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Zero Trust: Deep Learning and NLP for HTTP Anomaly Detection in IDS

IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2215-2229页

作者： Nguyen, Manh Tien Anh Tong, Van Souihi, Sondes Bannour Souihi, Sami Vingroup Big Data Institute Viet Nam Hanoi University of Science and Technology School of Information and Communication Technology Viet Nam CEA List Université Paris-Saclay PalaiseauF-91120 France LISSI-TincNET Research Team University Paris-Est Creteil France

Web applications have become integral to daily life due to the migration of applications and data to cloud-based platforms, increasing their vulnerability to attacks. This paper addresses the need for robust intrusion detection systems by proposing a system grounded in Zero Trust architecture, which mandates continuous monitoring and multi-layered defenses. The Zero Trust principles ensure ongoing threat assessment and comprehensive protection against various attack vectors. Building on these foundational Zero Trust principles, our study introduces a system designed to not only distinguish normal HTTP requests from well-known attack patterns but also detect emerging types of anomalous attacks. Our system consists of two models that integrate Natural Language Processing approaches, Deep Learning techniques, and Transfer Learning strategies. The first model is employed to detect new anomalous HTTP requests that differ from normal requests. HTTP requests identified as anomalous are transmitted to the second model in charge of classifying specific categories of both well-known and novel attacks. Experiments show that our end-to-end system achieves the average F1-score of 89% on the combination of the CAPEC dataset and the zero-shot CSIC dataset. The proposed system proves also to be able to identify anomalous requests with a minimal latency of 4.8 milliseconds in production settings. © 1983-2012 IEEE.

关键词： Zero-shot learning

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A Hybrid Model Using Coordinate Attention Mechanisms for Stock Price Prediction 24

A Hybrid Model Using Coordinate Attention Mechanisms for Sto...

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2024 International Conference on Digital Economy, Blockchain and Artificial Intelligence, DEBAI 2024

作者： Sun, Ying Zhang, Bowen Peng, Cheng The Institute for Sustainable Development Macau University of Science and Technology China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Computer Science University of Electronic Science and Technology of China Zhongshan China

ISBN: (纸本)9798400710261

Predicting stock prices is a complex and challenging task in financial markets due to the intricate and non-linear nature of stock price movements. This study explores the application of Coordinate Attention mechanisms within advanced Temporal Convolutional Network (TCN) frameworks for stock price prediction based solely on historical stock price data. We present a hybrid model that combines the advantages of modern TCNs with Coordinate Attention mechanisms to improve the precision and robustness of stock prediction models. Extensive experiments are conducted on the well-known TSLA stock dataset. The results show varying levels of alignment with actual stock prices, highlighting the strengths and weaknesses of each model. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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A Semantic-Driven Generative Information Extraction Framework Oriented to Patent Text 21

A Semantic-Driven Generative Information Extraction Framewor...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhao, Yushan Wang, Tengke Zhang, Shunxiang Anhui University of Science and Technology School of Mathematics and Big Data Huainan China Anhui University of Science and Technology School of Computer Science and Engineering Huainan China

ISBN: (纸本)9798350329223

Generative information extraction for patent text relies on a text-to-structure encoder-decoder framework, facilitating the automatic construction of patent knowledge bases. Existing models challenge to capture the long-range semantic dependencies due to the decentralized semantic context within the patent text, diminishing the accuracy of information extraction. In this work, we present a Semantic-driven Generative Information Extraction (SGIE) framework oriented to patent texts by enhancing and associating semantic context, leading to an improved information extraction performance. Information networks obtained from the pre-trained language model are used as information representations in the encoder layer, and based on an AMR (Abstract Meaning Representation) parser combined with patent schema knowledge, the patent-AMR graphs are created and viewed as semantic representations. The semantic-enhanced information representations are acquired by alignment from the same level of semantic and information representations, so then the global information representation is gained by gated fusing all semantic-enhanced information representations. A patent scheme knowledge-constrained decoder is employed beam search to generate information based on the semantic order decided, so patent technical information is extracted. Experimental results show that the proposed model is highly accurate and efficient in patent texts and is promising. © 2023 IEEE.

关键词： Semantics

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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A Learnable Graph Convolutional Neural Network Model for Relation Extraction 1

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28th China Conference on Information Retrieval, CCIR 2022

作者： Xu, Jinling Chen, Yanping Qin, Yongbin Huang, Ruizhang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (数字)9783031247552

ISBN: (纸本)9783031247545

Relation extraction is the task of extracting the semantic relationships between two named entities in a sentence. The task relies on semantic dependencies relevant to named entities. Recently, graph convolutional neural networks have shown great potential in supporting this task, wherein dependency trees are usually adopted to learn semantic dependencies between entities. However, the requirement of external toolkits to parse sentences poses a problem, owing to them being error prone. Furthermore, entity relations and parsing structures vary in semantic expressions. Therefore, manually designed rules are required to prune the structure of the dependency trees. This study proposed a novel learnable graph convolutional neural network model (L-GCN) that directly encodes every word of a sentence as nodes of a graph neural network. Then, the L-GCN uses a learnable adjacency matrix to encode dependencies between nodes. The model offers the advantage of automatically learning high-order abstract representations of the semantic dependencies between words. Moreover, a fusion module was designed to aggregate the global and local semantic structure information of sentences. Further, the proposed L-GCN was evaluated on the ACE 2005 English dataset and Chinese Literature Text Corpus. The experimental results confirmed the effectiveness of L-GCN in learning the semantic dependencies of a relation instance. Moreover, it clearly outperformed previous dependency-tree-based models. © 2023, Springer Nature Switzerland AG.

关键词： Neural network models

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An electron-transfer-tuning strategy at the graphene/metal interface for improving acidic water electrolysis in proton exchange membrane electrolyzers

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Journal of Energy Chemistry 2025年第4期103卷 344-352页

作者： Yue Xu Shuaidong Li Yingjian He Fumiya Shiokawa Samuel Jeong Aimi Asilah Haji Tajuddin Zeyu Xi Yoshikazu Ito Jingzi Zhang Zeyun Cai Xi Lin Kailong Hu School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Special Environments Physical Sciences Harbin Institute of TechnologyShenzhen 518055GuangdongChina School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Applied Physics Graduate School of Pure and Applied SciencesUniversity of TsukubaTsukuba 305-8573Japan

Graphene encapsulation has been shown to be an effective technique for improving the corrosion resistance of non-noble metal catalysts for the acidic water *** key challenge lies in enhancing the electrocatalytic activity of graphene-encapsulated metals while maintaining their durability in acidic ***,an electron-transfer-tuning strategy is investigated at the graphene/NiMo interface,aiming to improve the hydrogen evolution reaction(HER)performance of graphene-encapsulated NiMo *** doping of Ti,a low electronegativity element,into NiMo substrate was confirmed to increase electron transfer from the metal core toward the *** electron-rich state on graphene facilitates the adsorption of positively charged protons on graphene,thereby enabling a Pt/C-comparable performance in 0.5 M H_(2)SO_(4),with only a 3.8%degradation in performance over a 120-h continuous *** proton exchange membrane(PEM)water electrolyzer assembled by the N-doped grapheneencapsulated Ti-doped NiMo exhibits a smaller cell voltage to achieve a current density of 2.0 A cm^(-2),in comparison to the Pt/C based *** study proposes a novel electron-transfer-tuning strategy to improve the HER activity of graphene-encapsulated non-noble metal catalysts without sacrificing durability in acidic electrolytes.

关键词： Hydrogen evolution Acidic media Graphene encapsulation Interface Proton exchange membrane

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Tree Regularization for Visual Explanations of Cancer Cell Classification

Tree Regularization for Visual Explanations of Cancer Cell C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Wang, Li Nawaz, Adil Leung, Victor C.M. Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China

ISBN: (纸本)9798350386226

The challenge of interpretability remains a significant barrier to adopting deep neural networks in healthcare domains. Although tree regularization aims to align a deep neural network's decisions with a single axis-aligned decision tree, however, relying on one tree for all inputs often leads to sub-optimal performance and interoperability. To address this limitation, we propose an enhanced tree regularization method that integrates a post-hoc visual explainable model such as Grad-CAM. This approach guides the deep model to be well-approximated by decision trees tailored to the salient regions identified by Grad-CAM in the input space. We rigorously validate the effectiveness of this framework on two cancer cell datasets: CNMC, which focuses on acute lymphoblastic leukemia cells, and ISBI2016, which comprises benign and malignant skin lesions. The results demonstrate that the proposed method delivers simpler and more interpretable explanations without compromising accuracy, thereby advancing the interpretability of deep learning models in critical healthcare applications. © 2024 IEEE.

关键词： Cancer cells

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An Enhanced-Precision Bandgap Reference Incorporating Temperature-Compensation 3

An Enhanced-Precision Bandgap Reference Incorporating Temper...

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3rd International Symposium on Semiconductor and Electronic technology, ISSET 2024

作者： Xiang, Tianzi Xu, Quanyuan Liu, Haorui Lu, Ying Southwest Forestry University College of Big Data and Intelligent Engineering Yunnan Kunming China Southwest Forestry University Key Laboratory of Forestry and Ecological Big Data State Forestry Administration Yunnan Kunming China Changchun University of Science and Technology School of Physics Jilin Changchun China

ISBN: (纸本)9798350390285

In this work, an Enhanced-Precision Bandgap Reference Incorporating Temperature-Compensation was proposed. Unlike traditional bandgap references, the reference voltage output of 0.701 V is realized by combining reference current and resistor. Meanwhile, an innovative Linear-piecewise current compensation was proposed, which aimed at optimizing the temperature drift of the bandgap reference. The simulation results shows that the proposed curvature-compensation technique improves the TC from 24.96 to 2.14 ppm°C in the temperature range of -40 °C to 120 °C;the PSRR of Enhanced-Precision Bandgap Reference at low frequency is 72.40 dB;the line regulation is about 0.08 %/V for input voltages between 3.2 and 3.7 V in standard 0.18 μm 1P6M CMOS process. The proposal of this new circuit provides a new idea for the realization of high- performance integrated circuits in the future. © 2024 IEEE.

关键词： Piecewise linear techniques

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Backdoor Attack Based on Privacy Inference against Federated Learning 7

Backdoor Attack Based on Privacy Inference against Federated...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Wu, Duo Hao, Lingguang Wei, Bing Hao, Kuangrong Han, Tao He, Langping Donghua University College of Information Science and Technology Shanghai China China Telecom Research Institute College of Big Data and Artificial Intelligence Shanghai China

ISBN: (纸本)9798350363173

As a pioneering distributed learning framework, federated learning (FL) has gained widespread adoption. It operates collaboratively among participants, with communication limited to sharing model parameters between the server and participants. However, FL is also more susceptible to active attacks from malicious insiders. Poisoned updates submitted by attackers can degrade the performance of the global model. Previous research only considered using the naive data of malicious clients for backdoor poisoning, and therefore achieved limited backdoor attack success rates. In this paper, we propose a novel Federated Backdoor Attack based on Privacy Inference (FBA-PI). Combining privacy inference based on generative adversarial networks (GAN), the attacker first infers sensitive information from victim participants and then injects backdoor triggers into the naive and generated data. Finally, malicious clients can embed backdoor semantics into the global model by participating in regular federated aggregation. Extensive experiments on the MNIST dataset verify the effectiveness of our proposed method. © 2024 IEEE.

关键词： Generative adversarial networks

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Energy Management Strategy for Fuel Cell Hybrid Power System Considering Fuel Cell Recoverable Performance Loss 5th

Energy Management Strategy for Fuel Cell Hybrid Power System...

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5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022

作者： He, Kai Liu, Zhongyong Zhang, Heng Mao, Lei University of Science and Technology of China Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China

ISBN: (纸本)9789819910267

In the development of fuel cell hybrid vehicles, energy management strategy (EMS) plays an important role. The traditional EMS studies focus on the optimization of energy distribution in order to minimize the hydrogen consumption and degradation cost of fuel cell. However, the phenomenon of fuel cell recoverable performance loss which can affect the efficiency of fuel cell is ignored. Therefore, it is necessary to consider recoverable performance loss in the EMS of fuel cell hybrid power system, which can better simulate practical fuel cell hybrid vehicle scenarios. Although recoverable performance loss can be reversed by performing recovery procedures, fuel cell operation is interrupted during the procedures, thus it is worth studying appropriate time to perform recovery procedures without affecting total power output. To solve these problems, this paper proposed an EMS framework by considering fuel cell recoverable performance. In the study, fuel cell recoverable performance loss is converted to equivalent hydrogen consumption, then an advanced deep reinforcement learning method, Deep Deterministic Policy Gradient (DDPG), is selected to obtain EMS and determine the optimal time for conducting recovery procedure. Results show that with the proposed method, the total cost can be reduced by about 10% compared to EMS without considering fuel cell recoverable performance. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Fuel cells

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