Computational intelligence techniques for automatic detection of Wi-Fi attacks in wireless IoT networks

These days, number of smart products based on Internet-of-Things (IoT) has been increased. These products are unified via various wireless technologies like, Bluetooth, Z-wave, Wi-Fi, Zigbee, etc. While the need on the wireless networks has improved, the assaults against them throughout the time hav...

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Vydáno v:Wireless networks Ročník 27; číslo 4; s. 2761 - 2784
Hlavní autoři: Nivaashini, M., Thangaraj, P.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.05.2021
Springer Nature B.V
Témata:
Algorithms
Artificial intelligence
Assaults
Bluetooth
Communications Engineering
Computer Communication Networks
Electrical Engineering
Engineering
Evaluation
Internet of Things
Intrusion detection systems
IT in Business
Machine learning
Networks
Time measurement
Wireless networks
Attack classification
Wireless attacks
Intrusion detection system
Attribute reduction
Machine learning
ISSN:1022-0038, 1572-8196
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Shrnutí:These days, number of smart products based on Internet-of-Things (IoT) has been increased. These products are unified via various wireless technologies like, Bluetooth, Z-wave, Wi-Fi, Zigbee, etc. While the need on the wireless networks has improved, the assaults against them throughout the time have expanded on top. In order to identify these assaults, an intrusion detection system (IDS) with a prominent precision and low identification time is required. In this work, a machine learning (ML) based wireless intrusion detection system (WIDS) for wireless networks to effectively identify assaults against them has been proposed. A ML prototype has been implemented to categorize the wireless network records into ordinary or one of the particular assault categories. The operation of an IDS is extensively enhanced when the attributes are more discriminative and delegate. Different attribute selection methods have been investigated to identify the best set of attributes for the WIDS. The proposed model is evaluated on aegean wireless intrusion dataset using various parameters like attack detection rate, detection time, precision, F-measure, etc. The experimental evaluation is carried out in the tools like, Weka, Rstudio and Anaconda Navigator Python. Finally, the experimental result shows the best performing ML algorithm with best set of reduced attributes.
Bibliografie:ObjectType-Article-1
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ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-021-02594-2