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Intrusion detection in networks using cuckoo search optimization

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Abstract

One of the key problems for researchers and network managers is anomaly detection in network traffic. Anomalies in network traffic might signal a network intrusion, requiring the use of a quick and dependable network intrusion detection system. Intrusion detection systems based on artificial intelligence (AI) techniques are gaining the interest of the research community as AI techniques have evolved in recent years. This research proposes a novel method for anomaly detection using artificial neural networks (ANNs) optimized using cuckoo search algorithm. For simulation purposes, the NSL-KDD dataset has been utilized with a 70:30 ratio where 70% of data is used for training and the remaining 30% is used for testing. The proposed model is then evaluated in terms of mean absolute error, mean square error, root-mean-square error, and accuracy. The results of the proposed work are compared with standard methods available in the literature including fuzzy clustering artificial neural network (FC-ANN), intrusion detection with artificial bee colony, neural network intrusion detection system, and selection of relevant feature. The results clearly show that the proposed method outperforms the listed standard methods.

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References

  • Aghdam MH, Kabiri P et al (2016) Feature selection for intrusion detection system using ant colony optimization. Int J Netw Secur 18(3):420–432

    Google Scholar 

  • Ahmad Z, Shahid Khan A, Wai Shiang C, Abdullah J, Ahmad F (2021) Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans Emerg Telecommun Technol 32(1):4150

    Google Scholar 

  • Alamiedy TA, Anbar M, Alqattan ZN, Alzubi QM (2019) Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm. J Ambient Intell Human Comput 1–22

  • Aldweesh A, Derhab A, Emam AZ (2020) Deep learning approaches for anomaly-based intrusion detection systems: a survey, taxonomy, and open issues. Knowl Based Syst 189:105124

    Article  Google Scholar 

  • Ali MH, Al Mohammed BAD, Ismail A, Zolkipli MF (2018) A new intrusion detection system based on fast learning network and particle swarm optimization. IEEE Access 6:20255–20261

    Article  Google Scholar 

  • Almseidin M, Alzubi M, Kovacs S, Alkasassbeh M (2017) Evaluation of machine learning algorithms for intrusion detection system. In: 2017 IEEE 15th international symposium on intelligent systems and informatics (SISY). IEEE, pp 000277–000282

  • Atefi K, Yahya S, Dak A.Y, Atefi A (2013) A hybrid intrusion detection system based on different machine learning algorithms. In: Proceedings of the 4th international conference on computing and informatics, ICOCI. pp 312–320

  • Axelsson S (2000) Intrusion detection systems: a survey and taxonomy. Technical report, Citeseer

  • Baraneetharan E (2020) Role of machine learning algorithms intrusion detection in WSNs: a survey. J Inf Technol 2(03):161–173

    Google Scholar 

  • Batista LO, de Silva GA, Araújo VS, Araújo VJS, Rezende TS, Guimarães AJ, Souza PVDC (2019) Fuzzy neural networks to create an expert system for detecting attacks by SQL injection. ar**v:1901.02868

  • da Costa KA, Papa JP, Lisboa CO, Munoz R, de Albuquerque VHC (2019) Internet of things: a survey on machine learning-based intrusion detection approaches. Comput Netw 151:147–157

    Article  Google Scholar 

  • Debar H (2000) An introduction to intrusion–detection systems. Proc Connect 2000

  • Debar H, Dacier M, Wespi A (1999) Towards a taxonomy of intrusion–detection systems. Comput Netw 31(8):805–822

    Article  Google Scholar 

  • Farahnakian F, Heikkonen J (2018) A deep auto-encoder based approach for intrusion detection system. In: 2018 20th international conference on advanced communication technology (ICACT). IEEE, pp 178–183

  • Ferdiana R et al (2020) A systematic literature review of intrusion detection system for network security: research trends, datasets and methods. In: 2020 4th international conference on informatics and computational sciences (ICICoS). IEEE, pp 1–6

  • Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  • Gao Y, Wu H, Song B, ** Y, Luo X, Zeng X (2019) A distributed network intrusion detection system for distributed denial of service attacks in vehicular ad hoc network. IEEE Access 7:154560–154571

    Article  Google Scholar 

  • Garcia-Teodoro P, Diaz-Verdejo J, Maciá-Fernández G, Vázquez E (2009) Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Sec 28(1–2):18–28

    Article  Google Scholar 

  • Hubballi N, Suryanarayanan V (2014) False alarm minimization techniques in signature-based intrusion detection systems: a survey. Comput Commun 49:1–17

    Article  Google Scholar 

  • Imran M, Hlavacs H, Khan FA, Jabeen S, Khan FG, Shah S, Alharbi M (2018) Aggregated provenance and its implications in clouds. Future Gener Comput Syst 81:348–358

    Article  Google Scholar 

  • Khan A, Shah R, Imran M, Khan A, Bangash JI, Shah K (2019) An alternative approach to neural network training based on hybrid bio meta-heuristic algorithm. J Ambient Intell Humaniz Comput 10(10):3821–3830

    Article  Google Scholar 

  • Khan FA, Shaheen S, Asif M, Rahman AU, Imran M, Rehman SU (2019) Towards reliable and trustful personal health record systems: a case of cloud-dew architecture based provenance framework. J Ambient Intell Humaniz Comput 10(10):3795–3808

    Article  Google Scholar 

  • Krishnaveni S, Vigneshwar P, Kishore S, Jothi B, Sivamohan S (2020) Anomaly-based intrusion detection system using support vector machine. In: Artificial intelligence and evolutionary computations in engineering systems. Springer, pp 723–731

  • Kumar V, Sangwan OP (2012) Signature based intrusion detection system using snort. Int J Comput Appl Inf Technol 1(3):35–41

    Google Scholar 

  • Maseer ZK, Yusof R, Bahaman N, Mostafa SA, Foozy CFM (2021) Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset. IEEE Access 9:22351–22370

    Article  Google Scholar 

  • Othman SM, Ba-Alwi FM, Alsohybe NT, Al-Hashida AY (2018) Intrusion detection model using machine learning algorithm on big data environment. J Big Data 5(1):1–12

    Article  Google Scholar 

  • Rao KR, Battula SK, Krishna TLSR (2017) A smart heuristic scanner for an intrusion detection system using two-stage machine learning techniques. Int J Adv Intell Paradigms 9(5–6):519–529

    Article  Google Scholar 

  • Saranya T, Sridevi S, Deisy C, Chung TD, Khan MA (2020) Performance analysis of machine learning algorithms in intrusion detection system: a review. Proc Comput Sci 171:1251–1260

    Article  Google Scholar 

  • Singh AP, Singh MD (2014) Analysis of host-based and network-based intrusion detection system. Int J Comput Netw Inf Sec 6(8):41–47

    Google Scholar 

  • Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD cup 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications. IEEE, pp 1–6

  • Wang S-S, Yan K-Q, Wang S-C, Liu C-W (2011) An integrated intrusion detection system for cluster-based wireless sensor networks. Expert Syst Appl 38(12):15234–15243

    Article  Google Scholar 

  • Wang H, Gu J, Wang S (2017) An effective intrusion detection framework based on SVM with feature augmentation. Knowl Based Syst 136:130–139

    Article  Google Scholar 

  • Yang X-S, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  • Zhou AT, Blustein J, Zincir-Heywood N (2004) Improving intrusion detection systems through heuristic evaluation. In: Canadian conference on electrical and computer engineering 2004 (IEEE Cat. No. 04CH37513), vol 3. IEEE, pp 1641–1644

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Acknowledgements

The authors would like to thank Austrian Agency for International Cooperation in Education and Research (OeAD) and Ernst Mach Follow Up Grant Program.

Funding

This research received no funding from any agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan

    Muhammad Imran & Sangeen Khan

  2. Entertainment Computing, Faculty of Computer Science, University of Vienna, Vienna, Austria

    Helmut Hlavacs

  3. Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    Fakhri Alam Khan

  4. Computer Science, IMSciences, Peshawar, Pakistan

    Sajid Anwar

Authors
  1. Muhammad Imran
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  2. Sangeen Khan
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  3. Helmut Hlavacs
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  4. Fakhri Alam Khan
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  5. Sajid Anwar
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Contributions

All authors contributed to the study conception and design. Data curation, methodology, and software were performed by Sangeen Khan, Sajid Anwar, and Fakhri Alam Khan. Formal analysis, investigation, validation, and writing were performed by Muhammad Imran and Helmut Hlavacs. Muhammad Imran, Sangeen Khan, and Fakhri Alam Khan contributed to the design and implementation of the research.

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Correspondence to Muhammad Imran.

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Communicated by Tiancheng Yang.

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S. Khan, H. Hlavacs, F. Alam Khan, S. Anwar contributed equally to this work.

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Imran, M., Khan, S., Hlavacs, H. et al. Intrusion detection in networks using cuckoo search optimization. Soft Comput 26, 10651–10663 (2022). https://doi.org/10.1007/s00500-022-06798-2

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