstract:With the explosion of IoT device use, networks are becoming more vulnerable to attack. Network administrators need better tools to verify and discover these devices in order to minimize attack risk. Existing tools provide rule-based assessment capabilities that cannot keep pace with the proliferation of devices. Current techniques demonstrate that given a rich set of Labeled Packet traces, one could design a pipeline that identifies all the devices in that trace with over 99% accuracy [30, 32]. However, it has also been observed [25], that such techniques are brittle when no labels are available. More perniciously, they provide false confidence scores about the label they do ascribe to a sample. This paper introduces a probabilistic framework for providing meaningful feedback in device identification, particularly when the device has not been previously observed. In our work, we use stacked autoencoders for automatically learning features from device traffic, learn the classes of traffic observed, and probabilistically model each device as a distribution of traffic classes. Our experiments show that we are able to identify previously seen devices after only 18.9 TCP-flow samples with 100% accuracy for devices where at least 50 samples are observed. We also show that we can distinguish between two broad classes of devices - IoT and Non-IoT - by examining the average number of flow classes observed over a set of samples. Our experiments show that we can infer the correct class of unseen devices with an over 82% average F1 score and 70% accuracy.
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devicemien network device behavior modeling for identifying unknown iot devices
The Internet of Things, 2019Co-Authors: Jorge Ortiz, Catherine Helen CrawfordAbstract:With the explosion of IoT device use, networks are becoming more vulnerable to attack. Network administrators need better tools to verify and discover these devices in order to minimize attack risk. Existing tools provide rule-based assessment capabilities that cannot keep pace with the proliferation of devices. Current techniques demonstrate that given a rich set of Labeled Packet traces, one could design a pipeline that identifies all the devices in that trace with over 99% accuracy [30, 32]. However, it has also been observed [25], that such techniques are brittle when no labels are available. More perniciously, they provide false confidence scores about the label they do ascribe to a sample. This paper introduces a probabilistic framework for providing meaningful feedback in device identification, particularly when the device has not been previously observed. In our work, we use stacked autoencoders for automatically learning features from device traffic, learn the classes of traffic observed, and probabilistically model each device as a distribution of traffic classes. Our experiments show that we are able to identify previously seen devices after only 18.9 TCP-flow samples with 100% accuracy for devices where at least 50 samples are observed. We also show that we can distinguish between two broad classes of devices - IoT and Non-IoT - by examining the average number of flow classes observed over a set of samples. Our experiments show that we can infer the correct class of unseen devices with an over 82% average F1 score and 70% accuracy.
H. T. Tsang - One of the best experts on this subject based on the ideXlab platform.
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Optical Packet labeling based on simultaneous polarization shift keying and amplitude shift keying.
Optics letters, 2004Co-Authors: Chi-wai Chow, C. S. Wong, H. T. TsangAbstract:We propose and demonstrate label encoding-swapping and transmission for an orthogonally Labeled Packet by use of a 10-Gbit/s amplitude shift keying payload and a 2.5-Gbit/s polarization shift keying (PolSK) label. A simple scheme for demodulating and demultiplexing the PolSK label based on an injection-locked Fabry-Perot laser diode is described. The extinction ratio of the newly added PolSK label can be precisely controlled and maintained after the intermediate node.
Jorge Ortiz - One of the best experts on this subject based on the ideXlab platform.
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IoTDI - DeviceMien: network device behavior modeling for identifying unknown IoT devices
Proceedings of the International Conference on Internet of Things Design and Implementation, 2019Co-Authors: Jorge Ortiz, Catherine Helen Crawford, Franck LeAbstract:With the explosion of IoT device use, networks are becoming more vulnerable to attack. Network administrators need better tools to verify and discover these devices in order to minimize attack risk. Existing tools provide rule-based assessment capabilities that cannot keep pace with the proliferation of devices. Current techniques demonstrate that given a rich set of Labeled Packet traces, one could design a pipeline that identifies all the devices in that trace with over 99% accuracy [30, 32]. However, it has also been observed [25], that such techniques are brittle when no labels are available. More perniciously, they provide false confidence scores about the label they do ascribe to a sample. This paper introduces a probabilistic framework for providing meaningful feedback in device identification, particularly when the device has not been previously observed. In our work, we use stacked autoencoders for automatically learning features from device traffic, learn the classes of traffic observed, and probabilistically model each device as a distribution of traffic classes. Our experiments show that we are able to identify previously seen devices after only 18.9 TCP-flow samples with 100% accuracy for devices where at least 50 samples are observed. We also show that we can distinguish between two broad classes of devices - IoT and Non-IoT - by examining the average number of flow classes observed over a set of samples. Our experiments show that we can infer the correct class of unseen devices with an over 82% average F1 score and 70% accuracy.
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devicemien network device behavior modeling for identifying unknown iot devices
The Internet of Things, 2019Co-Authors: Jorge Ortiz, Catherine Helen CrawfordAbstract:With the explosion of IoT device use, networks are becoming more vulnerable to attack. Network administrators need better tools to verify and discover these devices in order to minimize attack risk. Existing tools provide rule-based assessment capabilities that cannot keep pace with the proliferation of devices. Current techniques demonstrate that given a rich set of Labeled Packet traces, one could design a pipeline that identifies all the devices in that trace with over 99% accuracy [30, 32]. However, it has also been observed [25], that such techniques are brittle when no labels are available. More perniciously, they provide false confidence scores about the label they do ascribe to a sample. This paper introduces a probabilistic framework for providing meaningful feedback in device identification, particularly when the device has not been previously observed. In our work, we use stacked autoencoders for automatically learning features from device traffic, learn the classes of traffic observed, and probabilistically model each device as a distribution of traffic classes. Our experiments show that we are able to identify previously seen devices after only 18.9 TCP-flow samples with 100% accuracy for devices where at least 50 samples are observed. We also show that we can distinguish between two broad classes of devices - IoT and Non-IoT - by examining the average number of flow classes observed over a set of samples. Our experiments show that we can infer the correct class of unseen devices with an over 82% average F1 score and 70% accuracy.
Jianbin Xue - One of the best experts on this subject based on the ideXlab platform.
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BIC-TA - The Binary Anti-Collision Algorithm Based on Labeled Packet
Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) 2013, 2013Co-Authors: Jianbin Xue, Lijing Qin, Wenhua WangAbstract:An improved algorithm based on label Packet (PABI) is put forward, which aimed at much times search and the large amount of communication data of current binary anti-collision algorithm. The new algorithm improve decoding accuracy by grouping labels within the field that reader identify by setting a counter on the label, extract conflicts bit then make up conflict block in the recognition process, and introduce the concept of matrix to express the possible decode of conflict block, thereby. The simulation results show that the algorithm have a significant advantage in the number of searches and the amount of communication data in the case of a large number of tags.
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the binary anti collision algorithm based on Labeled Packet
BIC-TA, 2013Co-Authors: Jianbin Xue, Lijing Qin, Wenhua WangAbstract:An improved algorithm based on label Packet (PABI) is put forward, which aimed at much times search and the large amount of communication data of current binary anti-collision algorithm. The new algorithm improve decoding accuracy by grouping labels within the field that reader identify by setting a counter on the label, extract conflicts bit then make up conflict block in the recognition process, and introduce the concept of matrix to express the possible decode of conflict block, thereby. The simulation results show that the algorithm have a significant advantage in the number of searches and the amount of communication data in the case of a large number of tags.
