Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection

Wireless Communications and Mobile Computing, Journal Year: 2021, Volume and Issue: 2021(1)

Published: Jan. 1, 2021

The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. IIoT has been used generate large quantities data from multiple sensors, the device encountered several issues. faced various forms cyberattacks jeopardize its capacity supply organizations with seamless operations. Such risks result in financial reputational damages for businesses, as well theft sensitive information. Hence, Network Intrusion Detection Systems (NIDSs) have developed fight protect systems, but collections information can be development an intelligent NIDS are difficult task; thus, there serious challenges detecting existing new attacks. Therefore, study provides deep learning‐based intrusion detection paradigm hybrid rule‐based feature selection train verify captured TCP/IP packets. training process was implemented using feedforward neural network model. proposed scheme tested utilizing two well‐known datasets, NSL‐KDD UNSW‐NB15. suggested method beats other relevant methods terms accuracy, rate, FPR by 99.0%, 1.0%, respectively, dataset, 98.9%, 99.9%, 1.1%, UNSW‐NB15 according results performance comparison. Finally, simulation experiments evaluation metrics revealed appropriate IIOT attack classification.

Language: Английский

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XGBoost for Imbalanced Multiclass Classification-Based Industrial Internet of Things Intrusion Detection Systems

Sustainability, Journal Year: 2022, Volume and Issue: 14(14), P. 8707 - 8707

Published: July 16, 2022

The Industrial Internet of Things (IIoT) has advanced digital technology and the fastest interconnection, which creates opportunities to substantially grow industrial businesses today. Although IIoT provides promising for growth, massive sensor IoT data collected are easily attacked by cyber criminals. Hence, requires different high security levels protect network. An Intrusion Detection System (IDS) is one crucial solutions, aims detect network’s abnormal behavior monitor safe network traffic avoid attacks. In particular, effectiveness Machine Learning (ML)-based IDS approach building a secure application attracting research community in both general specific However, most available datasets contain multiclass output with imbalanced distributions. This main reason reduction detection accuracy attacks ML-based model. proposes an applying eXtremely Gradient Boosting (XGBoost) model overcome this issue. Two modern were used assess our proposed method’s robustness, X-IIoTDS TON_IoT. XGBoost achieved excellent attack F1 scores 99.9% 99.87% on two datasets. result demonstrated that improved performance was superior existing frameworks.

Language: Английский

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Machine Learning and Prediction of Infectious Diseases: A Systematic Review

Machine Learning and Knowledge Extraction, Journal Year: 2023, Volume and Issue: 5(1), P. 175 - 198

Published: Feb. 1, 2023

The aim of the study is to show whether it possible predict infectious disease outbreaks early, by using machine learning. This was carried out following guidelines Cochrane Collaboration and meta-analysis observational studies in epidemiology preferred reporting items for systematic reviews meta-analyses. suitable bibliography on PubMed/Medline Scopus searched combining text, words, titles medical topics. At end search, this review contained 75 records. analyzed demonstrate that incidence trends some diseases; several techniques types learning, obtain accurate plausible results.

Language: Английский

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Medium-long-term prediction of water level based on an improved spatio-temporal attention mechanism for long short-term memory networks

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 618, P. 129163 - 129163

Published: Jan. 24, 2023

Language: Английский

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Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics

Pattern Recognition Letters, Journal Year: 2021, Volume and Issue: 151, P. 69 - 75

Published: Aug. 14, 2021

Language: Английский

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Optimized multimedia data through computationally intelligent algorithms

Multimedia Systems, Journal Year: 2022, Volume and Issue: 29(5), P. 2961 - 2977

Published: March 28, 2022

Language: Английский

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Internet of Medical Things (IoMT): Applications, Challenges, and Prospects in a Data-Driven Technology

Published: Jan. 1, 2022

Internet of Things technology (IoT) is a fast-growing area computing, and it applicable to almost all human endeavor. The introduction IoT into medicine brought about the Medical (IoMT) that has really redefined smart healthcare systems globally, though its apprehension security threats risk especially in field second none. Though very challenging provide secured expansion using sensor medical domain but impart IoMT-based system can never be denied was greatly deployed various countries accordant with available facilities curb spread Covid-19 pandemic. But because sensitivity data critical information systems, continues posing several perilous challenges these keep growing. Therefore, this chapter discussed inherent opportunities facing data-driven solutions for IoMT. This will broaden research reassure users IoMT delivery.

Language: Английский

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Deep learning for Covid-19 forecasting: State-of-the-art review.

Neurocomputing, Journal Year: 2022, Volume and Issue: 511, P. 142 - 154

Published: Sept. 8, 2022

Language: Английский

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Classifying and Localizing Abnormalities in Brain MRI Using Channel Attention Based Semi-Bayesian Ensemble Voting Mechanism and Convolutional Auto-Encoder

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 75528 - 75545

Published: Jan. 1, 2023

Brain tumors represent a severe and often life-threatening condition in adults, as the rapid multiplication of cancerous cells within tumor can critically impair patient's normal functioning. The clinical practice commonly utilizes imaging modalities such MRI, PET CT scans to assess brain tumor's size, type, location. purpose this research is create computer aided diagnosis (CAD) system that segment categorize automatically. designed work specifically with T1W-CE Magnetic Resonance Images (MRI) brain. classification task involves determining type present image, while segmentation separating region from surrounding healthy tissue. By automating these tasks, proposed aims increase accuracy effectiveness treatment planning for patients. multi-class (BCT) considered one most daunting problems medical imaging. This article proposes model named VS-BEAM be used efficiently decision-making. (Voting Based Semi-Supervised Bayesian Ensemble Attention Mechanism) has been examined classification. achieved highest level possible. achieves maximum sensitivity, specificity, diagnostic compared existing models using MRI images. A convolutional autoencoder utilized extracting obtained testing data 264 was 98.91%, indicating method effective context assist detecting larger or even smaller tumors.

Language: Английский

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Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

Electronics, Journal Year: 2021, Volume and Issue: 10(24), P. 3125 - 3125

Published: Dec. 16, 2021

The COVID-19 pandemic has frightened people worldwide, and coronavirus become the most commonly used phrase in recent years. Therefore, there is a need for systematic literature review (SLR) related to Big Data applications crisis. objective highlight technological advancements. Many studies emphasize area of Our study categorizes many manage control pandemic. There very limited SLR prospective with Data. picked five databases: Science direct, IEEE Xplore, Springer, ACM, MDPI. Before screening, following recommendation, Preferred Reporting Items Systematic Reviews Meta Analyses (PRISMA) were reported 893 from 2019, 2020 until September 2021. After 60 met inclusion criteria through data statistics, analysis was as search string. research’s findings successfully dealt healthcare risk diagnosis, estimation or prevention, decision making, drug problems. We believe that this will motivate research community perform expandable transparent against crisis COVID-19.

Language: Английский

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