Abstract
The malware families uses Domain Generated Algorithms (DGA) to generate and register different domains to connect to the command and Control server. To improve the automated analysis of DGA-based malware, we have developed an analysis system for detection and classification of DGA’s. In this paper we proposed to take a string of characters as input given in the domain names and classify them as either benign or malicious domain name using deep learning architectures such as Long Short Term Memory (LSTM) and Bidirectional LSTM. We have used the data set given by shared task on Detecting Malicious Domain names (DMD 2018). We have developed a system for both binary and multiclass classification task to detect the malicious domain names. We have observed that the proposed model for binary classification performed better than multiclass classification.
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
Does Alexa have a list of its top-ranked websites? https://support.alexa.com
OpenDNS domain list. https://umbrella.cisco.com/blog
Le, H., Pham, Q., Sahoo, D., Hoi, S.C.: URLnet: Learning a URL representation with deep learning for malicious URL detection. arXiv preprint arXiv:1802.03162 (2018)
Mohan, V.S., Vinayakumar, R., Soman, K., Poornachandran, P.: SPOOF net: syntactic patterns for identification of ominous online factors. In: 2018 IEEE Security and Privacy Workshops (SPW), pp. 258–263. IEEE (2018)
Vinayakumar, R., Soman, K.P.: Applying traditional machine learning and deep learning models to detect and categorize DGA. Big Data Eng. Appl. (2018, under-review)
Sahoo, D., Liu, C., Hoi, S.C.: Malicious URL detection using machine learning: a survey. arXiv preprint arXiv:1701.07179 (2017)
Vinayakumar, R., Poornachandran, P., Soman, K.P.: Scalable framework for cyber threat situational awareness based on domain name systems data analysis. In: Roy, S.S., Samui, P., Deo, R., Ntalampiras, S. (eds.) Big Data in Engineering Applications. SBD, vol. 44, pp. 113–142. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8476-8_6
Vinayakumar, R., Soman, K., Poornachandran, P.: Detecting malicious domain names using deep learning approaches at scale. J. Intell. Fuzzy Syst. 34(3), 1355–1367 (2018)
Vinayakumar, R., Soman, K., Poornachandran, P., Sachin Kumar, S.: Evaluating deep learning approaches to characterize and classify the DGAs at scale. J. Intell. Fuzzy Syst. 34(3), 1265–1276 (2018)
Vinayakumar R, Soman KP, P.P.: BigCogNet: big data based cognitive security system for an organization. In: Alazab, M., Tang, M.J. (eds.) Deep Learning Applications for Cyber Security, Advanced Sciences and Technologies for Security Applications (under-review)
Vinayakumar, R, Soman KP, P.P., Menon, P.: A deep-dive on Machine learning for Cybersecurity use cases. In: Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices. CRC Press (In Press)
Woodbridge, J., Anderson, H.S., Ahuja, A., Grant, D.: Predicting domain generation algorithms with long short-term memory networks. arXiv preprint arXiv:1611.00791 (2016)
Yu, B., Pan, J., Hu, J., Nascimento, A., De Cock, M.: Character level based detection of DGA domain names. In: ICLR (2018). To appear
Zeng, F., Chang, S., Wan, X.: Classification for DGA-based malicious domain names with deep learning architectures. Int. J. Intell. Inf. Syst. 6(6), 67 (2017)
Acknowledgment
We would like to thank the management of SSN College of Engineering for funding GPU system, which helps us to carry out the deep learning related research work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bharathi, B., Bhuvana, J. (2019). Domain Name Detection and Classification Using Deep Neural Networks. In: Thampi, S., Madria, S., Wang, G., Rawat, D., Alcaraz Calero, J. (eds) Security in Computing and Communications. SSCC 2018. Communications in Computer and Information Science, vol 969. Springer, Singapore. https://doi.org/10.1007/978-981-13-5826-5_53
Download citation
DOI: https://doi.org/10.1007/978-981-13-5826-5_53
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5825-8
Online ISBN: 978-981-13-5826-5
eBook Packages: Computer ScienceComputer Science (R0)