Skip to main content
Springer Nature Link
Log in

Intrusion defense: Leveraging ant colony optimization for enhanced multi-optimization in network security

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

The security of a data is the data is the prime challenge in the current scenario. To protect our data from unauthorized access we need a system that analyses traffic to identify network attacks, as well as alert the user by generating an alarm. In recent times numerous intrusion detection models have been built to detect intrusion activity. In this paper, we proposed the most effective hybrid Intrusion detection system (IDS) based on association rule mining (ARM) and ant colony optimization (ACO) for the identification of the attacks. The standard NSL-KDD dataset and CICIoT2023 dataset is considered for evaluating the performance of the proposed hybrid approach. The novel ARM-ACO hybrid model outperforms in detection accuracy as well as in the very low false alarm rate. The major advancement in the proposed model is that it also considered the categorical data which is not evaluated or considered in the previous methodology on unbiased selection of a dataset. As compared to earlier methodologies, our results were superior in terms of categorization as well as accuracy. In NSL-KDD dataset the accuracy of the proposed model is 99.80%, with a very low false alarm rate and in CICIoT2023 dataset the accuracy is 99.93% which is much better than the previous approaches. The proposed ARM-ACO model clearly shows its outperformance in terms of accuracy as well as low false alarm rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The data and material of the manuscript is available.

Code availability

The code is available in GitHub.

References

  1. Manimurugan S, Majdi A-Q, Mustaffa Mohmmed C, Narmatha RV (2020) Intrusion detection in networks using crow search optimization algorithm with adaptive neuro-fuzzy inference system. Microprocess Microsyst. https://doi.org/10.1016/j.micpro.2020.103261

    Article  Google Scholar 

  2. Mhawi DN, Hashim SH (2023) Proposed Hybrid Ensemble Learning Algorithms for an Efficient Intrusion Detection System. Iraqi Journal of Computers, Communications, Control & Systems Engineering (IJCCCE). 22(2). https://doi.org/10.33103/uot.ijccce.22.2.7.

  3. Ramesh Kumar M, Sudhakaran P (2024) Securing IoT networks: A robust intrusion detection system leveraging feature selection and LGBM. Peer-to-Peer Networking Applications 17:2921–2943. https://doi.org/10.1007/s12083-024-01721-y

    Article  MATH  Google Scholar 

  4. Qasem AA, Qutqut MH, Alhaj F et al (2024) SRFE: A stepwise recursive feature elimination approach for network intrusion detection systems. Peer-to-Peer Networking Appl. https://doi.org/10.1007/s12083-024-01763-2

    Article  Google Scholar 

  5. Nandhini U, SVN, S. (2024) An improved Harris Hawks optimizer based feature selection technique with effective two-staged classifier for network intrusion detection system. Peer-to-Peer Networking Appl 17:2944–2978. https://doi.org/10.1007/s12083-024-01727-6

    Article  MATH  Google Scholar 

  6. Mashuqur Rahman Mazumder AKM, Mohammed Kamruzzaman N, Akter N, Arbe N, Rahman MM. (2021) Network intrusion detection using hybrid machine learning model. International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). https://doi.org/10.1109/icaect49130.2021.9392483.

  7. Narayanasami S, Sengan S, Khurram S, Arslan F, Murugaiyan SK, Rajan R, Peroumal V, Dubey AK, Srinivasan S, Sharma DK (2021) Biological feature selection and classification techniques for intrusion detection on BAT. Wireless Pers Commun. https://doi.org/10.1007/s11277-021-08721-8

    Article  Google Scholar 

  8. Ramaiah M, Chandrasekaran V, Ravi V, Kumar N (2021) An intrusion detection system using optimized deep neural network architecture. Transactions Emerg Telecommun Technol. 32(4). https://doi.org/10.1002/ett.4221

  9. Mighan SN, Kahani M (2020) A novel scalable intrusion detection system based on deep learning. Int J Inf Secur 20(3):387–403. https://doi.org/10.1007/s10207-020-00508-5

    Article  MATH  Google Scholar 

  10. 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. https://doi.org/10.1109/access.2021.3056614

    Article  Google Scholar 

  11. Selvakumar B, Muneeswaran K (2019) Firefly algorithm based feature selection for network intrusion detection. Computers Secur 81:148–155. https://doi.org/10.1016/j.cose.2018.11.005

    Article  MATH  Google Scholar 

  12. Subba B (2019) A neural network based NIDS framework for intrusion detection in contemporary network traffic. IEEE Int Conference Adv Networks Telecommun Systems (ANTS). https://doi.org/10.1109/ants47819.2019.9117966

    Article  MATH  Google Scholar 

  13. Taher KA, Mohammed Yasin Jisan B, Rahman MM (2019) Network intrusion detection using supervised machine learning technique with feature selection. Int Confer Robotics, Electrical Signal Processing Techniques (ICREST). https://doi.org/10.1109/icrest.2019.8644161.

  14. Shao Y, Liu B, Li G, Wang S (2017) Software defect prediction based on class-association rules. Second Int Conference Reliability Systems Eng (ICRSE). https://doi.org/10.1109/icrse.2017.8030774

    Article  MATH  Google Scholar 

  15. Devan P, Khare N (2020) An efficient XGBoost–DNN-based classification model for network intrusion detection system. Neural Comput Appl 32(16):12499–12514. https://doi.org/10.1007/s00521-020-04708-x

    Article  MATH  Google Scholar 

  16. Rajadurai H, Gandhi UD (2020) A stacked ensemble learning model for intrusion detection in wireless network. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04986-5

    Article  MATH  Google Scholar 

  17. Wei W, Chen S, Lin Q, Ji J, Chen J (2020) A multi-objective immune algorithm for intrusion feature selection. Appl Soft Comput 95:106522. https://doi.org/10.1016/j.asoc.2020.106522

    Article  MATH  Google Scholar 

  18. Baek S, Kwon D, Suh SC, Kim H, Kim I, Kim J (2021) Clustering-based label estimation for network anomaly detection. Digital Commun Networks 7(1):37–44. https://doi.org/10.1016/j.dcan.2020.06.001

    Article  MATH  Google Scholar 

  19. Kushwah GS, Ranga V (2020) Voting extreme learning machine based distributed denial of service attack detection in cloud computing. J Inform Security Appl 53:102532. https://doi.org/10.1016/j.jisa.2020.102532

    Article  MATH  Google Scholar 

  20. Choudhary S, Kesswani N (2020) Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 datasets using deep learning in IoT. Procedia Computer Science 167:1561–1573. https://doi.org/10.1016/j.procs.2020.03.367

    Article  Google Scholar 

  21. Almiani M, AbuGhazleh A, Al-Rahayfeh A, Atiewi S, Razaque A (2020) Deep recurrent neural network for IoT intrusion detection system. Simulation Modeling Practice Theory 101:102031. https://doi.org/10.1016/j.simpat.2019.102031

    Article  Google Scholar 

  22. Singh S, Banerjee S (2020) Machine learning mechanisms for network anomaly detection system: A review. Int Conference Commun Signal Processing (ICCSP). https://doi.org/10.1109/iccsp48568.2020.9182197

    Article  MATH  Google Scholar 

  23. Abrar I, Ayub Z, Masoodi F, Bamhdi AM (2020) A machine learning approach for intrusion detection system on NSL-KDD dataset. Int Conference Smart Electronics Commun (ICOSEC). https://doi.org/10.1109/icosec49089.2020.9215232

    Article  Google Scholar 

  24. Su T, Sun H, Zhu J, Wang S, Li Y (2020) BAT: deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE Access 8:29575–29585. https://doi.org/10.1109/access.2020.2972627

    Article  MATH  Google Scholar 

  25. Almasoudy FH, Al-Yaseen WL, Idrees AK (2020) Differential evolution wrapper feature selection for intrusion detection system. Procedia Computer Sci 167:1230–1239. https://doi.org/10.1016/j.procs.2020.03.438

    Article  MATH  Google Scholar 

  26. Kunal, Dua M (2020). Attribute selection and ensemble classifier based novel approach to intrusion detection system. Procedia Computer Sci. 167:2191–2199https://doi.org/10.1016/j.procs.2020.03.271

  27. Yu Y, Bian N (2020) An intrusion detection method using few-shot learning. IEEE Access 8:49730–49740. https://doi.org/10.1109/access.2020.2980136

    Article  MATH  Google Scholar 

  28. Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. Computational Intell Security. https://doi.org/10.1109/cisda.2009.5356528

    Article  MATH  Google Scholar 

  29. Behal S, Kumar K (2016) Trends in validation of DDoS research. Procardia Computer Sci 85:7–15. https://doi.org/10.1016/j.procs.2016.05.170

    Article  MATH  Google Scholar 

  30. Khraisat A, Gondal I, Vamplew P, Kamruzzaman J (2019) Survey of intrusion detection systems techniques, datasets and challenges. Cyber Security. 2(1). https://doi.org/10.1186/s42400-019-0038-7.

  31. Balasaraswathi VR, Sugumaran M, Hamid Y (2017) Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms. J Commun Inform Networks 2(4):107–119. https://doi.org/10.1007/s41650-017-0033-7

    Article  Google Scholar 

  32. Tsai C, Hsu YF, Lin C, Lin W (2009) Intrusion detection by machine learning: A review. Expert Syst Appl 36(10):11994–12000. https://doi.org/10.1016/j.eswa.2009.05.029

    Article  MATH  Google Scholar 

  33. Behal S, Kumar K (2016) Trends in validation of DDoS research. Procedia Computer Sci 85:7–15. https://doi.org/10.1016/j.procs.2016.05.170

    Article  MATH  Google Scholar 

  34. Ibrahim ZK, Thanon MY (2021) Performance comparison of intrusion detection system using three different machine learning algorithms. 6th International Conference on Inventive Computation Technologies (ICICT). 1116–1124, https://doi.org/10.1109/ICICT50816.2021.9358775.

  35. Parimala G, Kayalvizhi R (2021). An effective intrusion detection system for securing IoT using feature selection and deep learning. Int Conference Comp Commun Informatics (ICCCI). 1–4. https://doi.org/10.1109/ICCCI50826.2021.9402562.

  36. Stiawan D et al (2021) An approach for optimizing ensemble intrusion detection systems. IEEE Access 9:6930–6947. https://doi.org/10.1109/ACCESS.2020.3046246

    Article  MATH  Google Scholar 

  37. Li L-H, Ahmad R, Tsai W-C, Sharma AK (2021) A feature selection based DNN for intrusion detection system. 15th International Conference on Ubiquitous Information Management and Communication (IMCOM). 1–8, https://doi.org/10.1109/IMCOM51814.2021.9377405.

  38. Ibrahim ZK, Thanon MY (2021) Performance comparison of intrusion detection system using three different machine learning algorithms. International Conference on Inventive Computation Technologies (ICICT). 1116–1124. https://doi.org/10.1109/ICICT50816.2021.9358775.

  39. Saber A, Abbas M, Fergani B (2021) Two-dimensional intrusion detection system: a new feature selection technique. 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), 69–74. https://doi.org/10.1109/IHSH51661.2021.9378721.

  40. Sharma M, Sharma M, Elmiligi H, Gebali F (2021) A novel intrusion detection system for RPL-based cyber-physical systems. IEEE Canadian J Elec Comp Eng. https://doi.org/10.1109/ICJECE.2021.3053231

    Article  MATH  Google Scholar 

  41. Ahmad HamdiQaiwmchi N, Amintoosi H, Mohajerzadeh A (2021) Intrusion detection system based on gradient corrected online sequential extreme learning machine. IEEE Access 9:4983–4999. https://doi.org/10.1109/ACCESS.2020.3047933

    Article  Google Scholar 

  42. Jiang L, Kong G, Li C (2021) Wrapper framework for test-cost-sensitive feature selection. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(3):1747–1756. https://doi.org/10.1109/TSMC.2019.2904662

    Article  MATH  Google Scholar 

  43. Lifandali O, Abghour N, Chiba Z (2023) Feature selection using a combination of ant colony optimization and random forest algorithms applied to isolation forest based intrusion detection system. Procedia Computer Science 220:796–805. https://doi.org/10.1016/j.procs.2023.03.106

    Article  Google Scholar 

  44. Alsarhan A, Alauthman M, Alshdaifat E, Al-Ghuwairi AR, Al-Dubai A (2021) Machine learning-driven optimization for svm-based intrusion detection system in vehicular ad hoc networks. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-02963-x

    Article  Google Scholar 

  45. Oleiwi HW, Mhawi DN, Al-Raweshidy H (2022). MLTs-ADCNs: Machine Learning Techniques for Anomaly Detection in Communication Networks. In IEEE Access, 10:91006–91017. https://doi.org/10.1109/ACCESS.2022.3201869.

  46. Mhawi DN, Aldallal A, Hassan S (2022) Advanced Feature-Selection-Based Hybrid Ensemble Learning Algorithms for Network Intrusion Detection Systems. Symmetry 14(7):1461. https://doi.org/10.3390/sym14071461

    Article  MATH  Google Scholar 

  47. Kocher G, Kumar G (2021) Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges. Soft Comput 25:9731–9763. https://doi.org/10.1007/s00500-021-05893-0

    Article  MATH  Google Scholar 

  48. Lo W, Alqahtani H, Thakur K, Almadhor A, Chander S, Kumar G (2022) A hybrid deep learning based intrusion detection system using spatial-temporal representation of in-vehicle network traffic, Vehicular Communications, Volume 35. ISSN 100471:2214–2096. https://doi.org/10.1016/j.vehcom.2022.100471

    Article  Google Scholar 

  49. Gulshan Kumar, Hamed Alqahtani (2022). Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions, CMES - Computer Modeling in Engineering and Sciences. 134(1):89–119, ISSN 1526–1492, https://doi.org/10.32604/cmes.2022.020724

  50. Kumar G, Kumar K, Sachdeva M (2010) The use of artificial intelligence based techniques for intrusion detection: a review. Artif Intell Rev 34:369–387. https://doi.org/10.1007/s10462-010-9179-5

    Article  MATH  Google Scholar 

  51. Kumar G, Kumar K (2011) An information theoretic approach for feature selection. Security Commun Networks J 5:178–185. https://doi.org/10.1002/sec.303

    Article  MATH  Google Scholar 

  52. Kumar G, Kumar K (2013) Design of an Evolutionary Approach for Intrusion Detection. Scientific World J. https://doi.org/10.1155/2013/962185

    Article  MATH  Google Scholar 

  53. Dwivedi S, Vardhan M, Tripathi S (2021) Building an Efficient Intrusion detection system using grasshopper optimization algorithm for anomaly detection. Clust Comput 24(3):1881–1900. https://doi.org/10.1007/s10586-020-03229-5

    Article  MATH  Google Scholar 

  54. Sumaiya Thaseen I, Saira Banu J, Lavanya K, Rukunuddin Ghalib M, Abhishek K (2020) An integrated intrusion detection system using correlation based attribute selection and artificial neural network. Transact Emerg Telecommun Technol. 32(2). https://doi.org/10.1002/ett.4014

  55. Ahmad Z, Shahid Khan A, Wai Shiang C, Abdullah J, Ahmad F (2020) Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transact Emerg Telecommun Technol. 32(1), https://doi.org/10.1002/ett.4150.

  56. Dubey S, Gupta C (2024) An Effective Model for Binary and Multi-classification Based on RFE and XGBoost Methods in Intrusion Detection System. Cyber Security and Digital Forensics. Lecture Notes in Networks and Systems, vol. 896. Springer, Singapore. https://doi.org/10.1007/978-981-99-9811-1_3.

  57. Gupta C, Kumar A, Jain NK (2024) An Enhanced Hybrid Intrusion Detection Based on Crow Search Analysis Optimizations and Artificial Neural Network. Wireless Personal Commun 134:43–68. https://doi.org/10.1007/s11277-024-10880-3

    Article  MATH  Google Scholar 

  58. Gupta C, Kumar A, Jain NK (2023) A Detailed Analysis on Intrusion Detection Systems, Datasets, and Challenges. Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_26.

  59. Gheni HQ, Al-Yaseen WL (2024) Two-step data clustering for improved intrusion detection system using CICIoT2023 dataset, e-Prime - Advances in Electrical Engineering, Electronics and Energy, Volume 9. ISSN 100673:2772–6711. https://doi.org/10.1016/j.prime.2024.100673

    Article  MATH  Google Scholar 

  60. Neto ECP, Dadkhah S, Ferreira R, Zohourian A, Lu R, Ghorbani AA (2023) CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment. Sensors 23(13):5941. https://doi.org/10.3390/s23135941

    Article  Google Scholar 

Download references

Funding

For conducting this study No funding was received.

Author information

Author notes

  1. Amit Kumar and Neelesh Kumar Jain contributed equally.

Authors and Affiliations

  1. Computer Science & Engineering, JUET, Guna, 473226, MP, India

    Chetan Gupta, Amit Kumar & Neelesh Kumar Jain

Authors
  1. Chetan Gupta
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Amit Kumar
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Neelesh Kumar Jain
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

C.G. developed the theoretical formalism, performed the analytic calculations, conducted the numerical simulations, designed the model and computational framework, and analyzed the data. A.K. helped supervise the project, provided critical feedback, and contributed to shaping the research, analysis, and manuscript. N.K.J. conceived the study, oversaw the overall direction and planning, ensured the flow of the paper, and performed professional and comprehensive proofreading. All authors reviewed the manuscript.

Corresponding author

Correspondence to Chetan Gupta.

Ethics declarations

Conflicts of interest

There is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Tropical Collection: Track on Security and Privacy

Guest Editor: Rongxing Lu

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, C., Kumar, A. & Jain, N.K. Intrusion defense: Leveraging ant colony optimization for enhanced multi-optimization in network security. Peer-to-Peer Netw. Appl. 18, 98 (2025). https://doi.org/10.1007/s12083-025-01911-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12083-025-01911-2

Keywords