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Distributed Denial-of-Service Prediction on IoT Framework by Learning Techniques by Dwivedi, Shubhra ;Vardhan, Manu ;Tripathi, Sarsij is a Computer Science article available to read on EtoBox.

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Distributed denial-of-service (DDoS) attacks on the Internet of Things (IoT) pose a serious threat to several web-based networks. The intruder's ability to deal with the power of various cooperating devices to instigate an attack makes its administration even more multifaceted. This complexity can be further increased while lots of intruders attempt to overload an attack against a device. To counter and defend against modern DDoS attacks, several effective and powerful techniques have been used in the literature, such as data mining and artificial intelligence for the intrusion detection system (IDS), but they have some limitations. To overcome the existing limitations, in this study, we propose an intrusion detection mechanism that is an integration of a filter-based selection technique and a machine learning algorithm, called information gain-based intrusion detection system (IGIDS). In addition, IGIDS selects the most relevant features from the original IDS datasets that can help to distinguish typical low-speed DDoS attacks and, then, the selected features are passed on to the classifiers, i.e. support vector machine (SVM), decision tree (C4.5), naïve Bayes (NB) and multilayer

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Author

Dwivedi, Shubhra ;Vardhan, Manu ;Tripathi, Sarsij

Publisher

Walter de Gruyter GmbH

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)