Abstract
Wireless networks were widely used, but they were actually a threat. Represents Recognized as a wireless AP (access point). In particular, unauthorized APs used by businesses, military installations, and government agencies can be exposed to hacking attacks. Therefore, to protect your information, it is significant to identify unauthorized APs. This paper addresses round-trip time (RTT) values as records to identify allowed and unallowed APs in a wireless integrated atmosphere. Machine learning techniques such as potential Dirichlet map**, k nearest neighbors, naive bays, support vector machines, bagging, adapter boosting, gradient boosting machines, random forests, additional trees, and gradient descent techniques are employed to resolve these issues. Gradient Boosting algorithm is used for protection and identification. This is developed and tested on data set. Experimental results show that it offers the highest accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jana S, Kasera SK (2010) On fast and accurate detection of unauthorized wireless access points using clock skews. IEEE Trans Mobile Comput 9(3):449–462
Han H (2011) A timing-based scheme for rogue AP detection. IEEE Trans Parallel Distrib Syst 22(11):1912–1925
Awad F, Al-Refai M, Al-Qerem A (2017) Rogue access point localization using particle swarm optimization. In: 8th international conference on information and communication systems (ICICS), Irbid, Jordan. https://doi.org/10.1109/IACS.2017.7921985
Han D, Andersen, DG, Kaminsky M, Papagiannaki K, Seshan S (2009) Access point localization using local signal strength gradient. Passive and Active Network Measurement, pp 99–108
Agarwal M, Biswas S, Nandi S (2018) An efficient scheme to detect evil twin rogue access point attack in 802.11 wi-fi networks. Int J Wirel Inf Netw 25(3):120–135
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Narasimham, C., Manasa, V., Karimisetty, S., Viswanadha Reddy, N., Maurya, S. (2023). Detection of Unauthorized Access Points Based on Machine Learning Techniques. In: Maurya, S., Peddoju, S.K., Ahmad, B., Chihi, I. (eds) Cyber Technologies and Emerging Sciences. Lecture Notes in Networks and Systems, vol 467. Springer, Singapore. https://doi.org/10.1007/978-981-19-2538-2_41
Download citation
DOI: https://doi.org/10.1007/978-981-19-2538-2_41
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2537-5
Online ISBN: 978-981-19-2538-2
eBook Packages: EngineeringEngineering (R0)