Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Sayed, Nasir; Shoaib, Muhammad; Ahmed, Waqas; Qasem, Sultan Noman; Albarrak, Abdullah M.; Saeed, Faisal

doi:10.32604/cmc.2023.030831

Cited by 4 publications

(2 citation statements)

References 59 publications

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“…In Europe and North America, cardiovascular disease is the leading cause of mortality (10), emphasizing the importance of incorporating developing risk variables to enhance risk prediction, enable early diagnosis, and customize care. The ability of machine learning like CNN (11) and LSTM is used to achieve patterns that can be used to inform healthcare decisions (12). By adjusting various tuning settings and combining k-fold crossvalidation, we demonstrate the juxtaposition of algorithm CNN and LSTM in this study.…”

Section: Introductionmentioning

confidence: 88%

Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data

Shoaib,

Junaid,

Husnain

et al. 2024

Front. Cardiovasc. Med.

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The 2017 World Health Organization Fact Sheet highlights that coronary artery disease is the leading cause of death globally, responsible for approximately 30% of all deaths. In this context, machine learning (ML) technology is crucial in identifying coronary artery disease, thereby saving lives. ML algorithms can potentially analyze complex patterns and correlations within medical data, enabling early detection and accurate diagnosis of CAD. By leveraging ML technology, healthcare professionals can make informed decisions and implement timely interventions, ultimately leading to improved outcomes and potentially reducing the mortality rate associated with coronary artery disease. Machine learning algorithms create non-invasive, quick, accurate, and economical diagnoses. As a result, machine learning algorithms can be employed to supplement existing approaches or as a forerunner to them. This study shows how to use the CNN classifier and RNN based on the LSTM classifier in deep learning to attain targeted “risk” CAD categorization utilizing an evolving set of 450 cytokine biomarkers that could be used as suggestive solid predictive variables for treatment. The two used classifiers are based on these “45” different cytokine prediction characteristics. The best Area Under the Receiver Operating Characteristic curve (AUROC) score achieved is (0.98) for a confidence interval (CI) of 95; the classifier RNN-LSTM used “450” cytokine biomarkers had a great (AUROC) score of 0.99 with a confidence interval of 0.95 the percentage 95, the CNN model containing cytokines received the second best AUROC score (0.92). The RNN-LSTM classifier considerably beats the CNN classifier regarding AUROC scores, as evidenced by a p-value smaller than 7.48 obtained via an independent t-test. As large-scale initiatives to achieve early, rapid, reliable, inexpensive, and accessible individual identification of CAD risk gain traction, robust machine learning algorithms can now augment older methods such as angiography. Incorporating 65 new sensitive cytokine biomarkers can increase early detection even more. Investigating the novel involvement of cytokines in CAD could lead to better risk detection, disease mechanism discovery, and new therapy options.

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Section: Introductionmentioning

confidence: 88%

Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data

Shoaib,

Junaid,

Husnain

et al. 2024

Front. Cardiovasc. Med.

Self Cite

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“…In addition, plant pathologists and cultivators have a high demand for the development of user-friendly tools and platforms that facilitate efficient data collection and evaluation from plant omics. Utilizing recent advances in cloud computing, the Internet of Things (IoT) ( Sayed et al., 2023 ), and mobile technology can facilitate real-time monitoring and decision-making. Methods based on deep learning and utilizing plant omics data have substantial potential for improving plant disease control and advancing global food security.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection

Shoaib,

Shah,

Sayed

et al. 2023

Front. Plant Sci.

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Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model’s resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.

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LMCA: a lightweight anomaly network traffic detection model integrating adjusted mobilenet and coordinate attention mechanism for IoT

Han,

Zhou,

Weng

et al. 2023

Telecommun Syst

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Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Cited by 4 publications

References 59 publications

Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data

Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection

LMCA: a lightweight anomaly network traffic detection model integrating adjusted mobilenet and coordinate attention mechanism for IoT

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