Titlebook: Ubiquitous Security; Second International Guojun Wang,Kim-Kwang Raymond Choo,Ernesto Damiani Conference proceedings 2023 The Editor(s) (if

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https://doi.org/10.1007/978-981-99-0272-9artificial intelligence; computer security; authentification; computer crime; machine learning; computer

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Wanyi Gu,Guojun Wang,Peiqiang Li,Xubin Li,Guangxin Zhai,Xiangbin Li,Mingfei Chentrong-coupling problem. Namely it is the QED theory, where the correspondent problem of the “Landau pole” or the “Moscow zero” of the beta-function have led to the well known discussion about the common base of the quantum field theory.

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補(bǔ)助

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Detecting Unknown Vulnerabilities in?Smart Contracts with?Multi-Label Classification Model Using CNNiLSTM model. Our model determines whether a vulnerability is unknown by detecting the opcode sequence representing the entire execution process of a transaction. Experimental results with the opcode sequences of transactions show that the model can achieve 82.86% accuracy and 83.63% F1-score.

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Detecting Unknown Vulnerabilities in?Smart Contracts with?Binary Classification Model Using Machine inally, we validate the effectiveness of our scheme by three machine learning models, namely the K-Near Neighbor Algorithm (KNN), the Support Vector Machine (SVM), and the Logistic Regression (LR). The SVM model achieves an accuracy of 91.4% and F1-score of 75.3% for the detection of unknown vulnerabilities.

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Hierarchical Policies of?Subgoals for?Safe Deep Reinforcement Learningper, we combine the subgoal embedding method with REINFORCE algorithm and PPO(Proximal Policy Optimization) algorithm to test the method in the MiniGrid-DoorKey game environment of the gym platform. The experiments demonstrate the effectiveness of the subgoal embedding method.

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