Skip to main content
SpringerLink
Log in

Feature selection and comparison of classification algorithms for wireless sensor networks

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

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.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    Article  MathSciNet  MATH  Google Scholar 

  • Aburomman AA, Reaz MBI (2017) A survey of intrusion detection systems based on ensemble and hybrid classifiers. Comput Secur 65:135–152

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Chebrolu S, Abraham A, Thomas JP (2005) Feature deduction and ensemble design of intrusion detection systems. Comput Secur 24(4):295–307

    Article  Google Scholar 

  • Chen Y, Abraham A, Yang B (2006) Feature selection and classification using a flexible neural tree. Neurocomputing 70(1–3):305–313

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hariharan M, Abhishek HK, Prasad BG (2019) DDoS attack detection using C5.0 machine learning algorithm. IJ Wirel Microwave Technol 1:52–59

    Google Scholar 

  • Hasan MAM, Nasser M, Ahmad S, Molla KI (2016) Feature selection for intrusion detection using random forest. J Inf Secur 7(3):129–140

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Kumari S, Khan MK, Atiquzzaman M (2015) User authentication schemes for wireless sensor networks: a review. Ad Hoc Netw 27:159–194

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Ozdemir S, Xiao Y (2009) Secure data aggregation in wireless sensor networks: A comprehensive overview. Comput Netw 53(12):2022–2037

    Article  MATH  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Pande SD, Bhagat VB (2016) Hybrid wireless network approach for QoS. Int J Recent Innov Trends Comput Commun 4(4):327–332

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Selvakumar B, Muneeswaran K (2019) Firefly algorithm-based feature selection for network intrusion detection. Comput Secur 81:148–155

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Lovely Professional University, Punjab, India

    Sagar Pande

  2. Babasaheb Bhimrao Ambedkar University, Lucknow, India

    Aditya Khamparia

  3. Department of Computer Science Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India

    Deepak Gupta

Authors
  1. Sagar Pande
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aditya Khamparia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Deepak Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sagar Pande.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03411-6

Keywords

Search

Navigation