Abstract
Cybercrime has expanded in various areas as the number of websites and hosting services has grown significantly. The identification of hostile domain names has recently gained the interest of researchers in order to avoid cyber-attacks. In terms of network traffic and content, several attempts have been made to distinguish these names. The novel approach proposed in this paper utilizes the neural network-based algorithms to recognize the potentially hostile domain names. An LSTM network is created and prepared based on the dataset. The primary task is to first divide the URL into subdomain, domain, and domain-suffix. Then based on this, the proposed neural network is trained to classify the given data set as malicious or benign. The proposed system can perform well with a higher level of accuracy on the validation set.
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
Satoh, A., Fukuda, Y., Hayashi, T., Kitagata, G.: A superficial analysis approach for identifying malicious domain names generated by DGA malware. IEEE Open J. Commun. Soc. 1, 1837–1849 (2020)
Fukuda, K., Heidemann, J., Qadeer, A.: Detecting malicious activity with DNS backscatter over time. IEEE/ACM Trans. Netw. 25(5), 3203–3218 (2017)
Analysis of Bitnet Names-2020. https://ieeexplore.ieee.org/abstract/document/8756252
Lazar, D., Cohen, K., Freund, A., Bartik, A., Ron, A.: IMDoC: identification of malicious domain campaigns via DNS and communicating files. IEEE Access 9, 45242–45258 (2021)
Centre of Excellence in DNS Security Report (2020)
Balzarotti, D., Stolfo, S.J., Cova, M. (eds.): Research in Attacks, Intrusions and Defenses: 15th International Symposium, RAID 2012, Proceedings, vol. 7462, pp. 12–14. Springer, Amsterdam, The Netherlands (2012)
Yuan, J., Chen, G., Tian, S., Pei, X.: Malicious URL detection based on a parallel neural joint model. IEEE Access 9, 9464–9947 (2021)
Liu, Z., Zhang, Y., Chen, Y., Fan, X., Dong, C.: Detection of algorithmically generated domain names using the recurrent convolutional neural network with spatial pyramid pooling. Entropy 22(9), 1058 (2020)
Ranganathan, G.: A study to find facts behind preprocessing on deep learning algorithms. J. Innov. Image Process. (JIIP) 3(01), 66–74 (2021)
Shakya, S., Pulchowk, L.N., Smys, S.: Anomalies detection in fog computing architectures using deep learning. J. Trends Comput. Sci. Smart Technol. (1), 46–55 (2020)
Shakya, S., Nepal, L.: Computational enhancements of wearable healthcare devices on pervasive computing system. J. Ubiquitous Comput. Commun. Technol. (UCCT) 2(02), 98–108 (2020)
Chen, J.I.Z., Yeh, L.-T.: Analysis of the impact of mechanical deformation on strawberries harvested from the farm. J. ISMAC (3), 166–172 (2020)
Manoharan, S.: Early diagnosis of lung cancer with probability of malignancy calculation and automatic segmentation of lung CT scan images. J. Innov. Image Process. (JIIP) 2(04), 175–186 (2020)
Cucchiarelli, A., Morbidoni, C., Spalazzi, L., Baldi, M.: Algorithmically generated malicious domain names detection based on n-grams features. Exp. Syst. Appl. 170, 114551 (2021)
Pei, X., Tian, S., Yu, L., Wang, H., Peng, Y.: A two-stream network based on capsule networks and sliced recurrent neural networks for DGA botnet detection. J. Netw. Syst. Manage. 28(4), 1694–1721 (2020)
Vinayakumar, R., Soman, K.P., Poornachandran, P.: Deepdga-minet: cost-sensitive deep learning based framework for handling multiclass imbalanced dga detection. In: Handbook of Computer Networks and Cyber Security, pp. 905–928. Springer, Cham (2020)
Chen, Z., Roussopoulos, M., Liang, Z., Zhang, Y., Chen, Z., Delis, A.: Malware characteristics and threats on the internet ecosystem. J. Syst. Softw. 85(7), 1650–1672 (2012)
Nelms, T., Perdisci, R., Ahamad, M.: Execscent: mining for new c&c domains in live networks with adaptive control protocol templates. In: 22nd {USENIX} Security Symposium ({USENIX} Security 13), pp. 589–604 (2013)
Vinayakumar, R., Soman, K.P., Poornachandran, P.: Detecting malicious domain names using deep learning approaches at scale. J. Intel. Fuzzy Syst. 34(3), 1355–1367 (2018)
Ren, F., Jiang, Z., Wang, X., Liu, J.: A DGA domain names detection modeling method based on integrating an attention mechanism and deep neural network. Cybersecurity 3(1), 1–13 (2020)
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 Singapore Pte Ltd.
About this paper
Cite this paper
Aarthi, B., Jeenath Shafana, N., Flavia, J., Chelliah, B.J. (2022). A Hybrid Multiclass Classifier Approach for the Detection of Malicious Domain Names Using RNN Model. In: Smys, S., Tavares, J.M.R.S., Balas, V.E. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1420. Springer, Singapore. https://doi.org/10.1007/978-981-16-9573-5_35
Download citation
DOI: https://doi.org/10.1007/978-981-16-9573-5_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9572-8
Online ISBN: 978-981-16-9573-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)