Detection of malicious domain names using machine learning and deep learning models
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DMD-2018
Data statistics-DMD-2018.pdf
README.md

README.md

DMD2018

DOI

This data set ia avilable for further research. As our dataset is for research purpose, please send us your complete name, supervisor name, university name and an official email from your university e-mail address to vinayakumarr77@gmail.com.

References

R. Vinayakumar, P. Poornachandran, K. Soman, Scalable framework for cyber threat situational awareness based on domain name systems data analysis, in: Big Data in Engineering Applications, Springer, 2018, pp. 113–142.

R. Vinayakumar, K. Soman, P. Poornachandran, Detecting malicious domain names using deep learning approaches at scale, Journal of Intelligent & Fuzzy Systems 34 (3) (2018) 1355– 1367.

R. Vinayakumar, K. Soman, P. Poornachandran, S. Sachin Kumar, Evaluating deep learning approaches to characterize and classify the dgas at scale, Journal of Intelligent & Fuzzy Systems 34 (3) (2018) 1265–1276.

Vinayakumar R, Soman KP, Prabaharan Poornachandran and Pradeep Menon, A deep-dive on Machine learning for Cybersecurity use cases, In: Brij Gupta, Michael Sheng (eds) Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices CRC press, USA [InPress]

Vysakh S Mohan, Vinayakumar R, Soman Kp and Prabaharan Poornachandran, S.P.O.O.F Net: Syntactic Patterns for identification of Ominous Online Factors, BioSTAR 2018, In Security and Privacy (SP), 2017 IEEE Symposium [InPress]

Vinayakumar R, Soman KP, Prabaharan Poornachandran, BigCogNet: Big data based Cognitive Security System for an Organization In: Mamoun Alazab and MingJian Tang (eds) Deep Learning Applications for Cyber Security, Advanced Sciences and Technologies for Security Applications, Springer [under-review]

Vinayakumar R, Soman KP, DGANet: Applying traditional machine learning and deep learning models to detect and categorize DGA, 2018