AI-Enabled Intelligent Intrusion Detection Framework Using Artificial Neural Networks for Secure and Sustainable Networked Systems
Authors
School of Computer Science and Engineering, Sandip University, Sijoul, Madhubani (India)
Department of Computer Science and Engineering, Shri Ramswaroop Memorial University, Barabanki (India)
Article Information
DOI: 10.47772/IJRISS.2026.100300102
Subject Category: Social science
Volume/Issue: 10/3 | Page No: 1497-1510
Publication Timeline
Submitted: 2026-03-11
Accepted: 2026-03-16
Published: 2026-03-27
Abstract
The explosion of cloud computing, online services, and interlinked digital services has contributed to increased susceptibility of modern networks to cyber-attacks. Traditional Intrusion Detection Systems (IDS) detect attacks by utilising signature-based detection methods, which often fail to recognise novel or previously unrecorded attack patterns. To counter these shortcomings, the research will describe a sophisticated Artificial Neural Network (ANN) application, designed to not only improve the effectiveness of cyber security systems, but also boost the overall rate of threat detection. Proposed detection systems will improve cyber security by employing the ability of neural networks to learn patterns, and will therefore be able to evaluate and categorise network activity as being acceptable, or as representing a threat. The complete system will consist of a number of steps including, but not limited to, the acquisition of datasets, and the application of preprocessing.feature encoding, feature normalization and selection to improve data quality and minimize redundancy. It is a multilayer feedforward neural network model that is trained and tested over benchmark intrusion detection datasets against a number of attack types that include Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R) attacks. As demonstrated through experimental analysis, the proposed ANN model will achieve high precision and recall in addition to low false positive rate at 97.6 percent. Additional comparative study can show that ANN-based methodology outperforms other traditional machine learning algorithms, such as Decision Trees, Support Vector Machines, and Random Forest classifiers. The results show that neural network-based solutions can be useful in detecting complex intrusion patterns and making real-time network security in modern computing and cloud-based systems, with Internet of Things (IoT) networks.
Keywords
Artificial Neural Network (ANN); Network Intrusion Detection; Cybersecurity
Downloads
References
1. D. E. Denning, “An intrusion-detection model,” IEEE Trans. Softw. Eng., vol. SE-13, no. 2, pp. 222–232, 1987. [Google Scholar] [Crossref]
2. S. Axelsson, “Intrusion detection systems: A survey and taxonomy,” Technical Report, Chalmers University of Technology, 2000. [Google Scholar] [Crossref]
3. W. Lee and S. J. Stolfo, “A framework for constructing features and models for intrusion detection systems,” ACM Trans. Inf. Syst. Secur., vol. 3, no. 4, pp. 227–261, 2000. [Google Scholar] [Crossref]
4. Shad Kirmani and Padma Raghavan. 2013. Scalable parallel graph partitioning. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC '13). Association for Computing Machinery, New York, NY, USA, Article 51, 1–10. https://doi.org/10.1145/2503210.2503280 [Google Scholar] [Crossref]
5. Kirmani S, Park J, Raghavan P. An embedded sectioning scheme for multiprocessor topology-aware mapping of irregular applications. The International Journal of High Performance Computing Applications. 2017;31(1):91-103. doi:10.1177/1094342015597082 [Google Scholar] [Crossref]
6. S. Kirmani and M. Shankar, “Generating keywords by associative context with input words,” US Patent US10699302B2, Jun. 30, 2020. [Online]. Available: https://patents.google.com/patent/US10699302B2/en [Google Scholar] [Crossref]
7. S. Kirmani and K. Madduri, "Spectral Graph Drawing: Building Blocks and Performance Analysis," 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Vancouver, BC, Canada, 2018, pp. 269-277, doi: 10.1109/IPDPSW.2018.00053 [Google Scholar] [Crossref]
8. S. Kirmani, H. Sun and P. Raghavan, "A Scalability and Sensitivity Study of Parallel Geometric Algorithms for Graph Partitioning," 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Lyon, France, 2018, pp. 420-427, doi: 10.1109/CAHPC.2018.8645916. [Google Scholar] [Crossref]
9. Ashirbad Mishra, Shad Kirmani, and Kamesh Madduri. 2020. Fast Spectral Graph Layout on Multicore Platforms. In Proceedings of the 49th International Conference on Parallel Processing (ICPP '20). Association for Computing Machinery, New York, NY, USA, Article 45, 1–11. https://doi.org/10.1145/3404397.3404471 [Google Scholar] [Crossref]
10. Tyler J, Pastor J, Huhns MN, Kirmani S, Du H. Exposing, formalizing and reasoning over the latent semantics of tags in multimodal data sources. Applied Ontology. 2013;8(2):95-130. doi:10.3233/AO-130124 [Google Scholar] [Crossref]
11. Mishra, S. Kirmani and K. Madduri, "Fast Sentence Classification using Word Co-occurrence Graphs*," 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, 2024, pp. 620-629, doi: 10.1109/BigData62323.2024.10825869. [Google Scholar] [Crossref]
12. S. Kirmani, “Exploiting Graph Embedding for Parallelism and Performance,” Ph.D. dissertation, Dept. of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, USA, 2014. Available: https://etda.libraries.psu.edu/catalog/27325 [Google Scholar] [Crossref]
13. F. Kirmani, B. J. Lane and J. R. Rose, "Exploring Machine Learning Techniques to Improve Peptide Identification," 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Greece, 2019, pp. 66-71, doi: 10.1109/BIBE.2019.00021. [Google Scholar] [Crossref]
14. Fawad Kirmani, Bryan Lane, and John Rose. 2025. Identifying Proteotypic Peptides via Deep Learning. In Proceedings of the 11th International Conference on Bioinformatics Research and Applications (ICBRA '24). Association for Computing Machinery, New York, NY, USA, 42–47. https://doi.org/10.1145/3700666.3700691 [Google Scholar] [Crossref]
15. Fawad Kirmani, Ananthavishnu S Unni, Varsha P Kulkarni, Kyle Lackey, John R Rose, Detecting polar ring galaxies via deep learning, RAS Techniques and Instruments, Volume 4, 2025, rzaf043, https://doi.org/10.1093/rasti/rzaf043 [Google Scholar] [Crossref]
16. Kirmani, F., “Detecting Strongly-Lensed Supernovae in Wide-field Space Telescope Imaging via Deep Learning”, arXiv e-prints, Art. no. arXiv:2512.19886, 2025. doi:10.48550/arXiv.2512.19886. [Google Scholar] [Crossref]
17. M. Alenezi, M. Nadeem, A. Agrawal, R. Kumar, and R. A. Khan, “Fuzzy multi criteria decision analysis method for assessing security design tactics for web applications,” Int. J. Intell. Eng. Syst., vol. 13, no. 5, 2020, doi: 10.22266/ijies2020.1031.17. [Google Scholar] [Crossref]
18. M. Ahmad et al., “Healthcare device security assessment through computational methodology,” Comput. Syst. Sci. Eng., vol. 41, no. 2, 2022, doi: 10.32604/csse.2022.020097. [Google Scholar] [Crossref]
19. H. Alyami et al., “The evaluation of software security through quantum computing techniques: A durability perspective,” Appl. Sci., vol. 11, no. 24, 2021, doi: 10.3390/app112411784. [Google Scholar] [Crossref]
20. W. Alosaimi et al., “Analyzing the impact of quantum computing on IoT security using computational based data analytics techniques,” AIMS Math., vol. 9, no. 3, pp. 7017–7039, 2024, doi: 10.3934/math.2024342. [Google Scholar] [Crossref]
21. Alharbi et al., “Managing Software Security Risks through an Integrated Computational Method,” Intell. Autom. Soft Comput., vol. 28, no. 1, p. 179, Mar. 2021, doi: 10.32604/IASC.2021.016646. [Google Scholar] [Crossref]
22. S. H. Almotiri, M. Nadeem, M. A. Al Ghamdi, and R. A. Khan, “Analytic Review of Healthcare Software by Using Quantum Computing Security Techniques,” Int. J. Fuzzy Log. Intell. Syst., vol. 23, no. 3, pp. 336–352, Sep. 2023, doi: 10.5391/IJFIS.2023.23.3.336. [Google Scholar] [Crossref]
23. M. Nadeem, M. Ahmad, M. Ahmad, P. C. Pathak, S. Gupta, and H. Pandey, “Evaluating the Factors of CGTMSE Scheme in Bank by Using Fuzzy AHP,” in 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), 2023, vol. 6, pp. 56–61, doi: 10.1109/IC3I59117.2023.10397669. [Google Scholar] [Crossref]
24. F. A. Alzahrani, M. Ahmad, M. Nadeem, R. Kumar, and R. A. Khan, “Integrity Assessment of Medical Devices for Improving Hospital Services,” Comput. Mater. Contin., vol. 67, no. 3, p. 3619, Mar. 2021, doi: 10.32604/CMC.2021.014869. [Google Scholar] [Crossref]
25. P. C. Pathak, M. Nadeem, and S. A. Ansar, “Security assessment of operating system by using decision making algorithms,” Int. J. Inf. Technol., 2024, doi: 10.1007/s41870-023-01706-9. [Google Scholar] [Crossref]
26. Masood Ahmad, F. Al-Amri, “Healthcare Device Security Assessment through Computational Methodology,” Comput. Syst. Sci. Eng., vol. 41, no. 2, pp. 811–828, 2022, doi: 10.32604/csse.2022.020097. [Google Scholar] [Crossref]
27. H. Alyami et al., “Analyzing the data of software security life-span: Quantum computing era,” Intell. Autom. Soft Comput., vol. 31, no. 2, 2022, doi: 10.32604/iasc.2022.020780. [Google Scholar] [Crossref]
28. F. A. Alzahrani, M. Ahmad, M. Nadeem, R. Kumar, and R. A. Khan, “Integrity Assessment of Medical Devices for Improving Hospital Services,” Comput. Mater. Contin., vol. 67, no. 3, 2021, doi: 10.32604/cmc.2021.014869. [Google Scholar] [Crossref]
29. F. Alassery, A. Alzahrani, A. I. Khan, A. Khan, M. Nadeem, and M. T. J. Ansari, “Quantitative Evaluation of Mental-Health in Type-2 Diabetes Patients Through Computational Model,” Intell. Autom. Soft Comput., vol. 32, no. 3, 2022, doi: 10.32604/IASC.2022.023314. [Google Scholar] [Crossref]
30. M. Nadeem, “Deep Learning Approach for Classifying DDoS Attack Traffic in SDN Environments”, JISCR, vol. 7, no. 2, pp. 109-126, Dec. 2024. [Google Scholar] [Crossref]
31. Mohd Nadeem, Amal Krishna Sarkar, Mohammed Ishrat, "Securing information systems through quantum computing Grover's algorithm approach", Computational Intelligence Applications in Cyber Security, 1st Edition, 2024. [Google Scholar] [Crossref]
32. Mohd Nadeem, Prabhash Chandra Pathak, Masood Ahmad, Nafees Akhter Farooqui, "Identification of security factors in cloud computing Defence security perspective", Computational Intelligence Applications in Cyber Security, 1st Edition, 2024. [Google Scholar] [Crossref]
33. N. Moustafa and J. Slay, “UNSW-NB15: A comprehensive data set for network intrusion detection systems,” in Military Communications and Information Systems Conf., 2015. [Google Scholar] [Crossref]
34. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 dataset,” in IEEE Symp. Computational Intelligence for Security and Defense Applications, 2009. [Google Scholar] [Crossref]
35. K. Scarfone and P. Mell, “Guide to intrusion detection and prevention systems,” NIST Special Publication 800-94, 2007. [Google Scholar] [Crossref]
36. M. Roesch, “Snort: Lightweight intrusion detection for networks,” in Proc. USENIX Conf. System Administration, 1999. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- The Impact of Ownership Structure on Dividend Payout Policy of Listed Plantation Companies in Sri Lanka
- Urban Sustainability in North-East India: A Study through the lens of NER-SDG index
- Performance Assessment of Predictive Forecasting Techniques for Enhancing Hospital Supply Chain Efficiency in Healthcare Logistics
- The Fractured Self in Julian Barnes' Postmodern Fiction: Identity Crisis and Deflation in Metroland and the Sense of an Ending
- Impact of Flood on the Employment, Labour Productivity and Migration of Agricultural Labour in North Bihar