Skip to main content

Advertisement

SpringerLink
Log in

A novel optimization based deep learning with artificial intelligence approach to detect intrusion attack in network system

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Modern life is increasingly influenced by networks, making cybersecurity a crucial area of study. However, due to their few resources and varied makeup, they are more vulnerable to a wide range of cyber-attacks. Such risks result in sensitive information being stolen as well as financial and reputational harm to firms. How far malicious detection techniques have advanced in the intrusion detection system (IDS) industry is difficult to quantify. Therefore, a unique IDS known optimized Artificial Intelligence approach is designed to effectively identify the intrusions. The preprocessing activity is initially carried out to improve the data quality for identifying network intrusions utilizing normalization and standardization. The Corporate Hierarchy optimization (CHO) technique is then used to choose the important features from the databases. By accurately identifying intrusions, the suggested golden eagle optimization-based Self-constructing Multi-layer Perceptron Interfaced Fuzzy system (GEO-SMPIF) solution improves privacy and security within the professional network infrastructure. The ensuing hyperparameters are adjusted optimally using GEO methods during parameter identification, and a backpropagation technique tunes the precondition parameters. Using datasets of NSL-KDD and UNSW-NB15, the Python platform creates the experimental setup. When compared to existing approaches, the analysis's findings show that the suggested method performs better at identifying intrusions with prediction performance in terms of a 99.78% of high detection rate, 99.99% of high accuracy, and 0.04 less false alarm rate in NSL-KDD and 99.70% of high detection rate, 99.97% of high accuracy and 0.065 of less false alarm rate in UNSW-NB15 dataset.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Code availability

Not applicable.

References

  • Abdelmoumin, G., Rawat, D. B., & Rahman, A. (2021). On the performance of machine learning models for anomaly-based intelligent intrusion detection systems for the internet of things. IEEE Internet of Things Journal, 9(6), 4280–4290.

    Article  Google Scholar 

  • Abduvaliyev, A, et al (2013) "On the vital areas of intrusion detection systems in wireless sensor networks." IEEE Communications Surveys & Tutorials 15.3 1223–1237

  • Abraham, A, Grosan, C and Chen, Y (2005) "Cyber security and the evolution in intrusion detection systems." Journal of Engineering and Technology, ISSN 0973–2632.

  • Almomani, O. (2021). A hybrid model using bio-inspired metaheuristic algorithms for network intrusion detection system. Comput. Mater. Continua, 68(1), 409–429.

    Article  MathSciNet  Google Scholar 

  • Alqahtani, A S (2022) "FSO-LSTM IDS: hybrid optimized and ensembled deep-learning network-based intrusion detection system for smart networks." The Journal of Supercomputing 1–18.

  • Alqahtani, H, et al (2020) "Cyber intrusion detection using machine learning classification techniques." International conference on computing science, communication and security. Springer, Singapore.

  • Apruzzese, G., et al. (2022). "Modeling realistic adversarial attacks against network intrusion detection systems. Digital Threats: Research Practice (DTRAP), 33, 1–19.

    Article  Google Scholar 

  • Askari, Q., Saeed, M., & Younas, I. (2020). Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Systems with Applications, 161, 113702.

    Article  Google Scholar 

  • Benmessahel, I., Xie, K., & Chellal, M. (2018). A new evolutionary neural networks based on intrusion detection systems using multiverse optimization. Applied Intelligence, 48(8), 2315–2327.

    Article  Google Scholar 

  • Benmessahel, Ilyas, et al. (2019). A new evolutionary neural networks based on intrusion detection systems using locust swarm optimization. Evolutionary Intelligence, 12.2, 131–146.

    Article  Google Scholar 

  • Dwivedi, S., Vardhan, M., & Tripathi, S. (2021). Building an efficient intrusion detection system using grasshopper optimization algorithm for anomaly detection. Cluster Computing, 24(3), 1881–1900.

    Article  Google Scholar 

  • Juang, C.-F., & Lin, C.-T. (1998). An online self-constructing neural fuzzy inference network and its applications. IEEE Transactions on Fuzzy Systems, 6(1), 12–32.

    Article  Google Scholar 

  • Kanna, P R, and Santhi, P (2022) "Hybrid intrusion detection using mapreduce based black widow optimized convolutional long short-term memory neural networks." Expert Systems with Applications: 116545.

  • Kareem, Saif S., et al. (2022). An effective feature selection model using hybrid metaheuristic algorithms for iot intrusion detection. Sensors, 22.4, 1396.

    Article  ADS  Google Scholar 

  • Kaur, J., Talwar, R., & Goel, A. K. (2021). MOpt Shield: An Intrusion Detection System based on Meld Optimization Algorithm to mitigate Amalgam Attacks. Indian Journal of Science and Technology, 14(20), 1622–1634.

    Article  Google Scholar 

  • Khan, M. A. (2021). HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system. Processes, 9(5), 834.

    Article  Google Scholar 

  • Kunang, Y. N., et al. (2021). Attack classification of an intrusion detection system using deep learning and hyperparameter optimization. Journal of Information Security and Applications, 58, 102804.

    Article  Google Scholar 

  • Kunhare, N., Tiwari, R., & Dhar, J. (2020). Particle swarm optimization and feature selection for intrusion detection system. Sādhanā, 45(1), 1–14.

    Article  Google Scholar 

  • Lahasan, B., & Samma, H. (2022). Optimized Deep Autoencoder Model for Internet of Things Intruder Detection. IEEE Access, 10, 8434–8448.

    Article  Google Scholar 

  • Liu, H., & Lang, B. (2019). "Machine learning and deep learning methods for intrusion detection systems: A survey. Applied sciences, 9.20, 4396.

    Article  Google Scholar 

  • Mohammadi-Balani, A., et al. (2021). Golden eagle optimizer: A nature-inspired metaheuristic algorithm. Computers & Industrial Engineering, 152, 107050.

    Article  Google Scholar 

  • Mohd, N., Singh, A., & Bhadauria, H. S. (2021). Intrusion Detection System Based on Hybrid Hierarchical Classifiers. Wireless Personal Communications, 121.1, 659–686.

    Article  Google Scholar 

  • Moustafa, N, and Slay, J (2015) "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)." 2015 military communications and information systems conference (MilCIS). IEEE.

  • Pawar, M., & Agarwal, J. (2017). A literature survey on security issues of WSN and different types of attacks in network. Indian J. Comput. Sci. Eng, 8(2), 80–83.

    Google Scholar 

  • Sarker, I. H., et al. (2020). "Intrudtree: a machine learning based cyber security intrusion detection model. Symmetry, 12.5, 754.

    Article  ADS  Google Scholar 

  • Sekhar, R., et al. (2021). A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization. SN Applied Sciences, 3(6), 1–16.

    Article  Google Scholar 

  • Song, C., et al. (2021). Intrusion detection based on hybrid classifiers for smart grid. Computers & Electrical Engineering, 93, 107212.

    Article  Google Scholar 

  • Taud, H, and Mas, JF (2018) "Multilayer perceptron (MLP)." Geomatic approaches for modeling land change scenarios. Springer, Cham, 451–455.

  • Vigneswaran, RK., et al (2018) "Evaluating shallow and deep neural networks for network intrusion detection systems in cyber security." 2018 9th International conference on computing, communication and networking technologies (ICCCNT). IEEE.

  • Vijayanand, R., & Devaraj, D. (2020). A novel feature selection method using whale optimization algorithm and genetic operators for intrusion detection system in wireless mesh network. IEEE Access, 8, 56847–56854.

    Article  Google Scholar 

  • Wisam, E., Akbulut, A., & Zaim, A. H. (2020). "Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Computer Networks, 168, 107042.

    Article  Google Scholar 

  • Xu, K, et al (2018) "Deeprefiner: Multi-layer android malware detection system applying deep neural networks." 2018 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE.

  • Yang, Li., Moubayed, A., & Shami, A. (2021). MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles. IEEE Internet of Things Journal, 9(1), 616–632.

    Article  Google Scholar 

  • Zhong, W., Ning, Yu., & Ai, C. (2020). Applying big data based deep learning system to intrusion detection. Big Data Mining and Analytics, 3(3), 181–195.

    Article  Google Scholar 

  • Zhu, N., et al. (2022). Optimization of the Random Forest Hyperparameters for Power Industrial Control Systems Intrusion Detection Using an Improved Grid Search Algorithm. Applied Sciences, 12.20, 10456.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering - AI&ML, KG Reddy College of Engineering and Technology, Chilukuru Village, Moinabad,R R Dist, Hyderabad, Telangana, India

    S. Siva Shankar

  2. Director of Artificial Intelligence Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Bui Thanh Hung

  3. Deputy Provost, lTM SLS Baroda University, Vadodara, 391510, Gujarat, India

    Prasun Chakrabarti

  4. Department of Chemistry, Sir Padampat Singhania University, Udaipur, Rajasthan, 313601, India

    Tulika Chakrabarti

  5. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522502, India

    Gayatri Parasa

Authors
  1. S. Siva Shankar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bui Thanh Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prasun Chakrabarti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tulika Chakrabarti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gayatri Parasa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Siva Shankar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent does not apply as this was a retrospective review with no identifying patient information.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

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

Siva Shankar, S., Hung, B.T., Chakrabarti, P. et al. A novel optimization based deep learning with artificial intelligence approach to detect intrusion attack in network system. Educ Inf Technol 29, 3859–3883 (2024). https://doi.org/10.1007/s10639-023-11885-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-023-11885-4

Keywords

Search

Navigation