Abstract
Wireless sensor networks (WSNs) are developing at an incredible pace because of their cost-effective solutions for applications like military and medical. WSN consists of a large number of nodes that have to suffer from constraints like limited computation capacity and limited battery capacity. There are a lot of attacks in WSNs; one of them is the distributed denial of service attack. Many studies have shown that decreasing the redundancy of relevant features from a dataset can make a model more accurate and efficient. In this paper, correlation-based feature selection, principal component analysis, linear discriminant analysis, recursive feature elimination, and univariate feature selection are used for feature selection. Results are compared after selecting features using these techniques. A novel technique for feature selection is introduced, which combines five feature selection techniques as a stack. After implementing the feature selection techniques, the model is trained with five machine learning algorithms, namely SVM, perceptron, K-nearest neighbor, stochastic gradient descent, and XGBoost. Finally, the model is evaluated with the help of K-fold cross-validation. Among all of the techniques best accuracy of 99.87% is achieved with the XGBoost classifier after selecting the best eleven features from the KDD dataset.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Aamir M, Zaidi SMA (2019) Clustering-based semi-supervised machine learning for DDoS attack classification. J King Saud Univ Comput Inf Sci 33(4):436–446. https://doi.org/10.1016/j.jksuci.2019.02.003
Abd-Eldayem MM (2014) A proposed HTTP service based IDS. Egypt Info J 15(1):13–24. https://doi.org/10.1016/j.eij.2014.01.001
Abdeldayem EH, Ibrahim AS, Ahmed AM, Genedi ES, Tantawy WH (2015) Positive remodeling index by MSCT coronary angiography: a prognostic factor for early detection of plaque rupture and vulnerability. Egypt J Radiol Nucl Med 46(1):13–24
Abualigah L, Diabat A (2021) Advances in sine cosine algorithm: a comprehensive survey. Artif Intell Rev 54:2567–2608. https://doi.org/10.1007/s10462-020-09909-3
Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609
Aburomman AA, Reaz MBI (2017) A survey of intrusion detection systems based on ensemble and hybrid classifiers. Comput Secur 65:135–152
Aljawarneh S, Aldwairi M, Yassein MB (2018) Anomaly-based intrusion detection system through feature selection analysis and building a hybrid efficient model. J Comput Sci 25:152–160
Alkasassbeh M, Al-Naymat G, Hassanat A, Almseidin M (2016) Detecting distributed denial of service attacks using data mining techniques. Int J Adv Comput Sci Appl 7(1):436–445
Alrawashdeh K, Purdy C (2016) Toward an online anomaly intrusion detection system based on deep learning. In: 2016 15th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 195–200
Amiri F, Yousefi MR, Lucas C, Shakery A, Yazdani N (2011) Mutual information-based feature selection for intrusion detection systems. J Netw Comput Appl 34(4):1184–1199
Attia M, Senouci SM, Sedjelmaci H, Aglzim EH, Chrenko D (2018) An efficient intrusion detection system against cyber-physical attacks in the smart grid. Comput Electr Eng 68:499–512
Borkar GM, Patil LH, Dalgade D, Hutke A (2019) A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN: a data mining concept. Sustain Comput Inform Syst 23:120–135
Calix RA, Sankara R (2013) Feature ranking and support vector machines classification analysis of the NSL-KDD intrusion detection corpus. In: International florida artificial intelligence research society conference, pp 292–295
Chae HS, Choi SH (2014) Feature selection for efficient intrusion detection using attribute ratio. Int J Comput Commun 8:134–139
Chebrolu S, Abraham A, Thomas JP (2005) Feature deduction and ensemble design of intrusion detection systems. Comput Secur 24(4):295–307
Chen Y, Abraham A, Yang B (2006) Feature selection and classification using a flexible neural tree. Neurocomputing 70(1–3):305–313
Chen Z, Jiang F, Cheng Y, Gu X, Liu W, Peng J (2018) XGBoost classifier for DDoS attack detection and analysis in SDN-based cloud. In: 2018 IEEE international conference on big data and smart computing (big comp). IEEE, pp 251–256
Cui J, Wang M, Luo Y, Zhong H (2019) DDoS detection and defense mechanism based on cognitive-inspired computing in SDN. Future Gener Comput Syst 97:275–283
Dayanandam G, Rao TV, Babu DB, Durga SN (2019) DDoS attacks—analysis and prevention. In: Innovations in computer science and engineering. Springer, Singapore, pp 1–10
Dong B, Wang X (2016) Comparison deep learning method to traditional methods using for network intrusion detection. In: 2016 8th IEEE international conference on communication software and networks (ICCSN). IEEE, pp 581–585
Doshi R, Apthorpe N, Feamster N (2018) Machine learning DDoS detection for the consumer internet of things devices. In: 2018 IEEE security and privacy workshops (SPW). IEEE, pp 29–35
Fotue D, Melakessou F, Labiod H, Engel T (2011) Mini-sink mobility with diversity-based routing in wireless sensor networks. In: Proceedings of the 8th ACM symposium on performance evaluation of wireless ad hoc, sensor, and ubiquitous networks, pp 9–16
Gao N, Gao L, Gao Q, Wang H (2014) An intrusion detection model based on deep belief networks. In: 2014 second international conference on advanced cloud and big data. IEEE, pp 247–252
Ghosh P, Debnath C, Metia D, Dutta R (2014) An efficient hybrid multilevel intrusion detection system in a cloud environment. IOSR J Comput Eng 16(4):16–26
Gündüz SY, Çeter MN (2018) Feature selection and comparison of classification algorithms for intrusion detection. Anadolu Univ J Sci Technol A Appl Sci Eng 19(1):206–218. https://doi.org/10.18038/aubtda.356705
Hariharan M, Abhishek HK, Prasad BG (2019) DDoS attack detection using C5.0 machine learning algorithm. IJ Wirel Microwave Technol 1:52–59
Hasan MAM, Nasser M, Ahmad S, Molla KI (2016) Feature selection for intrusion detection using random forest. J Inf Secur 7(3):129–140
http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. Accessed 28 Oct 1999
Idhammad M, Afdel K, Belouch M (2018) Semi-supervised machine learning approach for DDoS detection. Appl Intell 48(10):3193–3208
Javaid A, Niyaz Q, Sun W, Alam M (2016) A deep learning approach for network intrusion detection systems. EAI Endorsed Trans Secur Saf 3(9):e2
Khamparia A, Pande S, Gupta D, Khanna A, Sangaiah AK (2020) Multi-level framework for anomaly detection in social networking. Library Hi-Tech
Khasawneh AM, Kaiwartya O, Abualigah LM, Lloret J (2020) Green computing in underwater wireless sensor networks pressure-centric energy modeling. IEEE Syst J 14(4):4735–4745
Kumari S, Khan MK, Atiquzzaman M (2015) User authentication schemes for wireless sensor networks: a review. Ad Hoc Netw 27:159–194
Li Y, Ma R, Jiao R (2015) A hybrid malicious code detection method based on deep learning. Int J Secur Appl 9(5):205–216
Li C, Wu Y, Yuan X, Sun Z, Wang W, Li X, Gong L (2018) Detection and defense of DDoS attack–based on deep learning in OpenFlow-based SDN. Int J Commun Syst 31(5):e3497
López J, Zhou J (eds) (2008) Wireless sensor network security, vol 1. Ios Press, Amsterdam
Madhavan MV, Pande S, Umekar P, Mahore T, Kalyankar D (2021) Comparative analysis of detection of email spam with the aid of machine learning approaches. In: IOP conference series: materials science and engineering, vol 1022, no 1. IOP Publishing, pp 012113
Mallikarjunan KN, Bhuvaneshwaran A, Sundarakantham K, Shalinie SM (2019) DDAM: detecting DDoS attacks using a machine learning approach. In: Computational intelligence: theories, applications and future directions, vol I. Springer, Singapore, pp 261–273
Nkiama H, Said SZM, Saidu M (2016) A subset feature elimination mechanism for the intrusion detection system. Int J Adv Comput Sci Appl 7(4):148–157
Ozdemir S, Xiao Y (2009) Secure data aggregation in wireless sensor networks: A comprehensive overview. Comput Netw 53(12):2022–2037
Pande S, Gadicha AB (2015) Prevention mechanism on DDOS attacks by using multilevel filtering of distributed firewalls. International Journal on Recent and Innovation Trends in Computing and Communication 3(3):1005–1008
Pande SD, Khamparia A (2019) A review on detection of DDOS attack using machine learning and deep learning techniques. Think India J 22(16):2035–2043
Pande SD, Bhagat VB (2016) Hybrid wireless network approach for QoS. Int J Recent Innov Trends Comput Commun 4(4):327–332
Pande S, Khamparia A, Gupta D, Thanh DN (2021a) DDOS detection using machine learning technique. In: Recent studies on computational intelligence. Springer, Singapore, pp 59–68
Pande S, Khamparia A, Gupta D (2021b) An intrusion detection system for healthcare systems using machine and deep learning. World J Eng. https://doi.org/10.1108/WJE-04-2021-0204
Potluri S, Diedrich C (2016) Accelerated deep neural networks for the enhanced intrusion detection system. In: 2016 IEEE 21st international conference on emerging technologies and factory automation (ETFA). IEEE, pp 1–8
Safaldin M, Otair M, Abualigah L (2021) Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. J Ambient Intell Humaniz Comput 12(2):1559–1576
Selvakumar B, Muneeswaran K (2019) Firefly algorithm-based feature selection for network intrusion detection. Comput Secur 81:148–155
Shah SAR, Issac B (2018) Performance comparison of intrusion detection systems and application of machine learning to Snort system. Future Gener Comput Syst 80:157–170
Tesfahun A, Bhaskari DL (2013) Intrusion detection using random forests classifier with SMOTE and feature reduction. In: 2013 international conference on cloud & ubiquitous computing & emerging technologies. IEEE, pp 127–132
Tripathy S, Nandi S (2008) Defense against outside attacks in wireless sensor networks. Comput Commun 31(4):818–826
Vinutha HP, Basavaraju P (2018) Analysis of feature selection and ensemble classifier methods for intrusion detection. Int J Natural Comput Res 7(1):57–72
You L, Li Y, Wang Y, Zhang J, Yang Y (2016) A deep learning-based RNNs model for an automatic security audit of short messages. In: 2016 16th international symposium on communications and information technologies (ISCIT). IEEE, pp 225–229
Zekri M, El Kafhali S, Aboutabit N, Saadi Y (2017) DDoS attack detection using machine learning techniques in cloud computing environments. In: 2017 3rd international conference of cloud computing technologies and applications (CloudTech). IEEE, pp 1–7. https://doi.org/10.1109/CloudTech.2017.8284731
Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pande, S., Khamparia, A. & Gupta, D. Feature selection and comparison of classification algorithms for wireless sensor networks. J Ambient Intell Human Comput 14, 1977–1989 (2023). https://doi.org/10.1007/s12652-021-03411-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03411-6