The Experts below are selected from a list of 3927 Experts worldwide ranked by ideXlab platform
Jonathan Pan - One of the best experts on this subject based on the ideXlab platform.
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iot network behavioral fingerprint inference with limited network traces for Cyber Investigation
International Conference on Artificial Intelligence, 2021Co-Authors: Jonathan PanAbstract:The development and adoption of Internet of Things (IoT) devices will grow significantly in the coming years to enable Industry 4.0. Many forms of IoT devices will be developed and used across industry verticals. However, the euphoria of this technology adoption is shadowed by the solemn presence of Cyber threats that will follow its growth trajectory. Cyber threats would either embed their malicious code or attack vulnerabilities in IoT that could induce significant consequences in Cyber and physical realms. In order to manage such destructive effects, incident responders and Cyber investigators require the capabilities to find these rogue IoT, contain them quickly and protect other legitimate IoTs from attacks. Such online devices may only leave network activity traces. A collection of relevant traces could be used to infer the IoT’s network behavioral fingerprints and in turn could facilitate investigative find of these IoT. However, the challenge is how to infer these fingerprints when there are limited network activity traces. This research proposes a novel model construct that learns to infer the network behavioral fingerprint of specific IoT based on limited network activity traces using a One-Class Time Series Meta-learner called DeepNetPrint. Our research demonstrated our model to perform comparative well to supervised machine learning model trained with lots of network activity traces to identify IoT devices.
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IoT Network Behavioral Fingerprint Inference with Limited Network Trace for Cyber Investigation: A Meta Learning Approach
arXiv: Cryptography and Security, 2020Co-Authors: Jonathan PanAbstract:The development and adoption of Internet of Things (IoT) devices will grow significantly in the coming years to enable Industry 4.0. Many forms of IoT devices will be developed and used across industry verticals. However, the euphoria of this technology adoption is shadowed by the solemn presence of Cyber threats that will follow its growth trajectory. Cyber threats would either embed their malicious code or attack vulnerabilities in IoT that could induce significant consequences in Cyber and physical realms. In order to manage such destructive effects, incident responders and Cyber investigators require the capabilities to find these rogue IoT and contain them quickly. Such online devices may only leave network activity traces. A collection of relevant traces could be used to infer the IoT's network behaviorial fingerprints and in turn could facilitate investigative find of these IoT. However, the challenge is how to infer these fingerprints when there is limited network activity traces. This research proposes the novel model construct that learns to infer the network behaviorial fingerprint of specific IoT based on limited network activity traces using a One-Card Time Series Meta-Learner called DeepNetPrint. Our research also demonstrates the application of DeepNetPrint to identify IoT devices that performs comparatively well against leading supervised learning models. Our solution would enable Cyber investigator to identify specific IoT of interest while overcoming the constraints of having only limited network traces of the IoT.
Ali Dehghantanha - One of the best experts on this subject based on the ideXlab platform.
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special issue on big data applications in Cyber security and threat intelligence part 2
IEEE Transactions on Big Data, 2019Co-Authors: Kimkwang Raymond Choo, Mauro Conti, Ali DehghantanhaAbstract:The papers in this special section focus on Big Data applications in Cybersecurity and threat intelligence. The last decade has witnessed a tremendous rapid increase in volume, veracity, velocity and variety of data (also commonly referred to as the four V’s of big data in the literature1) generated by different Cyber security solutions and as part of Cyber Investigation cases. When a significant amount of data is collected from or generated by different devices and sources, intelligent big-data analytical techniques are necessary to mine, interpret and visualize such data. To mitigate existing Cyber security threats, it is important for big-data analytical techniques to keep pace. Therefore, in special issue we focus on cutting-edge from both academia and industry, with a particular emphasis on novel techniques to mine, interpret and visualize big-data from a wide range of sources and can be applied in Cyber security, Cyber forensics and threat intelligence context.
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special issue on big data applications in Cyber security and threat intelligence part 1
IEEE Transactions on Big Data, 2019Co-Authors: Kimkwang Raymond Choo, Mauro Conti, Ali DehghantanhaAbstract:The papers in this special section examine Big Data applications in Cyber security and threat intelligence. This last decade has witnessed a tremendous rapid increase in volume, veracity, velocity and variety of data generated by different Cyber security solutions and as part of Cyber Investigation cases. When a significant amount of data is collected from or generated by different devices and sources, intelligent big-data analytical techniques are necessary to mine, interpret and visualize such data. To mitigate existing Cyber security threats, it is important for big-data analytical techniques to keep pace. Therefore, in special issue we focus on cutting-edge from both academia and industry, with a particular emphasis on novel techniques to mine, interpret and visualize big-data from a wide range of sources and can be applied in Cyber security, Cyber forensics and threat intelligence context.
Kimkwang Raymond Choo - One of the best experts on this subject based on the ideXlab platform.
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special issue on big data applications in Cyber security and threat intelligence part 2
IEEE Transactions on Big Data, 2019Co-Authors: Kimkwang Raymond Choo, Mauro Conti, Ali DehghantanhaAbstract:The papers in this special section focus on Big Data applications in Cybersecurity and threat intelligence. The last decade has witnessed a tremendous rapid increase in volume, veracity, velocity and variety of data (also commonly referred to as the four V’s of big data in the literature1) generated by different Cyber security solutions and as part of Cyber Investigation cases. When a significant amount of data is collected from or generated by different devices and sources, intelligent big-data analytical techniques are necessary to mine, interpret and visualize such data. To mitigate existing Cyber security threats, it is important for big-data analytical techniques to keep pace. Therefore, in special issue we focus on cutting-edge from both academia and industry, with a particular emphasis on novel techniques to mine, interpret and visualize big-data from a wide range of sources and can be applied in Cyber security, Cyber forensics and threat intelligence context.
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special issue on big data applications in Cyber security and threat intelligence part 1
IEEE Transactions on Big Data, 2019Co-Authors: Kimkwang Raymond Choo, Mauro Conti, Ali DehghantanhaAbstract:The papers in this special section examine Big Data applications in Cyber security and threat intelligence. This last decade has witnessed a tremendous rapid increase in volume, veracity, velocity and variety of data generated by different Cyber security solutions and as part of Cyber Investigation cases. When a significant amount of data is collected from or generated by different devices and sources, intelligent big-data analytical techniques are necessary to mine, interpret and visualize such data. To mitigate existing Cyber security threats, it is important for big-data analytical techniques to keep pace. Therefore, in special issue we focus on cutting-edge from both academia and industry, with a particular emphasis on novel techniques to mine, interpret and visualize big-data from a wide range of sources and can be applied in Cyber security, Cyber forensics and threat intelligence context.
Pan Jonathan - One of the best experts on this subject based on the ideXlab platform.
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IoT Network Behavioral Fingerprint Inference with Limited Network Trace for Cyber Investigation: A Meta Learning Approach
2020Co-Authors: Pan JonathanAbstract:The development and adoption of Internet of Things (IoT) devices will grow significantly in the coming years to enable Industry 4.0. Many forms of IoT devices will be developed and used across industry verticals. However, the euphoria of this technology adoption is shadowed by the solemn presence of Cyber threats that will follow its growth trajectory. Cyber threats would either embed their malicious code or attack vulnerabilities in IoT that could induce significant consequences in Cyber and physical realms. In order to manage such destructive effects, incident responders and Cyber investigators require the capabilities to find these rogue IoT and contain them quickly. Such online devices may only leave network activity traces. A collection of relevant traces could be used to infer the IoT's network behaviorial fingerprints and in turn could facilitate investigative find of these IoT. However, the challenge is how to infer these fingerprints when there is limited network activity traces. This research proposes the novel model construct that learns to infer the network behaviorial fingerprint of specific IoT based on limited network activity traces using a One-Card Time Series Meta-Learner called DeepNetPrint. Our research also demonstrates the application of DeepNetPrint to identify IoT devices that performs comparatively well against leading supervised learning models. Our solution would enable Cyber investigator to identify specific IoT of interest while overcoming the constraints of having only limited network traces of the IoT.Comment: 7 pages, 5 figure
Mauro Conti - One of the best experts on this subject based on the ideXlab platform.
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special issue on big data applications in Cyber security and threat intelligence part 2
IEEE Transactions on Big Data, 2019Co-Authors: Kimkwang Raymond Choo, Mauro Conti, Ali DehghantanhaAbstract:The papers in this special section focus on Big Data applications in Cybersecurity and threat intelligence. The last decade has witnessed a tremendous rapid increase in volume, veracity, velocity and variety of data (also commonly referred to as the four V’s of big data in the literature1) generated by different Cyber security solutions and as part of Cyber Investigation cases. When a significant amount of data is collected from or generated by different devices and sources, intelligent big-data analytical techniques are necessary to mine, interpret and visualize such data. To mitigate existing Cyber security threats, it is important for big-data analytical techniques to keep pace. Therefore, in special issue we focus on cutting-edge from both academia and industry, with a particular emphasis on novel techniques to mine, interpret and visualize big-data from a wide range of sources and can be applied in Cyber security, Cyber forensics and threat intelligence context.
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special issue on big data applications in Cyber security and threat intelligence part 1
IEEE Transactions on Big Data, 2019Co-Authors: Kimkwang Raymond Choo, Mauro Conti, Ali DehghantanhaAbstract:The papers in this special section examine Big Data applications in Cyber security and threat intelligence. This last decade has witnessed a tremendous rapid increase in volume, veracity, velocity and variety of data generated by different Cyber security solutions and as part of Cyber Investigation cases. When a significant amount of data is collected from or generated by different devices and sources, intelligent big-data analytical techniques are necessary to mine, interpret and visualize such data. To mitigate existing Cyber security threats, it is important for big-data analytical techniques to keep pace. Therefore, in special issue we focus on cutting-edge from both academia and industry, with a particular emphasis on novel techniques to mine, interpret and visualize big-data from a wide range of sources and can be applied in Cyber security, Cyber forensics and threat intelligence context.
