Publication Type : Conference Paper
Publisher : Institute of Electrical and Electronics Engineers Inc.,
Source : 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, Institute of Electrical and Electronics Engineers Inc., p.666-671 (2019)
ISBN : 9781538695333
Keywords : Binary classification, Botnet, Brain, Classification (of information), Computer crime, Cyber security, Cybercrime, Data driven technique, Deep learning, Forecasting, Generation algorithm, Learning architectures, Long short-term memory, malware, Memory architecture, Multi-class classification, Real-time prediction
Campus : Amritapuri, Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication, CISAI
Year : 2019
Abstract : Real-time prediction of domain names that are generated using the Domain Generation Algorithms (DGAs) is a challenging cyber security task. Scope to collect the vast amount of data for training favored data-driven techniques and deep learning architectures have the potential to address this challenge. This paper proposes a deep learning framework using long short-term memory (LSTM) architecture for prediction of the domain names that are generated using the DGAs. Binary classification had benign and DGA domain names and multiclass classification was performed using 20 different DGAs. For the binary classification, LSTM model gave accuracy of 98.7% and 71.3% on two different test data sets and for the multi-class classification, it gave accuracy of 68.3% and 67.0% respectively. Two diversified data sets were used to analyze the robustness of the LSTM architecture. © 2019 IEEE.
Cite this Research Publication : S. Akarsh, Sriram, S., Poornachandran, P., Menon, V. K., and Dr. Soman K. P., “Deep Learning Framework for Domain Generation Algorithms Prediction Using Long Short-term Memory”, in 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, 2019, pp. 666-671.