Abstract
The regular utilization of wireless network as well as mobile hot spot gives a remote network condition, recognition as well as hazard through Wi-Fi; security is relentlessly expanding, whereas the user utilizing authorized and unauthorized APs (Access Points) in organization, cabinet, and armed forces offices, huge disadvantages are being subjected to different malicious codes and hacking assaults; it is important to notice illegal APs based on security of data. Here, user utilizes round-trip time (RTP) value dataset to identify legitimate and illegitimate access points over connected/remote networks. User also analyzes the performance of data utilizing the ML models, such as support vector machine, Classification 4.5, k-nearest neighbor (K-NN), multi-layer perceptron, and decision tree algorithms. This analysis determines the attacks on data in wired and remote networks.
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 Mob Comput 9(3):449–462
Han H et al (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, May 2017. https://doi.org/10.1109/iacs.2017.7921985
Pradeepkumar B et al Predicting external rogue access point in IEEE 802.11 b/g WLAN using RF signal strength. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, Sept 2017. https://doi.org/10.1109/icacci.2017.8126135
Este A, Gringoli F, Salgarelli L (2009) On the stability of the information carried by traffic flow features at the packet level. ACM SIGCOMM Comput Commun Rev 39(3):13–18
Peng, L et al (2016) Early stage internet traffic identification using data gravitation based classification. In: 2016 IEEE 14th international conference on dependable, autonomic and secure computing, 14th international conference on pervasive intelligence and computing, 2nd international conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), Auckland, New Zealand, Aug. 2016. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.98
Awad F et al (2017) Access point localization using autonomous mobile robot. In: 2017 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT), Aqaba, Jordan, July 2017. https://doi.org/10.1109/iwcmc.2017.7986377
Liu S, Liu Y, ** Z (2017) Attack behavioural analysis and secure access for wireless access point (AP) in open system authentication. In: 13th international wireless communications and mobile computing conference (IWCMC), Valencia, Spain, June 2017. https://doi.org/10.1109/IWCMC.2017.7986377
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 Wireless Inf Networks 25(3):120–135
Watkins L, Beyah R, Corbett C (2007) A passive approach to rogue access point detection. In: IEEE global telecommunications conference 2007, Washington, DC, USA, Nov 2007, pp 355–360
Yang C, Song Y, Gu G (2012) Active user-side evil twin access point detection using statistical techniques. IEEE Trans Inf Forensics Secur 7(5):1638–1651
Modi V, Parekh C (2017) Detection & analysis of evil twin attack in wireless network. Int J Adv Res Comput Sci 8(5):774–777
Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Parallel distributed processing: explorations in the microstructure of cognition, vol 1. MIT Press, Cambridge, pp 218–362
Vapnik V (2013) Support vector machine. In: The nature of statistical learning theory. Springer Science & Business Media, Berlin
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., Burlington
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Swetha, A., Shailaja, K. (2020). An Effective Approach for Security Attacks Based on Machine Learning Algorithms. In: Chillarige, R., Distefano, S., Rawat, S. (eds) Advances in Computational Intelligence and Informatics. ICACII 2019. Lecture Notes in Networks and Systems, vol 119. Springer, Singapore. https://doi.org/10.1007/978-981-15-3338-9_34
Download citation
DOI: https://doi.org/10.1007/978-981-15-3338-9_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3337-2
Online ISBN: 978-981-15-3338-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)