Abstract
The advancements of Internet of Things (IoT)-based platforms have considerably motivated the growth in malicious attacks to IoT devices and the network infrastructure. This has given rise to information theft and vulnerabilities arising due to the flow of anomalous traffic throughout the network causing a partial or complete interruption in normal network activities. Hence, it is crucial for network administrators and service providers to track the devices associated with a network as well as the type of traffic flowing through it. In relation to this, the present work provides an ensemble-based approach for characterizing the traffic flows pertaining to IoT systems. The proposed framework comprises of adaptive boosting (AdaBoost) algorithm and Naïve Bayes (NB) algorithm for identifying anomalous traffic flow behavior. Further, the performance of the proposed algorithm is comparatively assessed with conventional learning models. It is experimentally observed that the proposed algorithm provides a prediction accuracy of 96.316% along with a sensitivity value of 0.931, positive prediction value (PPV) of 0.992, and F-score of 0.959 for identifying anomalous traffic flows in the network. The proposed algorithm also provides a substantially high AUC-ROC value of 0.991. Hence, it can be evidently observed that the proposed approach may prove to be a befitting choice for designing intrusion detection system (IDS) and management of traffic flow behavior in large heterogeneous 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
References
Salman O, Elhajj IH, Chehab A, Kayssi A (2019) A machine learning based framework for IoT device identification and abnormal traffic detection. Transactions on emerging telecommunications technologies, e3743
Panda S, Panda G (2020) Intelligent classification of IoT traffic in healthcare using machine learning techniques. In: 2020 6th international conference on control, automation and robotics (ICCAR). IEEE, pp 581–585
Singh SK, Mishra AK, Rain fall prediction using bigdata analytics
Yao H, Gao P, Wang J, Zhang P, Jiang C, Han Z (2019) Capsule network assisted IoT traffic classification mechanism for smart cities. IEEE Int Things J 6(5):7515–7525
Tahaei H, Afifi F, Asemi A, Zaki F, Anuar NB (2020) The rise of traffic classification in IoT networks: A survey. J Network Comput Appl 154:102538
Bebortta S, Singh AK, Mohanty S, Senapati D (2020) Characterization of range for smart home sensors using Tsallis’ entropy framework. In: Advanced computing and intelligent engineering. Springer, Singapore, pp 265–276
Bebortta S, Panda M, Panda S (2020) Classification of pathological disorders in children using random forest algorithm. In: 2020 international conference on emerging trends in information technology and engineering (ic-ETITE). IEEE, pp 1–6
Bebortta S, Senapati D, Rajput NK, Singh AK, Rathi VK, Pandey HM, ... Tiwari P (2020) Evidence of power-law behavior in cognitive IoT applications. Neural Comput Appl 1–13
Kumar A, Lim TJ (2019) EDIMA: early detection of IoT malware network activity using machine learning techniques. In: 2019 IEEE 5th world forum on internet of things (WF-IoT). IEEE, pp 289–294
Glennan T, Leckie C, Erfani SM (2016) Improved classification of known and unknown network traffic flows using semi-supervised machine learning. In: Australasian conference on information security and privacy. Springer, Cham, pp 493–501
Khan MS, Ferens K, Kinsner W (2014) A chaotic complexity measure for cognitive machine classification of cyber-attacks on computer networks. Int J Cogn Inf Natural Intell (IJCINI) 8(3):45–69
Perera P, Tian YC, Fidge C, Kelly W (2017) A comparison of supervised machine learning algorithms for classification of communications network traffic. In: International conference on neural information processing. Springer, Cham, pp 445–454
Sharma K, Gupta BB (2019) Towards privacy risk analysis in Android applications using machine learning approaches. Int J E-Services Mobile Appl (IJESMA) 11(2):1–21
Rathore S, Park JH (2018) Semi-supervised learning based distributed attack detection framework for IoT. Appl Soft Comput 72:79–89
Kozik R, Choraś M, Ficco M, Palmieri F (2018) A scalable distributed machine learning approach for attack detection in edge computing environments. J Parallel Distr Comput 119:18–26
Lin H, Yan Z, Fu Y (2019) Adaptive security-related data collection with context awareness. J Network Comput Appl 126:88–103
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, Berlin
Zhou ZH (2012) Ensemble methods: foundations and algorithms. CRC Press, Boca Raton
Rish I (2001) An empirical study of the naive Bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol 3, No 22, pp 41–46
Moustafa N, Turnbull B, Choo KKR (2018) An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things. IEEE Int Things J 6(3):4815–4830
Adekitan AI, Abolade J, Shobayo O (2019) Data mining approach for predicting the daily Internet data traffic of a smart university. J Big Data 6(1):11
Rahmat S, Niyaz Q, Mathur A, Sun W, Javaid AY (2019) Network traffic-based hybrid malware detection for smartphone and traditional networked systems. In: 2019 IEEE 10th annual ubiquitous computing, electronics & mobile communication conference (UEMCON). IEEE, pp 0322–0328
Gratian M, Bhansali D, Cukier M, Dykstra J (2019) Identifying infected users via network traffic. Comput Secur 80:306–316
Khonde SR, Ulagamuthalvi V (2019) Ensemble-based semi-supervised learning approach for a distributed intrusion detection system. J Cyber Secur Technol 3(3):163–188
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bebortta, S., Singh, S.K. (2022). An Opportunistic Ensemble Learning Framework for Network Traffic Classification in IoT Environments. In: Giri, D., Raymond Choo, KK., Ponnusamy, S., Meng, W., Akleylek, S., Prasad Maity, S. (eds) Proceedings of the Seventh International Conference on Mathematics and Computing . Advances in Intelligent Systems and Computing, vol 1412. Springer, Singapore. https://doi.org/10.1007/978-981-16-6890-6_35
Download citation
DOI: https://doi.org/10.1007/978-981-16-6890-6_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6889-0
Online ISBN: 978-981-16-6890-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)