The Experts below are selected from a list of 1153452 Experts worldwide ranked by ideXlab platform
Linyuan Yu - One of the best experts on this subject based on the ideXlab platform.
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poisoning machine Learning based wireless idss via stealing Learning Model
Wireless Algorithms Systems and Applications, 2018Co-Authors: Pan Li, Wentao Zhao, Linyuan YuAbstract:Recently, machine Learning-based wireless intrusion detection systems (IDSs) have been demonstrated to have high detection accuracy in malicious traffic detection. However, many researchers argue that a variety of attacks are significantly challenging the security of machine Learning techniques themselves. In this paper, we study two different types of security threats which can effectively degrade the performance of machine Learning based wireless IDSs. First, we propose an Adaptive SMOTE (A-SMOTE) algorithm which can adaptively generate new training data points based on few existing ones with labels. Then, we introduce a stealing Model attack by training a substitute Model using deep neural networks (DNNs) based on the augmented training data in order to imitate the machine Learning Model embedded in targeted systems. After that, we present a novel poisoning strategy to attack against the substitute machine Learning Model, resulting in a set of adversarial samples that can be used to degrade the performance of targeted systems. Experiments on three real data sets collected from wired and wireless networks have demonstrated that the proposed stealing Model and poisoning attacks can effectively degrade the performance of IDSs using different machine Learning algorithms.
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WASA - Poisoning Machine Learning Based Wireless IDSs via Stealing Learning Model
Wireless Algorithms Systems and Applications, 2018Co-Authors: Pan Li, Wentao Zhao, Linyuan YuAbstract:Recently, machine Learning-based wireless intrusion detection systems (IDSs) have been demonstrated to have high detection accuracy in malicious traffic detection. However, many researchers argue that a variety of attacks are significantly challenging the security of machine Learning techniques themselves. In this paper, we study two different types of security threats which can effectively degrade the performance of machine Learning based wireless IDSs. First, we propose an Adaptive SMOTE (A-SMOTE) algorithm which can adaptively generate new training data points based on few existing ones with labels. Then, we introduce a stealing Model attack by training a substitute Model using deep neural networks (DNNs) based on the augmented training data in order to imitate the machine Learning Model embedded in targeted systems. After that, we present a novel poisoning strategy to attack against the substitute machine Learning Model, resulting in a set of adversarial samples that can be used to degrade the performance of targeted systems. Experiments on three real data sets collected from wired and wireless networks have demonstrated that the proposed stealing Model and poisoning attacks can effectively degrade the performance of IDSs using different machine Learning algorithms.
Rizqi Akbarani - One of the best experts on this subject based on the ideXlab platform.
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How to Improve Speaking Skill using Treffinger Learning Model
Eralingua: Jurnal Pendidikan Bahasa Asing dan Sastra, 2019Co-Authors: Rizqi AkbaraniAbstract:This research analyzed the implementation of Treffinger Learning Model in teaching speaking skill. This research used n classroom action research by applying treffinger Learning Model in speaking class. In collecting the data, research used observation to observe the implementation of Treffinger Learning Model during teaching Learning process and used speaking test to find the improvement of students’ speaking skill during implementation of Treffinger Learning Model. The research finding showed that the implementation of Treffinger Learning Model can improve the students’ speaking skill. Thereore, it can be concluded that Treffinger Learning Model is acceptable to be used as teaching Learning Model in teaching speaking.
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Treffinger Learning Model in Teaching Speaking
Academic Journal Perspective : Education Language and Literature, 2019Co-Authors: Rizqi AkbaraniAbstract:This research aims to find out how was the implementation of Treffinger Learning Model in teaching speaking skill. This research was applied for the tenth grade students of SMK Negeri 2 Madiun that consist of 26 students as sample in conducting the research. In collecting the data, research used observation to observe the implementation of Treffinger Learning Model during teaching Learning process and used test to find the improvement of students’ speaking skill during implementation of Treffinger Learning Model. The research finding showed that the implementation of Treffinger Learning Model can improve the students’ speaking skill. It can be concluded that Treffinger Learning Model is acceptable to be used as teaching Learning Model in teaching speaking.
Yoshitaka Okano - One of the best experts on this subject based on the ideXlab platform.
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Predicting investment behavior: An augmented reinforcement Learning Model
Neurocomputing, 2009Co-Authors: Tetsuya Shimokawa, Kyoko Suzuki, Tadanobu Misawa, Yoshitaka OkanoAbstract:The goal of this paper is to augment the ordinal temporal-difference type (TD-type) reinforcement Learning Model in order to detect the most suitable Learning Model of the human decision-making process in financial investment tasks. The simplicity and robustness of the TD-type Learning Model is fascinating. However, the available evidence and our observation suggest the necessity of introducing the nonlinear effect in Learning and the possibility that additional factors might play important roles in the investment decision-making process. To extend the ordinal TD-type Learning Model, we adopt a three-layered perceptron as the basis function and the hierarchical Bayesian method to calibrate the parameter values. The result of the predictive test suggests that the augmented TD-type Learning Model constructed in this paper can evade the overfitting and can predict people's investment behavior well as compared to other familiar Learning Models.
Pan Li - One of the best experts on this subject based on the ideXlab platform.
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poisoning machine Learning based wireless idss via stealing Learning Model
Wireless Algorithms Systems and Applications, 2018Co-Authors: Pan Li, Wentao Zhao, Linyuan YuAbstract:Recently, machine Learning-based wireless intrusion detection systems (IDSs) have been demonstrated to have high detection accuracy in malicious traffic detection. However, many researchers argue that a variety of attacks are significantly challenging the security of machine Learning techniques themselves. In this paper, we study two different types of security threats which can effectively degrade the performance of machine Learning based wireless IDSs. First, we propose an Adaptive SMOTE (A-SMOTE) algorithm which can adaptively generate new training data points based on few existing ones with labels. Then, we introduce a stealing Model attack by training a substitute Model using deep neural networks (DNNs) based on the augmented training data in order to imitate the machine Learning Model embedded in targeted systems. After that, we present a novel poisoning strategy to attack against the substitute machine Learning Model, resulting in a set of adversarial samples that can be used to degrade the performance of targeted systems. Experiments on three real data sets collected from wired and wireless networks have demonstrated that the proposed stealing Model and poisoning attacks can effectively degrade the performance of IDSs using different machine Learning algorithms.
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WASA - Poisoning Machine Learning Based Wireless IDSs via Stealing Learning Model
Wireless Algorithms Systems and Applications, 2018Co-Authors: Pan Li, Wentao Zhao, Linyuan YuAbstract:Recently, machine Learning-based wireless intrusion detection systems (IDSs) have been demonstrated to have high detection accuracy in malicious traffic detection. However, many researchers argue that a variety of attacks are significantly challenging the security of machine Learning techniques themselves. In this paper, we study two different types of security threats which can effectively degrade the performance of machine Learning based wireless IDSs. First, we propose an Adaptive SMOTE (A-SMOTE) algorithm which can adaptively generate new training data points based on few existing ones with labels. Then, we introduce a stealing Model attack by training a substitute Model using deep neural networks (DNNs) based on the augmented training data in order to imitate the machine Learning Model embedded in targeted systems. After that, we present a novel poisoning strategy to attack against the substitute machine Learning Model, resulting in a set of adversarial samples that can be used to degrade the performance of targeted systems. Experiments on three real data sets collected from wired and wireless networks have demonstrated that the proposed stealing Model and poisoning attacks can effectively degrade the performance of IDSs using different machine Learning algorithms.
Tetsuya Shimokawa - One of the best experts on this subject based on the ideXlab platform.
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Predicting investment behavior: An augmented reinforcement Learning Model
Neurocomputing, 2009Co-Authors: Tetsuya Shimokawa, Kyoko Suzuki, Tadanobu Misawa, Yoshitaka OkanoAbstract:The goal of this paper is to augment the ordinal temporal-difference type (TD-type) reinforcement Learning Model in order to detect the most suitable Learning Model of the human decision-making process in financial investment tasks. The simplicity and robustness of the TD-type Learning Model is fascinating. However, the available evidence and our observation suggest the necessity of introducing the nonlinear effect in Learning and the possibility that additional factors might play important roles in the investment decision-making process. To extend the ordinal TD-type Learning Model, we adopt a three-layered perceptron as the basis function and the hierarchical Bayesian method to calibrate the parameter values. The result of the predictive test suggests that the augmented TD-type Learning Model constructed in this paper can evade the overfitting and can predict people's investment behavior well as compared to other familiar Learning Models.
