Improved bacterial foraging optimization with deep learning based anomaly detection in smart cities

Khayyat, Manal M.

doi:10.1016/j.aej.2023.05.082

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…Khayyat [11] presented an Improved Bacterial Foraging Optimizer with optimal DL for AD (IBFO-ODLAD) from the IoT network. For the FS method, the IBFO-ODLAD approach develops the IBFO method to select optimum feature subsets.…”

Section: Related Workmentioning

confidence: 99%

Improved Radial Movement Optimization With Fuzzy Neural Network Enabled Anomaly Detection for IoT Assisted Smart Cities

Alrayes,

Mtouaa,

Aljameel

et al. 2023

IEEE Access

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Recently, an extensive implementation of the recent Internet of Things (IoT) model has resulted in the development of smart cities. The network traffic of smart cities using loT systems has developed rapidly and established novel cybersecurity problems later these loT devices are linked to sensors that are directly linked to huge cloud servers. Unfortunately, IoT systems and networks can be identified as extremely exposed to security attacks that aim at service accessibility and data integrity. Additionally, the heterogeneity of data gathered in distinct IoT devices, composed of the disturbances acquired in the IoT systems, renders the recognition of anomalous performance and threatened nodes very difficult related to typical Information Technology (IT) networks. Accordingly, there is a critical requirement for reliable and effectual anomaly detection (AD) for identifying malicious data to promise that it could not be utilized in IoT lead to decision support systems (DSS). This manuscript offers an Improved Radial Movement Optimization with Fuzzy Neural Network Enabled Anomaly Detection (IRMOFNN-AD) technique for IoT Assisted Smart Cities. The main purpose of the IRMOFNN-AD algorithm lies in the accurate and automated detection of the anomalies that exist in the IoT environment. For the feature selection process, the IRMOFNN-AD technique uses the IRMO system to elect an optimum set of features. Additionally, the IRMOFNN-AD algorithm applies the FNN model for the detection and classification of anomalies. Besides, the sine cosine algorithm (SCA) has been employed for the parameter tuning of the FNN algorithm. The simulation value of the IRMOFNN-AD system has been tested on benchmark IDS datasets. The extensive results illustrate the better detection outcomes of the IRMOFNN-AD system interms of different measures.

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Section: Related Workmentioning

confidence: 99%

Improved Radial Movement Optimization With Fuzzy Neural Network Enabled Anomaly Detection for IoT Assisted Smart Cities

Alrayes,

Mtouaa,

Aljameel

et al. 2023

IEEE Access

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Harnessing deep learning for faster water quality assessment: identifying bacterial contaminants in real time

Khokhar,

Shah,

Saleem

et al. 2024

Vis Comput

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Water is essential for human survival. Humans can live without food for a few days but without water, a person can barely survive for 3–5 days. Various parts of the world, particularly under-developed countries, have regions where clean water is scarce, and humans living in such conditions have no access to clean water. Our solution provides information on whether water is contaminated or not. Moreover, it overcomes the delay time in getting the result of water contamination using traditional methods of up to 5–6 hrs. Our proposed method detects the colonies of the bacteria that are taken from the water sample (after gram staining) and then classifies the type of bacteria to whom it belongs and how much quantity of each bacterium causes infection to the human body. Bacteria detection is performed by a novel deep learning-based model with user-specified parameters. To improve our ability to detect dangerous bacteria including E. coli, yeast, and particles, we perform tests using datasets from a variety of researchers. On the test benchmark, the fine-tuned proposed model achieves 84.56% accuracy and provides the level of contamination in water.

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DeepLG SecNet: utilizing deep LSTM and GRU with secure network for enhanced intrusion detection in IoT environments

Nanjappan,

Pradeep,

Natesan

et al. 2024

Cluster Comput

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Improved bacterial foraging optimization with deep learning based anomaly detection in smart cities

Cited by 5 publications

References 20 publications

Improved Radial Movement Optimization With Fuzzy Neural Network Enabled Anomaly Detection for IoT Assisted Smart Cities

Improved Radial Movement Optimization With Fuzzy Neural Network Enabled Anomaly Detection for IoT Assisted Smart Cities

Harnessing deep learning for faster water quality assessment: identifying bacterial contaminants in real time

DeepLG SecNet: utilizing deep LSTM and GRU with secure network for enhanced intrusion detection in IoT environments

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