Skip to main content
SpringerLink
Log in

Towards an efficient model for network intrusion detection system (IDS): systematic literature review

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

With the recent rise in internet usage, the volume of crucial, private, and confidential data traveling online has increased. Attackers have made attempts to break into the network due to weaknesses in the security mechanisms, gaining access to sensitive data that could disrupt the operation of the systems and jeopardize the confidentiality of the data. Intrusion detection systems, a key component of cybersecurity, were used to defend against these potential threats. Numerous review papers have examined various intrusion detection system (IDS) approaches for networks, many of which focused primarily on the anomaly-based IDS while paying less attention to the signature and hybrid-based approaches. Additionally, a lot of these studies took a non-systematic approach, comparing existing techniques, without taking into account the in-depth analytical synthesis of methodologies and results of the approaches to provide a thorough grasp of state-of-the-art. In order to provide a thorough assessment of the current status of Network IDS, this work provides an in-depth insight into what is attainable in the research of Anomaly, Signature, and Hybrid-Based methods by adhering to the principles of Preferred Reporting Items for Systematic Reviews and Meta-Analyses and guidelines for Software Engineering Domain. We discovered unexplored study topics and unsolved research issues based on the study's findings. We end by highlighting potential, high impact future research areas in order to improve the IDS model.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Data availability

Not Applicable.

References

  1. Ashiku, L., & Dagli, C. (2021). Network intrusion detection system using deep learning. Procedia Computer Science, 185, 239–247. https://doi.org/10.1016/j.procs.2021.05.025

    Article  Google Scholar 

  2. Gupta, N., Jindal, V., & Bedi, P. (2021). LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system. Computer Networks, 192, 108076. https://doi.org/10.1016/j.comnet.2021.108076

    Article  Google Scholar 

  3. Nguyen, M. T., & Kim, K. (2020). Genetic convolutional neural network for intrusion detection systems. Future Generation Computer Systems, 113, 418–427. https://doi.org/10.1016/j.future.2020.07.042

    Article  Google Scholar 

  4. Wu, Z., Wang, J., Hu, L., Zhang, Z., & Wu, H. (2020). A network intrusion detection method based on semantic re-encoding and deep learning. Journal of Network and Computer Applications, 164, 102688. https://doi.org/10.1016/j.jnca.2020.102688

    Article  Google Scholar 

  5. Kim, J., Kim, J., Kim, H., Shim, M., & Choi, E. (2020). CNN-based network intrusion detection against denial-of-service attacks. Electronics. https://doi.org/10.3390/electronics9060916

    Article  Google Scholar 

  6. Xiao, Y., Xing, C., Zhang, T., & Zhao, Z. (2019). An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access, 7, 42210–42219. https://doi.org/10.1109/ACCESS.2019.2904620

    Article  Google Scholar 

  7. Onah, J. O., Abdullahi, M., Hassan, I. H., & Al-Ghusham, A. (2021). Genetic algorithm based feature selection and naïve Bayes for anomaly detection in fog computing environment. Machine Learning with Applications, 6, 100156. https://doi.org/10.1016/j.mlwa.2021.100156

    Article  Google Scholar 

  8. Gu, J., & Lu, S. (2021). An effective intrusion detection approach using SVM with naïve Bayes feature embedding. Computers and Security, 103, 102158. https://doi.org/10.1016/j.cose.2020.102158

    Article  Google Scholar 

  9. Kanimozhi, V., & Jacob, T. P. (2021). Artificial intelligence outflanks all other machine learning classifiers in network intrusion detection system on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing. ICT Express, 7(3), 366–370. https://doi.org/10.1016/j.icte.2020.12.004

    Article  Google Scholar 

  10. Kanimozhi, V., & Jacob, T. P. (2019). Artificial intelligence based network intrusion detection with hyper-parameter optimization tuning on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing. ICT Express, 5(3), 211–214. https://doi.org/10.1016/j.icte.2019.03.003

    Article  Google Scholar 

  11. Mebawondu, J. O., Alowolodu, O. D., Mebawondu, J. O., & Adetunmbi, A. O. (2020). Network intrusion detection system using supervised learning paradigm. Scientific African, 9, e00497. https://doi.org/10.1016/j.sciaf.2020.e00497

    Article  Google Scholar 

  12. Jia, H., Liu, J., Zhang, M., He, X., & Sun, W. (2021). Network intrusion detection based on IE-DBN model. Computer Communications, 178, 131–140. https://doi.org/10.1016/j.comcom.2021.07.016

    Article  Google Scholar 

  13. Elmasry, W., Akbulut, A., & Zaim, A. H. (2020). Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Computer Networks, 168, 107042. https://doi.org/10.1016/j.comnet.2019.107042

    Article  Google Scholar 

  14. Wang, Z., Liu, Y., He, D., & Chan, S. (2021). Intrusion detection methods based on integrated deep learning model. Computers and Security, 103, 102177. https://doi.org/10.1016/j.cose.2021.102177

    Article  Google Scholar 

  15. Ahmed, H. A., Hameed, A., & Bawany, N. Z. (2022). Network intrusion detection using oversampling technique and machine learning algorithms. PeerJ Computer Science, 8, 820. https://doi.org/10.7717/peerj-cs.820

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Disha, R. A., & Waheed, S. (2022). Performance analysis of machine learning models for intrusion detection system using gini impurity-based weighted random forest (GIWRF) feature selection technique. Cybersecurity, 5(1), 1. https://doi.org/10.1186/s42400-021-00103-8

    Article  Google Scholar 

  18. Sharma, N. V., & Yadav, N. S. (2021). An optimal intrusion detection system using recursive feature elimination and ensemble of classifiers. Microprocessors and Microsystems, 85, 104293. https://doi.org/10.1016/j.micpro.2021.104293

    Article  Google Scholar 

  19. Gao, X., Shan, C., Hu, C., Niu, Z., & Liu, Z. (2019). An adaptive ensemble machine learning model for intrusion detection. IEEE Access, 7, 82512–82521. https://doi.org/10.1109/ACCESS.2019.2923640

    Article  Google Scholar 

  20. Vijayanand, R., Devaraj, D., & Kannapiran, B. (2018). Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection. Computers and Security, 77, 304–314. https://doi.org/10.1016/j.cose.2018.04.010

    Article  Google Scholar 

  21. Hadem, P., Saikia, D. K., & Moulik, S. (2021). An SDN-based intrusion detection system using SVM with selective logging for IP traceback. Computer Networks, 191, 108015. https://doi.org/10.1016/j.comnet.2021.108015

    Article  Google Scholar 

  22. Gu, J., Wang, L., Wang, H., & Wang, S. (2019). A novel approach to intrusion detection using SVM ensemble with feature augmentation. Computers and Security, 86, 53–62. https://doi.org/10.1016/j.cose.2019.05.022

    Article  Google Scholar 

  23. Alazzam, H., Sharieh, A., & Sabri, K. E. (2022). A lightweight intelligent network intrusion detection system using OCSVM and Pigeon inspired optimizer. Applied Intelligence, 52(4), 3527–3544. https://doi.org/10.1007/s10489-021-02621-x

    Article  Google Scholar 

  24. 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, Singapore, S. S. Dash, C. Lakshmi, S. Das, & B. K. Panigrahi (Eds.), Springer Singapore, pp. 723–731.

  25. Ozkan-Okay, M., Samet, R., Aslan, Ö., & Gupta, D. (2021). A comprehensive systematic literature review on intrusion detection systems. IEEE Access, 9, 157727–157760. https://doi.org/10.1109/ACCESS.2021.3129336

    Article  Google Scholar 

  26. Li, X., Yi, P., Wei, W., Jiang, Y., & Tian, L. (2021). LNNLS-KH: A feature selection method for network intrusion detection. Security and Communication Networks, 2021, 8830431. https://doi.org/10.1155/2021/8830431

    Article  Google Scholar 

  27. Folorunso, O., Ayo, F. E., & Babalola, Y. E. (2016). Ca-NIDS: A network intrusion detection system using combinatorial algorithm approach. Journal of Information Privacy and Security, 12(4), 181–196. https://doi.org/10.1080/15536548.2016.1257680

    Article  Google Scholar 

  28. Nazir, A., & Khan, R. A. (2021). A novel combinatorial optimization based feature selection method for network intrusion detection. Computers and Security, 102, 102164. https://doi.org/10.1016/j.cose.2020.102164

    Article  Google Scholar 

  29. Zhou, Y., Cheng, G., Jiang, S., & Dai, M. (2020). Building an efficient intrusion detection system based on feature selection and ensemble classifier. Computer Networks, 174, 107247. https://doi.org/10.1016/j.comnet.2020.107247

    Article  Google Scholar 

  30. Chiche, A., & Meshesha, M. (2021). Towards a scalable and adaptive learning approach for network intrusion detection. Journal of Computer Networks and Communications, 2021, 8845540. https://doi.org/10.1155/2021/8845540

    Article  Google Scholar 

  31. Nagaraju, S., Shanmugham, B., & Baskaran, K. (2021). High throughput token driven FSM based regex pattern matching for network intrusion detection system. Materials Today: Proceedings, 47, 139–143. https://doi.org/10.1016/j.matpr.2021.04.028

    Article  Google Scholar 

  32. Sohi, S. M., Seifert, J.-P., & Ganji, F. (2021). RNNIDS: Enhancing network intrusion detection systems through deep learning. Computers and Security, 102, 102151. https://doi.org/10.1016/j.cose.2020.102151

    Article  Google Scholar 

  33. Hajisalem, V., & Babaie, S. (2018). A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection. Computer Networks, 136, 37–50. https://doi.org/10.1016/j.comnet.2018.02.028

    Article  Google Scholar 

  34. Bhati, B. S., Rai, C. S., Balamurugan, B., & Al-Turjman, F. (2020). An intrusion detection scheme based on the ensemble of discriminant classifiers. Computers and Electrical Engineering, 86, 106742. https://doi.org/10.1016/j.compeleceng.2020.106742

    Article  Google Scholar 

  35. Zhou, Y., Mazzuchi, T. A., & Sarkani, S. (2020). M-AdaBoost-a based ensemble system for network intrusion detection. Expert Systems with Applications, 162, 113864. https://doi.org/10.1016/j.eswa.2020.113864

    Article  Google Scholar 

  36. Lv, L., Wang, W., Zhang, Z., & Liu, X. (2020). A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine. Knowledge-Based Systems, 195, 105648. https://doi.org/10.1016/j.knosys.2020.105648

    Article  Google Scholar 

  37. Ayyagari, M. R., Kesswani, N., Kumar, M., & Kumar, K. (2021). Intrusion detection techniques in network environment: a systematic review. Wireless Networks, 27(2), 1269–1285. https://doi.org/10.1007/s11276-020-02529-3

    Article  Google Scholar 

  38. Aldwairi, M., Alshboul, M. A., & Seyam, A. (2018). Characterizing realistic signature-based intrusion detection Benchmarks, In Proceedings of the 6th international conference on information technology: IoT and smart City, Hong Kong. https://doi.org/10.1145/3301551.3301591.

  39. AlYousef, M. Y., & Abdelmajeed, N. T. (2019). Dynamically detecting security threats and updating a signature-based intrusion detection system’s database. Procedia Computer Science, 159, 1507–1516. https://doi.org/10.1016/j.procs.2019.09.321

    Article  Google Scholar 

  40. Liu, J., et al. (2020). Adaptive intrusion detection via GA-GOGMM-based pattern learning with fuzzy rough set-based attribute selection. Expert Systems with Applications, 139, 112845. https://doi.org/10.1016/j.eswa.2019.112845

    Article  Google Scholar 

  41. Alsoufi, M. A., et al. (2021). Anomaly-based intrusion detection systems in IoT using deep learning a systematic literature review. Applied Sciences. https://doi.org/10.3390/app11188383

    Article  Google Scholar 

  42. Jiang, K., Wang, W., Wang, A., & Wu, H. (2020). Network intrusion detection combined hybrid sampling with deep hierarchical network. IEEE Access, 8, 32464–32476. https://doi.org/10.1109/ACCESS.2020.2973730

    Article  Google Scholar 

  43. Hwang, R. H., Peng, M. C., Huang, C. W., Lin, P. C., & Nguyen, V. L. (2020). An unsupervised deep learning model for early network traffic anomaly detection. IEEE Access, 8, 30387–30399. https://doi.org/10.1109/ACCESS.2020.2973023

    Article  Google Scholar 

  44. Li, X., Chen, W., Zhang, Q., & Wu, L. (2020). Building auto-encoder intrusion detection system based on random forest feature selection. Computers and Security, 95, 101851. https://doi.org/10.1016/j.cose.2020.101851

    Article  Google Scholar 

  45. Rao, K. N., Rao, K. V., & PVGD, P. R. (2021). A hybrid intrusion detection system based on sparse autoencoder and deep neural network. Computer Communications, 180, 77–88. https://doi.org/10.1016/j.comcom.2021.08.026

    Article  Google Scholar 

  46. Yang, Y., Zheng, K., Wu, C., & Yang, Y. (2019). Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. Sensors. https://doi.org/10.3390/s19112528

    Article  Google Scholar 

  47. Zhang, J., Ling, Y., Fu, X., Yang, X., Xiong, G., & Zhang, R. (2020). Model of the intrusion detection system based on the integration of spatial-temporal features. Computers and Security, 89, 101681. https://doi.org/10.1016/j.cose.2019.101681

    Article  Google Scholar 

  48. Ugtakhbayar, N., Usukhbayar, B., & Baigaltugs S. (2020). A hybrid model for anomaly-based intrusion detection system, in Advances in Intelligent Information Hiding and Multimedia Signal Processing, Singapore, J.-S. Pan, J. Li, P.-W. Tsai, & L. C. Jain (Eds.), Springer Singapore, pp. 419–431.

  49. Saheed, Y. K., Abdulganiyu, O. H., & Tchakoucht, T. A. (2023). A novel hybrid ensemble learning for anomaly detection in industrial sensor networks and scada systems for smart city infrastructures. Journal of King Saud University-Computer and Information Sciences, 35(5), 101532.

    Article  Google Scholar 

  50. Kaur, S., & Singh, M. (2020). Hybrid intrusion detection and signature generation using deep recurrent neural networks. Neural Computing and Applications, 32(12), 7859–7877. https://doi.org/10.1007/s00521-019-04187-9

    Article  Google Scholar 

  51. Maseno, E. M., Wang, Z., & Xing, H. (2022). A systematic review on hybrid intrusion detection system. Security and Communication Networks, 2022, 9663052. https://doi.org/10.1155/2022/9663052

    Article  Google Scholar 

  52. Abolfathi, M., Shomorony, I., Vahid, A., & Jafarian, J. H. (2022). A Game-theoretically optimal defense paradigm against traffic analysis attacks using multipath routing and deception, Proceedings of the 27th ACM on symposium on access control models and technologies.

  53. Kumar, V., Sinha, D., Das, A. K., Pandey, S. C., & Goswami, R. T. (2020). An integrated rule based intrusion detection system: analysis on UNSW-NB15 data set and the real time online dataset. Cluster Computing, 23(2), 1397–1418. https://doi.org/10.1007/s10586-019-03008-x

    Article  Google Scholar 

  54. Thilagam, T., & Aruna, R. (2021). Intrusion detection for network based cloud computing by custom RC-NN and optimization. ICT Express, 7(4), 512–520. https://doi.org/10.1016/j.icte.2021.04.006

    Article  Google Scholar 

  55. Kanna, P. R., & Santhi, P. (2021). Unified deep learning approach for efficient intrusion detection system using integrated spatial–temporal features. Knowledge-Based Systems, 226, 107132. https://doi.org/10.1016/j.knosys.2021.107132

    Article  Google Scholar 

  56. ManoharNaik, S., & Geethanjali, N. (2016). A multi-fusion pattern matching algorithm for signature-based network intrusion detection system, Preprints, pp. 1–8, https://doi.org/10.20944/preprints201608.0197.v1.

  57. Luo, G., Chen, Z., & Mohammed, B. O. (2022). A systematic literature review of intrusion detection systems in the cloud-based IoT environments. Concurrency and Computation: Practice and Experience, 34(10), e6822. https://doi.org/10.1002/cpe.6822

    Article  Google Scholar 

  58. RM, S. P., et al. (2020). An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture. Computer Communications, 160, 139–149. https://doi.org/10.1016/j.comcom.2020.05.048

    Article  Google Scholar 

  59. Abu Al-Haija, Q., & Al-Badawi, A. (2021). Attack-aware IoT network traffic routing leveraging ensemble learning. Sensors, 22(1), 241.

    Article  Google Scholar 

  60. Kalavadekar, P. N., & Sane, S. S. (2019). Building an effective intrusion detection system using combined signature and anomaly detection techniques. International Journal Innovative Technology Explore Engineering, 8(10), 429.

    Article  Google Scholar 

  61. Aldweesh, A., Derhab, A., & Emam, A. Z. (2020). Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowledge-Based Systems, 189, 105124. https://doi.org/10.1016/j.knosys.2019.105124

    Article  Google Scholar 

  62. Almutairi, A. H., & Abdelmajeed, N. T. (2017). Innovative signature based intrusion detection system: Parallel processing and minimized database. In 2017 International Conference on the Frontiers and Advances in Data Science (FADS), pp. 114–119, https://doi.org/10.1109/FADS.2017.8253208.

  63. Yang, Z., et al. (2022). A systematic literature review of methods and datasets for anomaly-based network intrusion detection. Computers and Security, 116, 102675. https://doi.org/10.1016/j.cose.2022.102675

    Article  Google Scholar 

  64. Abu Al-Haija, Q., & Al Badawi, A. (2022). High-performance intrusion detection system for networked UAVs via deep learning. Neural Computing and Applications, 34(13), 10885–10900. https://doi.org/10.1007/s00521-022-07015-9

    Article  Google Scholar 

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

    Article  Google Scholar 

  66. Rao, C. S., & Raju, K. B. (2019). Mapreduce accelerated signature-based intrusion detection mechanism (idm) with pattern matching mechanism. In Soft Computing in Data Analytics: Proceedings of International Conference on SCDA 2018 (pp. 157-164). Springer Singapore.

  67. Abbasi, J. S., Bashir, F., Qureshi, K. N., ul Islam, M. N., & Jeon, G. (2021). Deep learning-based feature extraction and optimizing pattern matching for intrusion detection using finite state machine. Computers and Electrical Engineering, 92, 107094.

    Article  Google Scholar 

  68. Abu Al-Haija, Q., Al Badawi, A., & Bojja, G. R. (2022). Boost-defence for resilient IoT networks: A head-to-toe approach. Expert Systems, 39(10), e12934.

    Article  Google Scholar 

  69. Liu, J., Gao, Y., & Hu, F. (2021). A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM. Computers and Security, 106, 102289. https://doi.org/10.1016/j.cose.2021.102289

    Article  Google Scholar 

  70. Shahraki, A., Abbasi, M., & Haugen, Ø. (2020). Boosting algorithms for network intrusion detection: A comparative evaluation of real AdaBoost, Gentle AdaBoost and modest AdaBoost. Engineering Applications of Artificial Intelligence, 94, 103770. https://doi.org/10.1016/j.engappai.2020.103770

    Article  Google Scholar 

  71. Mazini, M., Shirazi, B., & Mahdavi, I. (2019). Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms. Journal of King Saud University–Computer and Information Sciences, 31(4), 541–553. https://doi.org/10.1016/j.jksuci.2018.03.011

    Article  Google Scholar 

  72. Ahmad, I., Ul Haq, Q. E., Imran, M., Alassafi, M. O., & AlGhamdi, R. A. (2022). An efficient network intrusion detection and classification system. Mathematics, 10(3), 530.

    Article  Google Scholar 

  73. Kim, G., Lee, S., & Kim, S. (2014). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Systems with Applications, 41(4), 1690–1700. https://doi.org/10.1016/j.eswa.2013.08.066

    Article  MathSciNet  Google Scholar 

  74. Masdari, M., & Khezri, H. (2020). A survey and taxonomy of the fuzzy signature-based intrusion detection systems. Applied Soft Computing, 92, 106301. https://doi.org/10.1016/j.asoc.2020.106301

    Article  Google Scholar 

  75. Meftah, S., Rachidi, T., & Assem, N. (2019). Network based intrusion detection using the UNSW-NB15 dataset. International Journal of Computing and Digital Systems, 8(5), 478–487.

    Google Scholar 

  76. Masdari, M., & Khezri, H. (2021). Towards fuzzy anomaly detection-based security: A comprehensive review. Fuzzy Optimization and Decision Making, 20(1), 1–49. https://doi.org/10.1007/s10700-020-09332-x

    Article  MathSciNet  Google Scholar 

  77. Ashfaq, R. A. R., Wang, X.-Z., Huang, J. Z., Abbas, H., & He, Y.-L. (2017). Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences, 378, 484–497. https://doi.org/10.1016/j.ins.2016.04.019

    Article  Google Scholar 

  78. Larijani, H., Ahmad, J., & Mtetwa, N. (2018, September). A novel random neural network based approach for intrusion detection systems. In 2018 10th Computer Science and Electronic Engineering (CEEC) (pp. 50-55). https://doi.org/10.1109/CEEC.2018.8674228

  79. Tama, B. A., Comuzzi, M., & Rhee, K. (2019). TSE-IDS: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system. IEEE Access, 7, 94497–94507. https://doi.org/10.1109/ACCESS.2019.2928048

    Article  Google Scholar 

  80. Abdulganiyu, O. H., Ait Tchakoucht, T., & Saheed, Y. K. (2023). A systematic literature review for network intrusion detection system (IDS). International Journal of Information Security. https://doi.org/10.1007/s10207-023-00682-2

    Article  Google Scholar 

  81. Yerriswamy, T., & Murtugudde, G. (2021). An efficient algorithm for anomaly intrusion detection in a network. Global Transitions Proceedings, 2(2), 255–260.

    Article  Google Scholar 

  82. Alazzam, H., Sharieh, A., & Sabri, K. E. (2020). A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert Systems with Applications, 148, 113249. https://doi.org/10.1016/j.eswa.2020.113249

    Article  Google Scholar 

  83. Sona, A. S., & Sasirekha, N. (2021). Kulczynski indexed dragonfly feature optimization based polytomous adaptive base classifier for anomaly intrusion detection. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.01.765

    Article  Google Scholar 

  84. Liberati, A., et al. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ, 339, b2700. https://doi.org/10.1136/bmj.b2700

    Article  Google Scholar 

  85. Kitchenham, B., & Brereton, P. (2013). A systematic review of systematic review process research in software engineering. Information and Software Technology, 55(12), 2049–2075. https://doi.org/10.1016/j.infsof.2013.07.010

    Article  Google Scholar 

  86. Kitchenham, B. A., & Stuart, C. (2007). Guidelines for performing systematic literature reviews in software engineering, in EBSE Technical Report, Keele University and Durham University Joint Report, Report EBSE 2007–001, 2007. Available: https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf.

  87. Zhao, H., Li, M., & Zhao, H. (2020). Artificial intelligence based ensemble approach for intrusion detection systems. Journal of Visual Communication and Image Representation, 71, 102736. https://doi.org/10.1016/j.jvcir.2019.102736

    Article  Google Scholar 

  88. Abu Al-Haija, Q., & Zein-Sabatto, S. (2020). An efficient deep-learning-based detection and classification system for cyber-attacks in IoT communication networks. Electronics, 9(12), 2152.

    Article  Google Scholar 

  89. Saheed, Y. K., Abiodun, A. I., Misra, S., Holone, M. K., & Colomo-Palacios, R. (2022). A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Engineering Journal, 61(12), 9395–9409.

    Article  Google Scholar 

  90. D’Agostini, G. (1995). A multidimensional unfolding method based on Bayes’ theorem. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 362(2), 487–498. https://doi.org/10.1016/0168-9002(95)00274-X

    Article  Google Scholar 

  91. Box, G. E. P., & Tiao, G. C. (1973). Bayesian inference in statistical analysis. International Statistical Review, 43, 242.

    Google Scholar 

  92. Ng, A., & Jordan, M. (2001). On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in neural information processing systems, 14.

  93. Soucy, P., & Mineau, G. W. (2001). A simple KNN algorithm for text categorization. In Proceedings 2001 IEEE International Conference on Data Mining, pp. 647–648, https://doi.org/10.1109/ICDM.2001.989592.

  94. Li, W., Yi, P., Wu, Y., Pan, L., & Li, J. (2014). A new intrusion detection system based on KNN classification algorithm in wireless sensor network. Journal of Electrical and Computer Engineering, 2014.

  95. Kotsiantis, S. B. (2007) Supervised machine learning: A review of classification techniques, presented at the Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies.

  96. Du, W., & Zhan, Z. (2002) Building decision tree classifier on private data, presented at the Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14, Maebashi City, Japan.

  97. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251

    Article  Google Scholar 

  98. Kotsiantis, S. B. (2013). Decision trees: A recent overview. Artificial Intelligence Review, 39(4), 261–283. https://doi.org/10.1007/s10462-011-9272-4

    Article  Google Scholar 

  99. Loh, W.-Y. (2011). Classification and regression trees. WIREs Data Mining and Knowledge Discovery, 1(1), 14–23. https://doi.org/10.1002/widm.8

    Article  Google Scholar 

  100. Goeschel, K. (2016). Reducing false positives in intrusion detection systems using data-mining techniques utilizing support vector machines, decision trees, and naive Bayes for off-line analysis. SoutheastCon, 2016, 1–6.

    Google Scholar 

  101. Deng, H., Runger G., & Tuv, E. (2011). Bias of Importance Measures for Multi-valued Attributes and Solutions. In Artificial Neural Networks and Machine Learning – ICANN 2011, Berlin, Heidelberg, T. Honkela, W. Duch, M. Girolami, & S. Kaski, (Eds.), Springer Berlin Heidelberg, pp. 293–300.

  102. Tong, S., & Koller, D. (2001). Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2, 45–66.

    Google Scholar 

  103. Miranda, C., Kaddoum, G., Bou-Harb, E., Garg, S., & Kaur, K. (2020). A collaborative security framework for software-defined wireless sensor networks. IEEE Transactions on Information Forensics and Security, 15, 2602–2615. https://doi.org/10.1109/TIFS.2020.2973875

    Article  Google Scholar 

  104. Liu, Y., & Pi, D. (2017). A novel kernel SVM algorithm with game theory for network intrusion detection. KSII Transactions on Internet and Information Systems, 11, 4043–4060.

    Google Scholar 

  105. Hu, W., Liao, Y., & Vemuri, V. R. (2003). Robust support vector machines for anomaly detection in computer security, in ICMLA.

  106. Cutler, D. R., et al. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792. https://doi.org/10.1890/07-0539.1

    Article  Google Scholar 

  107. Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys and Tutorials, 18, 1153–1176.

    Article  Google Scholar 

  108. Doshi, R., Apthorpe, N., & Feamster, N. (2018, May). Machine learning ddos detection for consumer internet of things devices. In 2018 IEEE Security and Privacy Workshops (SPW) (pp. 29-35). IEEE.

  109. Pal, N. R., Pal, K., Keller, J. M., & Bezdek, J. C. (2005). A possibilistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems, 13, 517–530.

    Article  Google Scholar 

  110. Moustafa, N., Ahmed, M., & Ahmed, S. (2020, December). Data analytics-enabled intrusion detection: Evaluations of ToN_IoT linux datasets. In 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) (pp. 727-735). IEEE.

  111. Abdi, H., & Williams, L. J. (2010). Principal component analysis. WIREs Computational Statistics, 2(4), 433–459. https://doi.org/10.1002/wics.101

    Article  Google Scholar 

  112. Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1), 489–501. https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  113. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  114. Chen, X. W., & Lin, X. (2014). Big Data Deep learning: Challenges and perspectives. IEEE Access, 2, 514–525. https://doi.org/10.1109/ACCESS.2014.2325029

    Article  Google Scholar 

  115. Ciresan, D. C., Meier, U., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2011). Flexible, high performance convolutional neural networks for image classification. In Twenty-second international joint conference on artificial intelligence.

  116. Chen, Y., Zhang, Y., & Maharjan, S. (2017). Deep learning for secure mobile edge computing. arXiv preprint arXiv:1709.08025.

  117. Hermans, M., & Schrauwen, B. (2013). Training and analyzing deep recurrent neural networks, in NIPS 2013.

  118. Pascanu, R., Gülçehre, Ç., Cho, K., & Bengio, Y. (2014). How to construct deep recurrent neural networks, CoRR, vol. abs/1312.6026.

  119. Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018). Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications, 105, 233–261. https://doi.org/10.1016/j.eswa.2018.03.056

    Article  Google Scholar 

  120. Tang, T. A., Mhamdi, L., McLernon, D., Zaidi, S. A. R., & Ghogho, M. (2018). Deep recurrent neural network for intrusion detection in sdn-based networks, in 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), pp. 202–206, https://doi.org/10.1109/NETSOFT.2018.8460090.

  121. Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation, 31(7), 1235–1270. https://doi.org/10.1162/neco_a_01199

    Article  MathSciNet  Google Scholar 

  122. Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: continual prediction with LSTM. Neural computation, 12(10), 2451–2471.

    Article  Google Scholar 

  123. Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.

  124. Tschannen, M., Bachem, O., & Lucic, M. (2018). Recent advances in autoencoder-based representation learning. arXiv preprint arXiv:1812.05069.

  125. Hinton, G. E. (2012). A practical guide to training restricted Boltzmann machines. In Neural Networks: Tricks of the Trade: Second Edition (pp. 599-619). Berlin, Heidelberg: Springer Berlin Heidelberg.

  126. Mayuranathan, M., Murugan, M., & Dhanakoti, V. (2021). Best features based intrusion detection system by RBM model for detecting DDoS in cloud environment. Journal of Ambient Intelligence and Humanized Computing, 12, 3609–3619.

    Article  Google Scholar 

  127. Fiore, U., Palmieri, F., Castiglione, A., & Santis, A. D. (2013). Network anomaly detection with the restricted Boltzmann machine. Neurocomputer, 122, 13–23. https://doi.org/10.1016/j.neucom.2012.11.050

    Article  Google Scholar 

  128. Keyvanrad, M. A., & Homayounpour, M. M. (2014). A brief survey on deep belief networks and introducing a new object oriented toolbox (DeeBNet). arXiv preprint arXiv:1408.3264.

  129. Dietterich, T. G. (2000). Ensemble methods in machine learning. In International workshop on multiple classifier systems (pp. 1-15). Berlin, Heidelberg: Springer Berlin Heidelberg.

  130. Woniak, M., Graña, M., & Corchado, E. (2014). A survey of multiple classifier systems as hybrid systems. Information Fusion, 16, 3–17. https://doi.org/10.1016/j.inffus.2013.04.006

    Article  Google Scholar 

  131. Illy, P., Kaddoum, G., Moreira, C. M., Kaur, K., & Garg, S. (2019). Securing fog-to-things environment using intrusion detection system based on ensemble learning. In 2019 IEEE wireless communications and networking conference (WCNC) (pp. 1-7). IEEE.

  132. Domingos, P. M. (2012). A few useful things to know about machine learning. Communications of the ACM, 55, 78–87.

    Article  Google Scholar 

  133. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655

    Article  Google Scholar 

  134. Baba, N. M., Makhtar, M., Fadzli, S. A., & Awang, M. K. (2015). CURRENT ISSUES IN ENSEMBLE METHODS AND ITS APPLICATIONS. Journal of Theoretical & Applied Information Technology, 81(2).

  135. Santana, L. E., Silva, L., Canuto, A. M., Pintro, F., & Vale, K. O. (2010). A comparative analysis of genetic algorithm and ant colony optimization to select attributes for an heterogeneous ensemble of classifiers. In IEEE congress on evolutionary computation (pp. 1-8). IEEE.

  136. Bosman, H. H. W. J., Iacca, G., Tejada, A., Wörtche, H. J., & Liotta, A. (2015). Ensembles of incremental learners to detect anomalies in ad hoc sensor networks. Ad Hoc Networks, 35, 14–36.

    Article  Google Scholar 

  137. Abu Al-Haija, Q., & Al-Dala’ien, M. A. (2022). ELBA-IoT: An ensemble learning model for botnet attack detection in IoT networks. Journal of Sensor and Actuator Networks, 11(1), 18.

    Article  Google Scholar 

  138. Aho, A. V., & Corasick, M. J. (1975). Efficient string matching. Communications of the ACM, 18, 333–340.

    Article  MathSciNet  Google Scholar 

  139. Alicherry, M., Muthuprasanna, M., & Kumar, V. (2006, November). High speed pattern matching for network IDS/IPS. In Proceedings of the 2006 IEEE International Conference on Network Protocols (pp. 187-196). IEEE.

  140. Knuth, D. E., Morris, J. H., & Pratt, V. R. (1977). Fast pattern matching in strings. SIAM Journal on Computing, 6, 323–350.

    Article  MathSciNet  Google Scholar 

  141. Wu, S., & Manber, U. (1994). A fast algorithm for multi-pattern searching (pp. 1-11). Tucson, AZ: University of Arizona. Department of Computer Science.

  142. Boyer, R. S., & Moore, J. S. (1977). A fast string searching algorithm. Communications of the ACM, 20(10), 762–772. https://doi.org/10.1145/359842.359859

    Article  Google Scholar 

  143. Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020). A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions. Electronics, 9(7), 1177.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. EuroMed Research Center, School of Digital Engineering and Artificial Intelligence, Euro-Mediterranean University of Fes, 30030, Fes, Morocco

    Oluwadamilare Harazeem Abdulganiyu & Taha Ait Tchakoucht

  2. School of IT and Computing, American University of Nigeria, Yola, Nigeria

    Yakub Kayode Saheed

Authors
  1. Oluwadamilare Harazeem Abdulganiyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Taha Ait Tchakoucht
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yakub Kayode Saheed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oluwadamilare Harazeem Abdulganiyu.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest in this paper.

Additional information

Publisher's Note

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

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

Abdulganiyu, O.H., Tchakoucht, T.A. & Saheed, Y.K. Towards an efficient model for network intrusion detection system (IDS): systematic literature review. Wireless Netw 30, 453–482 (2024). https://doi.org/10.1007/s11276-023-03495-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03495-2

Keywords

Search

Navigation