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***:Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

Computers, Materials & Continua 2021年第12期69卷 3945-3966页

作者： Ziadoon K.Maseer Robiah Yusof Salama A.Mostafa Nazrulazhar Bahaman Omar Musa Bander Ali Saleh Al-rimy Faculty of Information and Communication Technology Universiti Teknikal MalaysiaMelaka76100Malaysia Center of Intelligent and Autonomous Systems Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaJohor86400Malaysia Faculty of Business and Technology UNITAR International UniversitySelangor47301Malaysia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor81310Malaysia

With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access ***,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of ***,due to the high dynamics and imbalanced nature of the data,the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern ***,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of ***,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(***)model to detect existing and novel *** HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)*** CICIDS2017 dataset is selected to evaluate the *** model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced *** have compared the performance of the *** model with three recent *** results show the *** model outperforms the three other models by achieving a higher detection accuracy of 99.38%and 99.99%for web attack and bot attack scenarios,***,it can detect the occurrence of low-frequency attacks that are undetectable by other models.

关键词： Cyberattacks internet of things intrusion detection system deep learning neural network supervised and unsupervised deep learning

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An intelligent Resource Scheduling Method with Edge Channel Deployment for BPM 19

An Intelligent Resource Scheduling Method with Edge Channel ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Bowen Dou, Wanchun Zhou, Xiaokang Zhang, Xuyun Qi, Lianyong Dai, Fei Chen, Chaochao Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shiga University Faculty of Data Science Hikone Japan Macquarie University Department of Computing Sydney Australia College of Computer and Software Qingdao China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350346558

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users' status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method. © 2022 IEEE.

关键词： Cost effectiveness

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FastFace: Fast-converging Scheduler for Large-scale Face Recognition Training with One GPU

arXiv

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arXiv 2024年

作者： Gong, Xueyuan Liu, Zhiquan Si, Yain-Whar Yuan, Xiaochen Wang, Ke Liu, Xiaoxiang Lin, Cong Zhang, Xinyuan School of Intelligent Systems Science and Engineering Jinan University Zhuhai China College of Cyber Security Jinan University Guangzhou China Faculty of Science and Technology University of Macau China Faculty of Applied Sciences Macau Polytechnic University China College of Information Science and Technology Jinan University Guangzhou China

Computing power has evolved into a foundational and indispensable resource in the area of deep learning, particularly in tasks such as Face Recognition (FR) model training on large-scale datasets, where multiple GPUs are often a necessity. Recognizing this challenge, some FR methods have started exploring ways to compress the fully-connected layer in FR models. Unlike other approaches, our observations reveal that without prompt scheduling of the learning rate (LR) during FR model training, the loss curve tends to exhibit numerous stationary subsequences. To address this issue, we introduce a novel LR scheduler leveraging Exponential Moving Average (EMA) and Haar Convolutional Kernel (HCK) to eliminate stationary subsequences, resulting in a significant reduction in converging time. However, the proposed scheduler incurs a considerable computational overhead due to its time complexity. To overcome this limitation, we propose FastFace, a fast-converging scheduler with negligible time complexity, i.e. O(1) per iteration, during training. In practice, FastFace is able to accelerate FR model training to a quarter of its original time without sacrificing more than 1% accuracy, making large-scale FR training feasible even with just one single GPU in terms of both time and space complexity. Extensive experiments validate the efficiency and effectiveness of FastFace. The code is publicly available at: https://***/amoonfana/FastFace © 2024, CC BY.

关键词： Face recognition

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The Investigation and Prediction of Voriconazole-Associated Hepatotoxicity under Therapeutic Drug Monitoring*

The Investigation and Prediction of Voriconazole-Associated ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jing Ma Yu Wang Shuang Ma Jingsong Li Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Engineering Research Center of EMR and Intelligent Expert Systems Ministry of Education Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China

Voriconazole is commonly used as the first-line agent to treat invasive fungal infections (IFIs), but the induction of hepatotoxicity limits its use. To improve the treatment outcomes and minimize toxicity, doctors often administer Therapeutic Drug Monitoring (TDM) to patients receiving voriconazole treatment. Here, we conducted a real-world clinical investigation of voriconazole-treated patients and found significant differences between the TDM (n=318) and non-TDM cohort (n=6,379), and such incidence of hepatotoxicity showed 10.6% in the non-TDM cohort, compared with 21.5% in the TDM cohort. Based on our previous investigation, we presented and compared several machine learning models (including AdaBoost, decision tree, GBDT, logistic regression, neural networks, and random forest) for the early warning of voriconazole-associated hepatoxicity. Through the five-fold cross validation, the logistic model outperformed other models with a mean AUC of 0.7933±0.0934. Our findings offer important insights into the safe and effective application of voriconazole.

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Revolutionizing Oil Spill Detection: A Machine Learning Approach for Satellite Image Classification

Revolutionizing Oil Spill Detection: A Machine Learning Appr...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Sherif, Khaled Rizk, Faris H. Zaki, Ahmed Mohamed Eid, Marwa M. Khodadadi, Nima Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt University of Miami Department of Civil and Architectural Engineering Coral GablesFL United States Isa Town Bahrain Ain Shams University Faculty of Computer and Information Sciences Department of Computer Science Cairo11566 Egypt Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Jadara Research Center Jadara University Irbid Jordan

ISBN: (纸本)9798350351408

Identifying and labeling oil spills in satellite imagery is an essential activity of both environmental monitoring and disaster response actions. This work is dedicated to applying an Artificial Neural Network (ANN) model for gathering oil spill data by using a dataset that is specially curated for this reason. Our dataset was developed from satellite pictures of the ocean, some of which depict oil spills and some that do not. The features were extracted from each picture using computer vision algorithms. Our ANN model is trained to distinguish between two classes: The metrics that are looked at consist of accuracy, sensitivity, specificity, PPV, NPV, and statistical significance, and they illustrate how the model performs. As a result, the ANN model gets an accuracy of 96.88% and a sensitivity of 92.86% at the same time, while the specificity is 99.88%. The sensitivity of this diagnostic test is 96.30%, and the specificity is 94.74%. A p-value of 0.985997 means that the reported finding reaches a statistical significance, which is enough to support our hypothesis. This can be concluded from the results of ANN, showing the potential of this model to successfully classify the image patches into two sets, namely the ones covered by oil spills and the oil spill-free ones. The research work is a great contribution to the development of the area of environmental monitoring through the machine learning methods used for quick and appropriate detection of environmental hazards. © 2024 IEEE.

关键词： Environmental monitoring

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MP-GAN: Cyber-Attack Detection and Localization for Cyber-Physical systems with Multi-Process Generative Adversarial Networks*

MP-GAN: Cyber-Attack Detection and Localization for Cyber-Ph...

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Artificial Intelligence of Things and systems (AIoTSys), International Conference on

作者： Yikui Zhou Jie Wang Junnan Tang Chao Gou Zigui Jiang Dan Li See-Kiong Ng School of Software Engineering Sun-Yat Sen University Zhuhai China School of Intelligent Systems Engineering Sun-Yat Sen University Shenzhen China Institute of Data Science/School of Computing National University of Singapore

Cyber-Physical System (CPS) integrates sensing, computation, cybernetics, and networking to control a hybrid physical system consisting of different functional subsystems, making the production process more intelligent and controllable. However, cyber-attacks during its operation will lead to abnormal system behaviors or even system breakdowns. In recent years, data-driven anomaly detection methods have been adopted to judge whether the CPS system is under cyber-attacks based on rich sensor measurements to avoid further economic losses or safety issues. However, the multi-process essence of CPS has not been adequately addressed in existing works to locate the processes or points being attacked for in-time actions. In this work, we proposed a Multi-Process Generative Adversarial Network (MP-GAN) framework to detect anomalous CPS statuses and locate cyber-attacks. Specifically, the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) was adopted as the base model to capture the temporal correlation of time series distributions, and the data was transferred between latent space and data space in a bi-directional manner, which regulated the generator to generate more realistic samples and thus better grasping the underline principles of the system. Moreover, parallel generators were employed to capture system performances at different physical processes, thus localizing the attacked CPS processes. Experiments on three CPS datasets, two collected from the Secure Water Treatment (SWaT) system and one collected from the water Distribution (WADI) system, showed that the proposed MP-GAN framework effectively reported anomalies caused by various cyber-attacks inserted in these complex multiprocess CPSs, which outperforms the state-of-the-art methods and can also locate most of the detected irregularities at the corresponding stages where the attacks were inserted.

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Parameter Identification Method for Underwater Vehicle WPT System Based on Primary Side Detection

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IEEE Transactions on Transportation Electrification 2025年

作者： Bai, Longlei JuYe Ma, Zhiyong Zhang, Ziye Jiang, Chaoqiang JiangYou Luo, Bo Sun, Liangshun Yantai Research Institute and the College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China Electronic and information engineering with Harbin Engineering University Harbin China China Department of Electrical Engineering faculty member in the State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong Hong Kong Yantai Research Institute Harbin Engineering University China

In the realm of underwater wireless power transmission systems, it is crucial to acquire system parameters accurately to ensure output stability. However, measuring these parameters in underwater environments presents a huge challenge. As a result, simplifying measurements through parameter identification becomes essential. The circuit model of the underwater wireless power transfer (WPT) system may require adjustments due to seawater conductivity, and utilizing current parameter identification methods is applicable to air, which can lead to notable inaccuracies in underwater parameter identification. This paper presents an analytical model of the mutual inductance in the underwater WPT system, analyzes its impact on output power and transmission efficiency, and proposes a mutual inductance and load parameter identification method suitable for underwater WPT systems. This method identifies mutual inductance and load values by measuring the input impedance of the equivalent load network on the inverter side at both resonant and non-resonant frequencies. The parameter identification model of the proposed underwater WPT system has been theoretically derived and experimentally verified using the S-S compensation topology. The experimental results illustrate that the proposed parameter identification method effectively identifies both the mutual inductance model and the load of the underwater WPT system, with the load identification error being less than 2% and the mutual inductance identification error being less than 5%. Furthermore, mutual inductance and load information can be obtained from the primary side without communication, and the output voltage is controlled with an accuracy of less than 1.2% through parameter identification. © 2025 IEEE. All rights reserved.

关键词： Inductive power transmission

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Detection and mitigation of RFI in SBRS observation data

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Research in Astronomy and Astrophysics 2021年第8期21卷 111-123页

作者： Zhen-Ping Qiang Jun Cheng Zhen-Hong Shang Kai-Fan Ji Fei Dai Hui Liu College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunming 650224China CAS Key Laboratory of Solar Activity National Astronomical ObservatoriesBeijing 100101China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650504China Yunnan Observatories Chinese Academy of SciencesKunming 650216China

In view of the inconsistency of channel gains and a large amount of interference noise in Solar Broadband Radio Spectrometer(SBRS) observation data,they will seriously affect the analysis of SBRS *** this paper,a method of Radio Frequency Interference(RFI) detection and mitigation for SBRS observation data is ***,the SBRS observation data are preprocessed,a part of the observation data was selected to calculate the mean and variance to achieve the normalization of the entire observation data,which can avoid the influence of strong noise on the normalization ***,we proposed an adaptive threshold RFI detection method based on fusion wavelet transform reconstruction and an RFI elimination method based on neighborhood weighted *** is worth mentioning that to detect RFI interference signals of different magnitudes,we adopted an iterative approach to the RFI detection and mitigation *** qualitative analysis of real observation data and quantitative analysis of simulated data,it is shown that the method proposed in this paper can effectively eliminate RFI in SBRS observation data,and improve the quality of observation data for further scientific analysis.

关键词： spectrographs:SBRS techniques:image processing and spectroscopic Sun:radio radiation

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A Knowledge-based and data-driven Approach for Predicting Acute Kidney Injury in Patients with Heart Failure*

A Knowledge-based and Data-driven Approach for Predicting Ac...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shengqiang Chi Tianshu Zhou Weiwei Zhu Xueyao Li Jingsong Li Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Engineering Research Center of EMR and Intelligent Expert Systems Ministry of Education Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China

It has great potential to integrate medical knowledge and electronic health record data for diagnosis prediction. However, present studies only utilized information from knowledge graphs, omitting potentially significant global graph structural features. In this study, we proposed a knowledge and data integrating modeling approach to reconstruct patient electronic health record data with graph structure and use medical knowledge as internal information of patient data to build a risk prediction model for acute kidney injury in patients with heart failure based on graph neural networks. Experimental results based on the MIMIC III data showed that the method proposed was superior to other baseline models in predicting the risk of acute kidney injury in heart failure patients, with an accuracy of 0.725 and an F1 score of 0.755. This study provides a novel approach to the disease risk prediction models that integrates medical knowledge and data.

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Detection of Alzheimer's Disease progression from Structural MRIs using ConvMixer based Deep Learning Model

Detection of Alzheimer's Disease progression from Structural...

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3rd International Conference on Machine Learning and data engineering, ICMLDE 2024

作者： Lashkary, Mahak Bansal, Kanika Jain, Samyak Shukla, Praveen Kumar Tekchandani, Hitesh Dhaka, Vijaypal Singh School of Computing and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur303007 India School of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur303007 India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Telangana Hyderabad500075 India

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive functions and quality of life. Affecting over 50 million people worldwide, early and accurate detection of AD progression is crucial for timely intervention and management. This paper presents a reliable method for classifying Alzheimer's stages using Deep Learning based ConvMixer model on structural MRI data. The proposed deep learning based convmixer model significantly utilizes the patch based learning with depthwise separable convolution to achieve improved performance. The dataset used in this work has been extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and comprises of three classes: Alzheimer's Disease (AD), Cognitive Impairment (CI), and Cognitively Normal (CN). The proposed approach demonstrated exceptional performance, achieving an accuracy of 99.88%, specificity of 99.87%, sensitivity of 99.90 and precision of 99.95%. The achieved results demonstrate the proposed model's accuracy in distinguishing Alzheimer's stages, making it a promising tool for early diagnosis and disease progression monitoring. © 2025 The Author(s).

关键词： Deep learning

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