Botnet Detection Model in Encrypted Traffics Software-Defined Network (SDN) Using Deep Neural Network (DNN)
How to cite (IJASEIT) :
C. Yin, Y. Zhu, J. Fei and X. He, “A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks,” IEEE Access, vol. 5, pp. 21954 - 2196, 2017.
U. Wijesinghe, U. Tupakula and V. Varadharajan, “Botnet detection using software defined networking,” 2015 22nd International Conference on Telecommunications (ICT), pp. 219-224, 2015.
P. Wang , F. Ye, X. Chen and Y. Qian, “Datanet: Deep Learning Based Encrypted Network Traffic Classification in SDN Home Gateway,” EEE Access, vol. 6, pp. 55380 - 55391, 2018.
M. K. Putchala, “Deep Learning Approach for Intrusion Detection System (IDS) in the Internet of Things (IoT) Network using Gated Recurrent Neural Networks (GRU),” CORE Scholar Wright State University, Ohio, 2017.
C. V. Neu, A. F. Zorzo, A. M. S. Orozco and R. A. Michelin, “An approach for detecting encrypted insider attacks on OpenFlow SDN Networks,” International Conference for Internet Technology and Secured Transactions, pp. 210-215, 2017.
H. Mutaher, P. Kumar and A. Wahid, “Openflow Controller-Based Sdn:Security Issues And Countermeasures,” International Journal of Advanced Research in Computer Science, vol. 9, no. 2, pp. 397-401, 2018.
Y. M. Mahardhika, A. Sudarsono and A. R. Barakbah, “An Implementation of Botnet Dataset to Predict Accuracy Based on Network Flow Model,” Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017, Vols. 2017-January, pp. 33-39, 2017.
K. Kim and M. E. Aminanto, “Deep Learning in Intrusion Detection Perspective: Overview and Further Challenges,” IEEE Xplore, Vols. 2018-January, pp. 5-10, 2018.
C. Jing, C. Xi, D. Ruiying, H. Li and W. Chiheng, “BotGuard:Lightweight Real-Time Botnet Detection in Software Defined Networks,” Wuhan University Journal of Natural Sciences, vol. 22, no. 2, p. 103-113, 2017.
R. Hadianto and T. W. Purboyo, “A Survey Paper on Botnet Attacks and Defenses in Software Defined Networking,” International Journal of Applied Engineering Research, vol. 13, pp. 483-489, 2018.
Gnanambal S, Thangaraj M, Meenatchi V.T and Gayathri V, “Classification Algorithms with Attribute Selection: an evaluation study using WEKA,” Int. J. Advanced Networking and Applications, vol. 09, no. 06, pp. 3640-3644, 2018.
L. Fausett , Fundamentals of Neural Networks: architectures, algorithms, and applications, United States: Prentice-Hall, Inc, 1994, p. 461.
F. Collet, Deep Learning with Python, Shelter Island: Manning Publications Co, 2018.
P. W. Chi, C.-T. Kuo, H.-M. Ruan, S.-J. Chen and C.-L. Lei, “An AMI Threat DetectionMechanism Based on SDN Networks,” SECURWARE 2014 - 8th International Conference on Emerging Security Information, Systems and Technologies, no. c, pp. 208-211, 2014.
M. Alauthaman, N. Aslam, L. Zhang, R. Alasem and M. A. Hossain, “A P2P Botnet detection scheme based on decision tree,” Neural Computing and Applications, vol. 29, no. 11, pp. 991-1004, 2018.
A. Hussein, L. Chadad, N. Adalian, A. Chehab, I. H. Elhajj and A. Kayssi, “Software-Defined Networking (SDN): the security review,” Journal of Cyber Security Technology, vol. 4, no. 1, pp. 1-66, 2019.
S. C. Su, Y.-R. Chen, S.-C. Tsai and Y.-B. Lin, “Detecting P2P Botnet in Software Defined Networks,” Security and Communication Networks, vol. 2018, pp. 1-13, 2018.
D. S. Rana, S. A. Dhondiyal and S. K. Chamoli, “Software Defined Networking (SDN) Challenges, issues and Solution,” International Journal of Computer Sciences and Engineering, vol. 7, no. 1, pp. 884-889, January 2019.
S. Chen, W. Sun and W. Hu, “On Dynamic Hypervisor Placement in Virtualized Software Defined Networks (vSDNs),” 2020 22nd International Conference on Transparent Optical Networks (ICTON), Vols. 2020-July, pp. 1-5, 2020.
S. Gaonkar, N. Fal Dessai, J. Costa, A. Borkar, S. Aswale and P. Shetgaonkar, “A Survey on Botnet Detection Techniques,” 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp. 1-6, 2020.
G. Vormayr, T. Zseby and J. , “Botnet Communication Patterns,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2768 - 2796, 2017.
P. Prasse, L. Machlica, T. Pevn´y, J. Havelka and T. Scheffer, “Malware Detection by Analysing Encrypted Network Traffic with Neural Networks,” Proceedings - 2017 IEEE Symposium on Security and Privacy Workshops, SPW 2017, Vols. 2017-December, pp. 205-210, 2017.
S. Singaravela, J. Suykensb and P. Geyer, “Deep-learning neural-network architectures and methods: Using componentbased models in building-design energy prediction,” Advanced Engineering Informatics, vol. 38, no. May, pp. 81-90, 2018.
R. Prasad and V. Rohokale, “BOTNET,” in Cyber Security: The Lifeline of Information and Communication Technology, Switzerland, Springer, 2020, pp. 43-65.
Y. Dong, R. Wang and J. He, “Real-Time Network Intrusion Detection System Based on Deep Learning,” Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, vol. 2019, 2019.
W. Li, W. Meng and L. F. Kwok, “A survey on OpenFlow-based Software Defined Networks: Security challenges and countermeasures,” Journal of Network and Computer Applications, vol. 68, pp. 126-139, 2016.

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).