Abstract
Predicting Cyber-attacks on IoT Networks using Machine Learning has a definite advantage over traditional methods because it helps secure against future attacks by identifying hidden patterns from past data, thereby improving the capability of a network. Thus automated systems are being developed which can be used to identify Cyber-attacks on IoT Networks using various machine learning techniques. In this work, three different types of features selection techniques were applied to the UNSW-NB15 data to find the best combination of relevant features. These selected sets of relevant features were considered to train five different deep learning architectures used to predict cyber attacks by varying the number of hidden layers. To handle the dataset’s class imbalance problem, we have considered three different sampling techniques: SMOTE, Borderline SMOTE (BSMOTE), and Adaptive Synthetic Sampling (ADASYN). The experimental results on the UNSW-NB15 data highlight that the usage of considered feature selection techniques and class balance techniques does not significantly improve the predictive ability to detect cyber attacks. The results also suggest that variation in Deep learning Architecture impacts the prediction of cyberattacks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kumar, K., Kumar, N., Shah, R.: Role of IoT to avoid spreading of COVID-19. Int. J. Intell. Netw. 1, 32–35 (2020)
Saharkhizan, M., Azmoodeh, A., Dehghantanha, A., Choo, K.-K.R., Parizi, R.M.: An ensemble of deep recurrent neural networks for detecting IoT cyber attacks using network traffic. IEEE Internet Things J. 7(9), 8852–8859 (2020)
Stellios, I., Kotzanikolaou, P., Psarakis, M., Alcaraz, C., Lopez, J.: A survey of IoT-enabled cyberattacks: assessing attack paths to critical infrastructures and services. IEEE Commun. Surv. Tutor. 20(4), 3453–3495 (2018)
Jyothsna, V., Rama Prasad, V.V., Munivara Prasad, K.: A review of anomaly based intrusion detection systems. Int. J. Comput. Appl. 28(7), 26–35 (2011)
Al-Garadi, M.A., Mohamed, A., Al-Ali, A.K., Du, X., Ali, I., Guizani, M.: A survey of machine and deep learning methods for Internet of Things (IoT) security. IEEE Commun. Surv. Tutor. 22(3), 1646–1685 (2020)
Sahu, A.K., Sharma, S., Tanveer, M., Raja, R.: Internet of things attack detection using hybrid deep learning model. Comput. Commun. 176, 146–154 (2021)
Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1–6. IEEE (2015)
Moustafa, N., Slay, J.: The evaluation of network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set, pp. 1–14, January 2016
Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1) (2014)
Tait, K.-A., et al.: Intrusion detection using machine learning techniques: an experimental comparison. arXiv preprint arXiv:2105.13435 (2021)
Wheelus, C., Bou-Harb, E., Zhu, X.: Tackling class imbalance in cyber security datasets. In: 2018 IEEE International Conference on Information Reuse and Integration (IRI), pp. 229–232. IEEE (2018)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6. IEEE (2009)
Haq, N.F., Onik, A.R., Hridoy, M.A.K., Rafni, M., Shah, F.M., Farid, D.M.: Application of machine learning approaches in intrusion detection system: a survey. IJARAI Int. J. Adv. Res. Artif. Intell. 4(3), 9–18 (2015)
Amiri, F., Yousefi, M.M.R., Lucas, C., Shakery, A., Yazdani, N.: Mutual information-based feature selection for intrusion detection systems. J. Netw. Comput. Appl. 34(4), 1184–1199 (2011)
XIAOHUI XIE. Principal component analysis (2019)
Yin, C., Zhu, Y., Fei, J., He, X.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954–21961 (2017)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Philip Kegelmeyer, W.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Acknowledgment
This research is funded by TestAIng Solutions Pvt. Ltd.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Akash, B.S., Yannam, P.K.R., Ruthvik, B.V.S., Kumar, L., Murthy, L.B., Krishna, A. (2022). Predicting Cyber-Attacks on IoT Networks Using Deep-Learning and Different Variants of SMOTE. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-030-99587-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-99587-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99586-7
Online ISBN: 978-3-030-99587-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)