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Sathappan, Abirami
- Improving Performance and Efficiency of Software Defined Networking by Identifying Malicious Switches through Deep Learning Model
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Authors
Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, IN
2 Department of Computer Science and Engineering, King Khalid University, Abha, SA
1 Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, IN
2 Department of Computer Science and Engineering, King Khalid University, Abha, SA
Source
International Journal of Computer Networks and Applications, Vol 9, No 1 (2022), Pagination: 72-83Abstract
In recent times, Software Defined Networking (SDN) has developed widely to provide capable solutions for future internet services. As with the solutions, SDN brings us a hazardous rise in malicious threats. We investigated a sort of Distributed Denial of Services (DDoS) assault known as an internet services attack, which evaluates the influence of both traffic flow and throughput depletions in order to characterize the abnormalities. This sort of attack has a significant impact on the whole SDN. This paper introduces a deep learning method to improve the performance efficiency of the SDN by classifying the network switch into either a trusted switch or a malicious switch device. In this research, an attack detection methodology for Internet services utilizing Software Defined Networking (SDN) is proposed. The SDN controller may evaluate traffic flow, detect anomalies, and restrict both incoming and outgoing traffic as well as source nodes. The SDN considers a Convolutional Neural Network (CNN) based attack detection system that can identify malicious node. Kaggle datasets are used to test and train CNN and the features such as packet duration, packet count, byte count, accuracy for identifying the flow of trusted and malicious switches. According to the results, the CNN-based attack detection system can identify the attack with an accuracy of 89 percent. The comparison evaluation with the already proposed LeNet CNN of the feature classification proves that the flow is the trusted one and with the constant throughput with the help of the deep learning model.Keywords
Software Defined Networking (SDN), Kaggle Dataset, Convolutional Neural Networks (CNN), Keras, Internet Service Attack, Malicious Switches, Malicious Node, Distributed Denial of Services.References
- Oliveira, T.F.; Xavier-de-Souza, S.; Silveira, L.F. Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers. Energies, 14, 3161, 2021.
- Mohsin Masood, Mohamed Mostafa Fouad, Saleh Seyedzadeh and Ivan Glesk, “Energy Efficient Software Defined Networking Algorithm for Wireless Sensor Networks”, 13th International Scientific Conference on Sustainable, Modern and Safe Transport, 2019.
- Madhukrishna Priyadarsini, Padmalochan Bera, and Mohammad Ashiqur Rahman, “A New Approach for Energy Efficiency in Software Defined Network”, Fifth International Conference on Software Defined Systems (SDS), 2018.
- Thangaraj Ethilu, Abirami Sathappan, Paul Rodrigues, "Modified Deep Learning Methodology Based Malicious Intrusion Detection System in Software Defined Networking", International Journal of Computer Networks and Applications (IJCNA), 8(4), PP: 381-389, 2021, DOI: 10.22247/ijcna/2021/209704.
- Danda B. Rawat and Swetha R. Reddy, Software Defined Networking Architecture, Security and Energy Efficiency: A Survey,IEEE communication surveys and tutorials,2019.
- W. Meng, W. Li, Y. Xiang and K.-K.R. Choo. A Bayesian Inferencebased Detection Mechanism to Defend Medical Smartphone Networks Against Insider Attacks. Journal of Network and Computer Applications, vol. 78, pp. 162-169, Elsevier, 2017.
- Rinki Gupta, Sreeraman Rajan, “Comparative Analysis of Convolution Neural Network Models for Continuous Indian Sign Language Classification”, Procedia Computer Science 171 (2020) 1542–1550.
- P. -W. Chi, M. -H. Wang and Y. Zheng, "SandboxNet: An Online Malicious SDN Application Detection Framework for SDN Networking," 2020 International Computer Symposium (ICS), 2020, pp. 397-402.
- Sebbar, A., ZKIK, K., Baddi, Y. MitM detection and defense mechanism CBNA-RF based on machine learning for large-scale SDN context. J Ambient Intell Human Comput 11, 5875–5894 (2020).
- Nife, F.N., Kotulski, Z. Application-Aware Firewall Mechanism for Software Defined Networks. J Netw Syst Manage 28, 605–626 (2020).
- Neu C. V., Tatsch C. G., Lunardi R. C., Michelin R. A., Orozco A. M. S.,and Zorzo A. F.: Lightweight IPS for port scan in OpenFlow SDN networks. In NOMS 2018 IEEE/IFIP Network Operations and Manag. Symposium, Taipei, Taiwan, pp. 1–6, (2018).
- H. Naeem, B. Guo, and M. R. Naeem, ‘‘A light-weight malware static visual analysis for IoT infrastructure,’’ in Proc. Int. Conf. Artif. Intell. Big Data (ICAIBD), May 2018, pp. 240–244.
- H. Zhang, X. Xiao, F. Mercaldo, S. Ni, F. Martinelli, and A. K. Sangaiah, ‘‘Classification of ransomware families with machine learning based on N-Gram of opcodes,’’ Future Gener. Comput. Syst., vol. 90, pp. 211–221, Jan. 2019.
- A. Khalilian, A. Nourazar, M. Vahidi-Asl, and H. Haghighi, ‘‘G3MD: Mining frequent opcode sub-graphs for metamorphic malware detection of existing families,’’ Expert Syst. Appl., vol. 112, pp. 15–33, Dec. 2018.
- Y.-S. Liu, Y.-K. Lai, Z.-H. Wang, and H.-B. Yan, ‘‘A new learning approach to malware classification using discriminative feature extraction,’’ IEEE Access, vol. 7, pp. 13015–13023, 2019.
- Chang Y., and Lin T.: Cloud-clustered firewall with distributed SDN devices. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, pp. 1–5. (2018).
- J. Yan, Y. Qi, and Q. Rao, ‘‘Detecting malware with an ensemble method based on deep neural network,’’ Secur. Commun. Netw., vol. 2018, pp. 1–16, Mar. 2018.
- D. Gibert, C. Mateu, J. Planes, and R. Vicens, ‘‘Classification of malware by using structural entropy on convolutional neural networks,’’ in Proc. 32nd AAAI Conf. Artif. Intell., (AAAI), 30th Innov. Appl. Artif. Intell. (IAAI), 8th AAAI Symp. Educ. Adv. Artif. Intell. (EAAI), New Orleans, LA, USA, 2018, pp. 7759–7764.
- Z. Ma, L. Liu, W. Meng. Towards Multiple-Mix-Attack Detection via Consensus-based Trust Management in IoT Networks. Computers & Security, In press (2020).
- Y. Meng. The practice on using machine learning for network anomaly intrusion detection. The 2011 International Conference on Machine Learning and Cybernetics (ICMLC 2011), IEEE, pp. 576-581, 2011.
- Andrzej Kamisiński, Carol Fung,” FlowMon: Detecting Malicious Switches in Software-Defined Networks”, ACM CCS workshop on Automated Decision Making for Active Cyber Defense ,2015.
- Lis, A.; Sudolska, A.; Pietryka, I.; Kozakiewicz, A. Cloud Computing and Energy Efficiency: Mapping the Thematic Structure of Research. Energies 2020, 13, 4117.
- Aujla, G.S.; Kumar, N.; Zomaya, A.Y.; Ranjan, R. Optimal Decision Making for Big Data Processing at Edge-Cloud Environment: An SDN Perspective. IEEE Trans. Ind. Inform. 2018, 14, 778–789.
- Xu, G.; Dai, B.; Huang, B.; Yang, J.; Wen, S. Bandwidth-aware energy efficient flow scheduling with SDN in data center networks. Future Gener. Comput. Syst. 2017, 68, 163–174.
- Fernández-Fernández, A.; Cervelló-Pastor, C.; Ochoa-Aday, L. Energy Efficiency and Network Performance: A Reality Check in SDN-Based 5G Systems. Energies 2017.
- Son, J.; Dastjerdi, A.V.; Calheiros, R.N.; Buyya, R. SLA-Aware and Energy-Efficient Dynamic Overbooking in SDN-Based Cloud Data Centers. IEEE Trans. Sustain. Comput. 2017, 2, 76–89.
- Modified Deep Learning Methodology Based Malicious Intrusion Detection System in Software Defined Networking
Abstract Views :376 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, IN
2 Department of Computer Science and Engineering, DMI College of Engineering, Chennai, Tamil Nadu, IN
1 Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, IN
2 Department of Computer Science and Engineering, DMI College of Engineering, Chennai, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 8, No 4 (2021), Pagination: 381-389Abstract
Software Defined Networking (SDN) has increased a high-level attention in recent years, mainly because of its ability to address the cyber security challenges. Machine learning architectures were developed as the SDN system to detect the security threads; however, present techniques are limited with (i) higher computation time during malicious switch detection, (ii) reduced malicious switch detection rate (MSDR). This paper presents modified deep learning architecture based SDN system consist of two stages: (i) training stage, computes the external feature maps from both trusted and malicious network switches connected to the SDN controller, (ii) testing stage, classifying the trust and malicious switches connected with SDN controller. The feature maps are trained and classified with Modified LeNET Convolutional Neural Networks (CNN) architecture. The proposed methodology is simulated via network simulator under environmental constraint conditions. The results shows that the proposed methodology reduced the malicious switch detection computational time about a half as well as it increased the MSDR to about 6% compared to the conventional methodologies.Keywords
SDN, Switch, Malicious, CNN, Feature Maps.References
- X.-F. Chen, and S.Z.Yu, “CIPA: Collaborative intrusion prevention architecture for programmable network and SDN,”Comput. Secur. Vol. 58, No.1, 2016, pp. 1-19.
- T. Das, V. Sridharan, and M. Gurusamy, “A survey on controller placement in sdn. ieee communications surveys and tutorials,” Vol. 22, No. 1, 2020, pp. 472-503.
- E. Vasilomanolakis, S. Karuppayah, M. Muhlhauser, and Mathias Fischer, “Taxonomy and survey of collaborative intrusion detection,” ACM Comput. Surv. Vol. 47, No. 4, 2015, pp. 1-10.
- C.J. Fung and R. Boutaba, “Design and management of collaborative intrusion detection networks,” IFIP/IEEE International Symposium on Integrated Network Management (IM), Vol.1, No.1, 2013, pp. 955-961.
- S. Hameed, and H.A. Khan, “SDN based collaborative scheme for mitigation of DDOS attacks,” Future Internet, Vol. 10, No. 3, 2018, pp. 281-288.
- Z. Ma, L. Liu, and W. Meng, “Towards multiple-mix-attack detection via consensus-based trust management in IOT networks,” Comput. Secur., Vol.1, N0.1, 2020, pp. 12-17.
- Y. Meng, “The practice on using machine learning for network anomaly intrusion detection,” IEEE International Conference on Machine Learning and Cybernetics, Vol.1, No.1, 2011, pp. 576-581.
- W. Meng, W. Li, Y. Xiang and K.-K.R. Choo., “A bayesian inference-based detection mechanism to defend medical smartphone networks against insider attacks,” Journal of Network and Computer Applications, Vol. 78, No.3, 2017, pp. 162-169.
- R. Gupta, and S. Rajan, “Comparative analysis of convolution neural network models for continuous Indian sign language classification”, Procedia Computer Science, Vol. 171, No.2, 2020, 1542–1550.
- D. Chasaki and C. Mansour, “Detecting malicious hosts in SDN through system call learning,” IEEE Conference on Computer Communications Workshops, 2021, pp. 1-6.
- M. Amanowicz and D. Jankowski, “Detection and classification of malicious flows in software-defined networks using data mining techniques,” Sensors, Vol.21, No.1, 2021, pp.1-24.
- A. Derhab, M. Guerroumi, M. Belaoued, and O. Cheikhrouhou, “BMC-SDN: blockchain-based multi controller architecture for secure software-defined networks,” Wireless Communications and Mobile Computing, Vol. 2021, No. 9984666, 2021, pp.1-15.
- F.N. Nife, and Z. Kotulski, “Application-aware firewall mechanism for software defined networks,” J. Network Syst Manage, Vol. 28, No.1,2020, pp. 605–626.
- A. Sebbar, K. ZKIK, and Y. Baddi, “MitM detection and defense mechanism CBNA-RF based on machine learning for large-scale SDN context,” Journal of Ambient Intell Human Comput, Vol.11, No.7, 2020, pp. 5875–5894.
- P. W. Chi, M. H. Wang, and Y. Zheng, "Sandbox Net: An online malicious SDN application detection framework for SDN networking," International Computer Symposium (ICS), Vol.1, No.1, 2020, pp. 397-402.
- C.V. Neu, C. Tatsch, R.C. Lunardi, R.A. Michelin, A.M. Orozco, and A.F. Zorzo, “Lightweight IPS for port scan in open flow SDN networks,” IEEE/IFIP Network Operations and Manag. Symposium, Taipei, Taiwan, 2018, pp. 1–6.
- Y. Chang, and T. Lin, “Cloud-clustered firewall with distributed SDN devices,” IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Vol.1, No.1, 2018, pp. 1–5, 2018.