An Intelligent Intrusion Detection Scheme Powered by Boosting Algorithm

Divakar, Subham; Priyadarshini, Rojalina; Barik, Rabindra K.; Roy, Diptendu Sinha

doi:10.1109/confluence51648.2021.9377076

Cited by 1 publication

(3 citation statements)

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“…The combination of these three words creates the main guidelines for protecting and securing information. Cloud/ Cluster/ Big data [100], [101], [117], [123], [134], [143], [164], [171], [197] 3 Industry [130], [52], [148], [171], [206], [ IoT devices in general Hardware [94], [95], [112], [114], [163], [168], [179], [180] Software [97], [98], [113], [122], [141], [152], [155], [157], [160], [181], [193]…”

Section: Ciamentioning

confidence: 99%

“…[176], [230] [77] UNSW-NB15 Intrusion Detection 175,341 training 82,332 testing 49 [125], [132], [143], [154], [157], [161], [162], [171], [172], [202], [205], [225], [234] [78] CICIDS2017 Intrusion Detection 30,540 80 [110], [115], [117], [122], [127], [153] [159], [166], [186] [79] N-BaIoT Botnet Detection 7062606 115 [126], [152], [177], [179], [183],…”

Section: Machine Learning and Deep Learningmentioning

confidence: 99%

“…The Confusion matrix, which is also called the error matrix, is the matrix which is the formal way to represent the results of the classification model. The confusion matrix presented in Table 9 shows 143,145,151,152,155,164,170,174,175,183,184,185,186,194,197,199,202 -Easy to implement.…”

Section: Knnmentioning

confidence: 99%

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A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

Harahsheh,

Chen

2023

IJCAI

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The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration.Povzetek: Podan je pregled uporabe strojnega in globokega učenja v IoT napravah ter izzivov, rešitev in prihodnjih smeri raziskav.Informatica 47 (2023) 1-54 k. Harahsheh et al.

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