2022
DOI: 10.1007/s12652-022-04407-6
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Intrusion detection for the internet of things (IoT) based on the emperor penguin colony optimization algorithm

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Cited by 19 publications
(8 citation statements)
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“… It achieves a detection rate that is 1.5 times more accurate than other IDSs. [36] The data sent via IoT devices can be unrelated, duplicated, or erroneous, posing a challenge for performing required tasks. Therefore, filtering and selecting transmitted data are necessary to achieve the highest possible level of security and address specific problem characteristics.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“… It achieves a detection rate that is 1.5 times more accurate than other IDSs. [36] The data sent via IoT devices can be unrelated, duplicated, or erroneous, posing a challenge for performing required tasks. Therefore, filtering and selecting transmitted data are necessary to achieve the highest possible level of security and address specific problem characteristics.…”
Section: Related Workmentioning
confidence: 99%
“…A novel wrapper FS model that uses the Emperor Penguin Colony (EPC) method to explore the issue space and a K-nearest neighbor classifier was proposed to solve FS for IoT challenges. The proposed EPC model is a novel approach to intrusion detection for IoT systems that achieves high accuracy and efficiency in filtering and selecting transmitted data [36] . The study in [6] addressed the high-dimensional nature of networking data that exacerbated IDS.…”
Section: Related Workmentioning
confidence: 99%
“…Extensive experiments on the Bot-IoT dataset selected six features from 40 features, achieving an accuracy of 99.98% and an F1 score of 99.63%. Alweshah et al [18] proposed a novel wrapping feature selection algorithm that employed the emperor penguin colony (EPC) to explore the search space, selecting K-nearest neighbors (KNN) as the classifier. Experimental results on well-known IoT datasets showed improved accuracy and reduced feature size compared to methods such as the multiobjective particle swarm optimization (MOPSO).…”
Section: Literature Surveymentioning
confidence: 99%
“…They hybridized salp swarm algorithm and slime mold algorithm for feature selection of network traffic. Alweshah et al ( 2022 ) proposed an emperor penguin colony based feature selection approach for the IoT-based IDS. The classification of intrusive traffic is performed using k-nearest neighbor (KNN) classifier.…”
Section: Introductionmentioning
confidence: 99%