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Detecting malicious domain names using deep learning approaches at scale

Publication Type : Conference Paper

Publisher : Journal of Intelligent and Fuzzy Systems, IOS Press

Source : Journal of Intelligent and Fuzzy Systems, IOS Press, Volume 34, Number 3, p.1355-1367 (2018)

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Keywords : Big data, Brain, Cluster computing, Deep learning, Domain name service, Internet protocols, Learning algorithms, Learning systems, Learning-based approach, Local area networks, Local area networks (LAN), Log file, Long short-term memory, Malicious activities, Malicious detections, Network security, Personal computing, Real-time protection, Recurrent neural network (RNN), Security systems

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Computer Science, Electronics and Communication

Year : 2018

Abstract : Threats related to computer security constantly evolving and attacking the networks and internet all the time. New security threats and the sophisticated methods that hackers use can bypass the detection and prevention mechanisms. A new approach which can handle and analyze massive amount of logs from diverse sources such as network packets, Domain name system (DNS) logs, proxy logs, system/service logs etc. required. This approach can be typically termed as big data. This approach can protect and provide solution to various security issues such as fraud detection, malicious activities and other advanced persistent threats. Apache spark is a distributed big data based cluster computing platform which can store and process the security data to give real time protection. In this paper, we collect only DNS logs from client machines in local area network (LAN) and store it in a server. To find the domain name as either benign or malicious, we propose deep learning based approach. For comparison, we have evaluated the effectiveness of various deep learning approaches such as recurrent neural network (RNN), long short-term memory (LSTM) and other traditional machine learning classifiers. Deep learning based approaches have performed well in comparison to the other classical machine learning classifiers. The primary reason is that deep learning algorithms have the capability to obtain the right features implicitly. Moreover, LSTM has obtained highest malicious detection rate in all experiments in comparison to the other deep learning approaches. © 2018 - IOS Press and the authors. All rights reserved.

Cite this Research Publication : R. Vinayakumar, Dr. Soman K. P., and Poornachandran, P., “Detecting malicious domain names using deep learning approaches at scale”, in Journal of Intelligent and Fuzzy Systems, 2018, vol. 34, pp. 1355-1367.

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