Cuckoo Optimisation based Intrusion Detection System for Cloud Computing

Full Text (PDF, 707KB), PP.42-49

Views: 0 Downloads: 0


D. Asir Antony Gnana Singh 1,* R. Priyadharshini 1 E. Jebamalar Leavline 1

1. Anna University, BIT-Campus, Tiruchirappalli, Tamil Nadu, India

* Corresponding author.


Received: 4 Sep. 2018 / Revised: 20 Sep. 2018 / Accepted: 16 Oct. 2018 / Published: 8 Nov. 2018

Index Terms

Intrusion detection system, Cloud security, Cloud computing, Feature selection, Machine-learning algorithm


In the digital era, cloud computing plays a significant role in scalable resource sharing to carry out seamless computing and information sharing. Securing the data, resources, applications and infrastructure of the cloud is a challenging task among the researchers. To secure the cloud, cloud security controls are deployed in the cloud computing environment. The cloud security controls are roughly classified as deterrent controls, preventive controls, detective controls and corrective controls. Among these, detective controls are significantly contributing for cloud security by detecting the possible intrusions to prevent the cloud environment from the possible attacks. This detective control mechanism is established using intrusion detection system (IDS). The detecting accuracy of the IDS greatly depends on the network traffic data that is employed to develop the IDS using machine-learning algorithm. Hence, this paper proposed a cuckoo optimisation-based method to preprocess the network traffic data for improving the detection accuracy of the IDS for cloud security. The performance of the proposed algorithm is compared with the existing algorithms, and it is identified that the proposed algorithm performs better than the other algorithms compared.

Cite This Paper

D. Asir Antony Gnana Singh, R. Priyadharshini, E. Jebamalar Leavline, "Cuckoo Optimisation based Intrusion Detection System for Cloud Computing", International Journal of Computer Network and Information Security(IJCNIS), Vol.10, No.11, pp.42-49, 2018. DOI:10.5815/ijcnis.2018.11.05


[1]Krutz RL, Vines RD. Cloud computing security architecture. Cloud security: a comprehensive guide to secure cloud computing. Indianapolis, IN: Wiley; 2010. pp. 179–80. Print.
[2]Tan Z, Nagar UT, He X, Nanda P, Liu RP, Wang S, Hu J. Enhancing big data security with collaborative intrusion detection. IEEE Transactions on Cloud Computing 2014; 1(3):27–33.
[3]Al-Jarrah OY, Alhussein O, Yoo PD, Muhaidat S, Taha K, Kim K. Data randomization and cluster-based partitioning for Botnet intrusion detection. IEEE Transactions on Cybernetics 2016;46(8):1796–806.
[4]Ambusaidi MA, He X, Nanda P, Tan Z. Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Transactions on Computers 2016;65(10):2986–98.
[5]El-Khatib K. Impact of feature reduction on the efficiency of wireless intrusion detection systems. IEEE Transactions on Parallel and Distributed Systems 2010; 21(8):1143–9.
[6]Mishra P, Pilli ES, Varadharajan V, Tupakula U. Intrusion detection techniques in cloud environment: a survey. Journal of Network and Computer Applications 2017; 77(2):18–47.
[7]Viegas E, Santin AO, Fran?a A, Jasinski R, Pedroni VA, Oliveira LS. Towards an energy-efficient anomaly-based intrusion detection engine for embedded systems IEEE Transactions on Computers 2017; 66(1):163–77.
[8]Mistry K, Zhang L, Neoh SC, Lim CP, Fielding B. A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition. IEEE Transactions on Cybernetics 2016; 47(6):1496–509.
[9]Lagrange A, Fauvel M, Grizonnet M. Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images. IEEE Transactions on Computational Imaging 2017; 3(2):230–42.
[10]Kaya GT, Kaya H, Bruzzone L. Feature selection based on high dimensional model representation for hyperspectral images. IEEE Transactions on Image Processing 2017; 26(6):2918–28.
[11]Wang Y, Wang J, Liao H, Chen H. Unsupervised feature selection based on Markov blanket and particle swarm optimization. Journal of Systems Engineering and Electronics 2017; 28(1):151–61.
[12]Ma L, Li M, Gao Y, Chen T, Ma X, Qu L. A novel wrapper approach for feature selection in object-based image classification using polygon-based cross-validation. IEEE Geoscience and Remote Sensing Letters 2017; 14(3):409–13.
[13]Huda S, Yearwood J, Jelinek HF, Hassan MM, Fortino G, Buckland M. A hybrid feature selection with ensemble classification for imbalanced healthcare data: a case study for brain tumor diagnosis. IEEE Access 2016; 4:9145–54.
[14]Nguyen TM, Wu QJ. Online feature selection based on fuzzy clustering and its applications. IEEE Transactions on Fuzzy Systems 2016; 24(6):1294–306.
[15]Abedinia O, Amjady N, Zareipour H. A new feature selection technique for load and price forecast of electrical power systems. IEEE Transactions on Power Systems 2017; 32(1):62–74.
[16]Yang Y, Xu HQ, Gao L, Yuan YB, McLaughlin K, Sezer S. Multidimensional intrusion detection system for IEC 61850-based SCADA networks. IEEE Transactions on Power Delivery 2017; 32(2):1068–78.
[17]Marchang N, Datta R, Das SK. A novel approach for efficient usage of intrusion detection system in mobile Ad Hoc networks. IEEE Transactions on Vehicular Technology 2017; 66(2):1684–95.
[18]Ha T, Yoon S, Risdianto AC, Kim J, Lim H. Suspicious flow forwarding for multiple intrusion detection systems on software-defined networks. IEEE Network 2016; 30(6):22–7.
[19]Zhou C, Huang S, Xiong N, Yang SH, Li H, Qin Y, Li X. Design and analysis of multimodel-based anomaly intrusion detection systems in industrial process automation. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2015; 45(10):1345–60.
[20]Lo CH, Ansari N. Consumer: a novel hybrid intrusion detection system for distribution networks in smart grid. IEEE Transactions on Emerging Topics in Computing 2013; 1(1):33–44.
[21]Durbha SS, King RL, Younan NH, Wrapper-based feature subset selection for rapid image information mining. IEEE Geoscience and Remote Sensing Letters 2010; 7(1):43–7.
[22]Singh DAAG, Leavline EJ, Priyanka R, Priya PP. Dimensionality reduction using genetic algorithm for improving accuracy in medical diagnosis. International Journal of Intelligent Systems and Applications 2016; 8(1):67–73.
[23]Singh DAAG, Leavline EJ, Valliyappan K, Srinivasan M. Enhancing the performance of classifier using particle swarm optimization (PSO)-based dimensionality reduction. International Journal of Energy, Information and Communications 2015; 6:19–26.
[24]Singh DAAG, Leavline EJ, Priyanka V, Swathi V. Agriculture classification system using differential evolution algorithm. International Advanced Research Journal in Science, Engineering and Technology 2016; 3:24–8.
[25]Singh DAAG, Leavline EJ, Nithya T, Nivetha S. Artificial immune system based organizational data prediction. International Journal of Engineering Science 2016; 6:4633–7.
[26]Singh DAAG, Surenther P, Leavline EJ. Ant colony optimization based attribute reduction for disease diagnostic system. International Journal of Applied Engineering Research 2015; 10(55):156–565.
[27]Liao Q, Zhou S, Shi H, Shi W. Parameter estimation of nonlinear systems by dynamic cuckoo search. Neural Computation 2017; 29(4):1103–23.
[28]Jiang M, Luo J, Jiang D, Xiong J, Song H, Shen J. A cuckoo search-support vector machine model for predicting dynamic measurement errors of sensors. IEEE Access 2016; 4:5030–7.
[29]Cheung NJ, Ding XM, Shen HB. A nonhomogeneous cuckoo search algorithm based on quantum mechanism for real parameter optimization. IEEE Transactions on Cybernetics 2017; 47(2):391–402.
[30]Kulshestha G, Agarwal A, Mittal A, Sahoo A. Hybrid cuckoo search algorithm for simultaneous feature and classifier selection. In: IEEE International Conference on Cognitive Computing and Information Processing (CCIP); March 2015. pp. 1–6.
[31]Frank E, Hall MA, Witten IH. The WEKA workbench. In: Kaufmann M, editor. Online appendix for data mining: practical machine learning tools and techniques. 4th ed. 2016.
[32]Alshammari R, Zincir-Heywood AN. An investigation on the identification of VoIP traffic: case study on Gtalk and Skype. In: 6th International Conference on Network and Services Management CNSM 2010). Niagara Falls, Canada, October 2010. pp. 25–9. URL:
[33]Singh G, Antony DA, Leavline EJ. Data mining in network security-techniques and tools: a research perspective. Journal of Theoretical and Applied Information Technology 2013; 57(2):269–78.