3/2021 - 5 |
Deep Learning Based DNS Tunneling Detection and Blocking SystemALTUNCU, M. A. , GULAGIZ, F. K. , OZCAN, H. , BAYIR, O. F. , GEZGIN, A. , NIYAZOV, A. , CAVUSLU, M. A. , SAHIN, S. |
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Author keywords
artificial neural networks, computer networks, domain name system, intrusion detection, machine learning
References keywords
tunneling(12), learning(10), detection(9), networks(7), information(7), security(6), machine(6), data(6), science(5), technology(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2021-08-31
Volume 21, Issue 3, Year 2021, On page(s): 39 - 48
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.03005
Web of Science Accession Number: 000691632000005
SCOPUS ID: 85114771421
Abstract
The main purpose of DNS is to convert domain names into IPs. Due to the inadequate precautions taken for the security of DNS, it is used for malicious communication or data leakage. Within the scope of this study, a real-time deep network-based system is proposed on live networks to prevent the common DNS tunneling threats over DNS. The decision-making capability of the proposed system at the instant of threat on a live system is the particular feature of the study. Networks trained with various deep network topologies by using the data from Alexa top 1 million sites were tested on a live network. The system was integrated to the network during the tests to prevent threats in real-time. The result of the tests reveal that the threats were blocked with success rate of 99.91%. Obtained results confirm that we can block almost all tunnel attacks over DNS protocol. In addition, the average time to block each tunneled package was calculated to be 0.923 ms. This time clearly demonstrates that the network flow will not be affected, and no delay will be experienced in the operation of our system in real-time. |
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[1] GraphTunnel: Robust DNS Tunnel Detection Based on DNS Recursive Resolution Graph, Gao, Guangyuan, Niu, Weina, Gong, Jiacheng, Gu, Dujuan, Li, Song, Zhang, Mingxue, Zhang, Xiaosong, IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, Issue , 2024.
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[2] A Hybrid Deep Learning Approach for Intrusion Detection in IoT Networks, EMEC, M., OZCANHAN, M. H., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 22, 2022.
Digital Object Identifier: 10.4316/AECE.2022.01001 [CrossRef] [Full text]
[3] Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions, Rizvi, Syed, Scanlon, Mark, Mcgibney, Jimmy, Sheppard, John, IEEE Access, ISSN 2169-3536, Issue , 2022.
Digital Object Identifier: 10.1109/ACCESS.2022.3214506 [CrossRef]
[4] Malicious DNS detection by combining improved transformer and CNN, Li, Heyu, Li, Zhangmeizhi, Zhang, Shuyan, Pu, Xiao, Scientific Reports, ISSN 2045-2322, Issue 1, Volume 14, 2024.
Digital Object Identifier: 10.1038/s41598-024-81189-1 [CrossRef]
[5] Real-Time Detection System for Data Exfiltration over DNS Tunneling Using Machine Learning, Abualghanam, Orieb, Alazzam, Hadeel, Elshqeirat, Basima, Qatawneh, Mohammad, Almaiah, Mohammed Amin, Electronics, ISSN 2079-9292, Issue 6, Volume 12, 2023.
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[6] Real-time Threat Detection Strategies for Resource-constrained Devices, Hamidouche, Mounia, Demissie, Biniam Fisseha, Cherif, Bilel, 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), ISBN 979-8-3503-6944-1, 2024.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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