Abstract
Cloud computing is the sharing of remote access resources over the Internet. But with this comes an extensive risk of unauthorized access. Hence, for the security and privacy of the data, intrusion detection system (IDS) is required. IDS has to process a huge number of data with dimensions in search of intrusions. The more data it has to scan through, the more time it takes to detect an intrusion. Thus to reduce the dataset, feature selection (FS) is used where redundant data dimensions are discarded. In this paper, authors have proposed a novel Sage Grouse Mating algorithm which is to be implemented for FS in IDS. The proposed model was tested on NSL-KDD and Kyoto2006+ datasets. The proposed model increases the average accuracy of IDS up to 81.729% and reduces the number of features from 41 to 14 on NSL-KDD dataset. So, the experimental outcomes show that the proposed model enhanced the performance of IDS and outperforms all other metaheuristic algorithms compared in this paper. Therefore, it constitutes a robust IDS for Cloud Environment.






Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Kholidy HA, Erradi A, Abdelwahed S, Baiardi F (2016) A risk mitigation approach for autonomous cloud intrusion response system. Computing 98(11):1111–1135
Vaquero LM, Rodero-Merino L, Morán D (2010) Locking the sky: a survey on iaas cloud security. Computing 91(1):93–118
Paul V, Mathew R (2019) Data storage security issues in cloud computing. In: International conference on Computer Networks, Big data and IoT. Springer, pp 177–187
Liao H-J, Lin C-HR, Lin Y-C, Tung K-Y (2013) Intrusion detection system: a comprehensive review. J Netw Comput Appl 36(1):16–24
Manickam M, Rajagopalan S (2019) A hybrid multi-layer intrusion detection system in cloud. Clust Comput 22(2):3961–3969
Ghosh P, Debnath C, Metia D, Dutta R (2014) An efficient hybrid multilevel intrusion detection system in cloud environment. IOSR J Comput Eng 16(4):16–26
Safara F, Souri A, Serrizadeh M (2020) Improved intrusion detection method for communication networks using association rule mining and artificial neural networks. IET Commun 14(7):1192–1197
Eskandari M, Janjua ZH, Vecchio M, Antonelli F (2020) Passban ids: an intelligent anomaly-based intrusion detection system for iot edge devices. IEEE Internet Things J 7(8):6882–6897
Younge AJ, VonLaszewski G, Wang L, Lopez-Alarcon S, Carithers W (2010) Efficient resource management for cloud computing environments. In: International conference on green computing. IEEE, pp 357–364
Zhu K, Song H, Liu L, Gao J, Cheng G (2011) Hybrid genetic algorithm for cloud computing applications. In: IEEE Asia-pacific services computing conference. IEEE, pp 182–187
Xia T, Qu G, Hariri S, Yousif M (2005) An efficient network intrusion detection method based on information theory and genetic algorithm. In: PCCC, 24th IEEE international performance, computing, and communications conference, 2005. IEEE, pp 11–17
Bahrololum M, Salahi E, Khaleghi M (2009) Anomaly intrusion detection design using hybrid of unsupervised and supervised neural network. Int J Comput Netw Commun (IJCNC) 1(2):26–33
Ahmed P, et al (2014) A hybrid-based feature selection approach for ids. In: Networks and communications (NetCom2013). Springer, pp 195–211
MendozaPalechor FE, DeLa HozCorrea EM, DeLa HozManotas AK (2014) Application of feast (feature selection toolbox) in ids (intrusion detection systems)
Malik AJ, Khan FA (2018) A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection. Clust Comput 21(1):667–680
Raman MG, Somu N, Kirthivasan K, Liscano R, Sriram VS (2017) An efficient intrusion detection system based on hypergraph-genetic algorithm for parameter optimization and feature selection in support vector machine. Knowl Based Syst 134:1–12
Kang S-H, Kim KJ (2016) A feature selection approach to find optimal feature subsets for the network intrusion detection system. Clust Comput 19(1):325–333
DeLaHoz E, Ortiz A, Ortega J, Dela Hoz E,(2013) Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques. In: International conference on hybrid artificial intelligence systems. Springer, pp 103–111
Hajisalem V, Babaie S (2018) A hybrid intrusion detection system based on abc-afs algorithm for misuse and anomaly detection. Comput Netw 136:37–50
Alzubi QM, Anbar M, Alqattan ZN, Al-Betar MA, Abdullah R (2019) Intrusion detection system based on a modified binary grey wolf optimisation. In: Neural computing and applications, pp 1–13
Sakr S (2014) Cloud-hosted databases: technologies, challenges and opportunities. Clust Comput 17(2):487–502
Manogaran G, Chilamkurti N, Hsu C-H (2018) Special issue on machine learning algorithms for internet of things, fog computing and cloud computing
Jahner JP, Gibson D, Weitzman CL, Blomberg EJ, Sedinger JS, Parchman TL (2016) Fine-scale genetic structure among greater sage-grouse leks in central nevada. BMC Evol Biol 16(1):1–13
Bird KL, Aldridge CL, Carpenter JE, Paszkowski CA, Boyce MS, Coltman DW (2013) The secret sex lives of sage-grouse: multiple paternity and intraspecific nest parasitism revealed through genetic analysis. Behav Ecol 24(1):29–38
Qiu S, Wang D, Xu G, Kumari S,(2020) Practical and provably secure three-factor authentication protocol based on extended chaotic-maps for mobile lightweight devices. In: IEEE transactions on dependable and secure computing, vol 17, no. 3
Li Z, Wang D, Morais E (2020) Quantum-safe round-optimal password authentication for mobile devices. In: IEEE transactions on dependable and secure computing
Bonneau J, Herley C, VanOorschot PC, Stajano F (2012) The quest to replace passwords: a framework for comparative evaluation of web authentication schemes. In: IEEE symposium on security and privacy. IEEE 2012, pp 553–567
Wang D, Li W, Wang P (2018) Measuring two-factor authentication schemes for real-time data access in industrial wireless sensor networks. IEEE Trans Industr Inf 14(9):4081–4092
Eberz S, Rasmussen KB, Lenders V, Martinovic I (2017) Evaluating behavioral biometrics for continuous authentication: challenges and metrics. In: Proceedings of the 2017 ACM on Asia conference on computer and communications security, pp 386–399
Wang D, Wang P (2016) Two birds with one stone: two-factor authentication with security beyond conventional bound. IEEE Trans Dependable Secure Comput 15(4):708–722
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the kdd cup 99 data set. In: IEEE symposium on computational intelligence for security and defense applications. IEEE, pp 1–6
Ghosh P, Bardhan M, Chowdhury NR, Phadikar S et al (2017) Ids using reinforcement learning automata for preserving security in cloud environment. Int J Inf Syst Model Des (IJISMD) 8(4):21–37
Ibrahim LM, Basheer DT, Mahmod MS (2013) A comparison study for intrusion database (kdd99, nsl-kdd) based on self organization map (som) artificial neural network. J Eng Sci Technol 8(1):107–119
Protić DD (2018) Review of kdd cup’99, nsl-kdd and kyoto 2006+ datasets. Vojnotehnički glasnik 66(3):580–596
Singh R, Kumar H, Singla R (2015) An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Syst Appl 42(22):8609–8624
Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, New York
Jamil M, Yang X-S (2013) A literature survey of benchmark functions for global optimisation problems. Int J Math Model Numer Optim 4(2):150–194
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, vol 4. IEEE, pp 1942–1948
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102
Ghosh P, Mandal AK, Kumar R (2015) An efficient cloud network intrusion detection system. In: Information systems design and intelligent applications. Springer, pp 91–99
Rastegari S, Hingston P, Lam C-P (2015) Evolving statistical rulesets for network intrusion detection. Appl Soft Comput 33:348–359
Aburomman AA, Reaz MBI (2017) A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems. Inf Sci 414:225–246
Mohammadi M, Raahemi B, Akbari A, Nassersharif B (2012) New class-dependent feature transformation for intrusion detection systems. Secur Commun Netw 5(12):1296–1311
Bamakan SMH, Wang H, Yingjie T, Shi Y (2016) An effective intrusion detection framework based on mclp/svm optimized by time-varying chaos particle swarm optimization. Neurocomputing 199:90–102
Abd-Eldayem MM (2014) A proposed http service based ids. Egypt Inf J 15(1):13–24
Kim G, Lee S, Kim S (2014) A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Syst Appl 41(4):1690–1700
Gogoi P, Bhuyan MH, Bhattacharyya D, Kalita JK (2012) Packet and flow based network intrusion dataset. In: International conference on contemporary computing. Springer, pp 322–334
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ghosh, P., Alam, Z., Sharma, R.R. et al. An efficient SGM based IDS in cloud environment. Computing 104, 553–576 (2022). https://doi.org/10.1007/s00607-022-01059-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-022-01059-4
Keywords
- Cloud computing (CC)
- Intrusion detection system (IDS)
- Feature selection (FS)
- Sage grouse mating (SGM)
- NSL-KDD dataset
- Kyoto dataset