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

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31#
發(fā)表于 2025-3-27 00:44:16 | 只看該作者
32#
發(fā)表于 2025-3-27 04:49:18 | 只看該作者
33#
發(fā)表于 2025-3-27 05:16:44 | 只看該作者
Vulnerability Detection with Representation Learningtion. However, existing methods usually ignore the feature representation of vulnerable datasets, resulting in unsatisfactory model performance. Such vulnerability detection techniques should achieve high accuracy, relatively high true-positive rate, and low false-negative rate. At the same time, it
34#
發(fā)表于 2025-3-27 11:02:57 | 只看該作者
35#
發(fā)表于 2025-3-27 15:24:03 | 只看該作者
Malware Traffic Classification Based on GAN and BP Neural Networkstworks for malware traffic classification, which is to identify malware traffic, normal traffic, and traffic types. The model is composed of generative adversarial network and back propagation neural networks. The generator of the generative adversarial network is responsible for inputting random no
36#
發(fā)表于 2025-3-27 21:05:17 | 只看該作者
37#
發(fā)表于 2025-3-28 01:29:03 | 只看該作者
Detecting Unknown Vulnerabilities in?Smart Contracts with?Binary Classification Model Using Machine contracts are inevitably written with some vulnerabilities, which makes them vulnerable to attacks that cause property damage, and existing detection techniques and static analysis methods mainly target known vulnerability detection. We design a machine learning-based unknown vulnerability detectio
38#
發(fā)表于 2025-3-28 03:54:50 | 只看該作者
39#
發(fā)表于 2025-3-28 09:15:39 | 只看該作者
An Aspect-Based Semi-supervised Generative Model for?Online Review Spam Detection is gradually changed by the network. More and more people consume food, clothing, housing and transportation through the Internet, and the online reviews left by people have become valuable information resources. However, the authenticity of online reviews is worrying. The proliferation of review s
40#
發(fā)表于 2025-3-28 13:28:35 | 只看該作者
Hierarchical Policies of?Subgoals for?Safe Deep Reinforcement Learnings well known that an agent based on deep reinforcement learning in complex environments is difficult to train. Moreover, the agent will generate unsafe and strange actions due to the lack of sufficient reward feedback from the environment. To make the agent converge to a better policy and make its b
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