A Comprehensive Framework for Enhancing Intrusion Detection Systems through Advanced Analytical Techniques
Subject Areas : Pattern RecognitionChetan Gupta 1 * , Amit Kumar 2 , Neelesh Kumar Jain 3
1 - Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, India
2 - Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, India
3 - Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, India
Keywords: IDS, DOS, XGBOOST, PCA, HIDS, NIDS,
Abstract :
Intrusion detection systems (IDS) are security technologies that monitor system activity, network traffic, and settings to detect potential threats. IDS provide proactive security management, detecting anomalies and ensuring continuous monitoring. It protects critical assets, such as sensitive data and intellectual property, from unauthorized access or data breaches, preventing downtime and disruption to business operations. In this paper we present a hybrid model based on Principal Component Analysis (PCA) and XGBoost algorithms. To show the effectiveness of the proposed system, various parameters are evaluated on the standard NSL-KDD dataset. First we trained the model using trained dataset and then evaluate the performance the model using testing dataset. In proposed work the we store the data into two-dimensional structure then we standardized and take a most significance features of the data then calculate the covariance matrix, after that calculate the eigenvalues and eigenvectors of the matrix and short in the descending order and using principal component identify the new features and remove the insignificant features. The proposed model outperforms and produces 97.76% accuracy and 94.51% precision; the recall rate is 93.44% and 93.97% F1-Score, which is much better than the previous proposed models. This hybrid approach is better to handle the categorical data and able to find the pattern well and the outcome of the model clearly shows the effectiveness of the proposed system.
[1] Louati, F., Ktata, F.B. et al. “Big-IDS: a decentralized multi agent reinforcement learning approach for distributed intrusion detection in big data networks”. – In: Cluster Computing, March 2024, Volume 27, pages 6823–6841. https://doi.org/10.1007/s10586-024-04306-9.
[2] Gupta, C., Kumar, A. & Jain, N.K. An Enhanced Hybrid Intrusion Detection Based on Crow Search Analysis Optimizations and Artificial Neural Network. Wireless Pers. Commun. 134, 43–68 (2024). https://doi.org/10.1007/s11277-024-10880-3.
[3] Gupta, N., Jindal, V. et al. “A Survey on Intrusion Detection and Prevention Systems”. – In: SN Computer Science. SCI. June 2023, Volume 4, article number 439. https://doi.org/10.1007/s42979-023-01926-7.
[4] 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.
[5] AL-Syouf, R., Bani-Hani, R. & AL-Jarrah, O.Y. “Machine learning approaches to intrusion detection in unmanned aerial vehicles (UAVs). – In: Neural Computing & Application”, August 2024 Volume 36, pages 18009–18041. https://doi.org/10.1007/s00521-024-10306-y.
[6] Kumar, V., Kumar, V., Singh, N. et al. “P3IDF-EC: PCA-Based Privacy-Preserving Intrusion Detection Framework for Edge Computing”. – In: SN COMPUT. SCI. August 2024. Volume 5. https://doi.org/10.1007/s42979-024-03152-1.
[7] Behiry, M.H., Aly, M. “Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods”. – In: J Big Data, January 2024, volume 11. https://doi.org/10.1186/s40537-023-00870-w.
[8] Altamimi, S., Abu Al-Haija, Q. “Maximizing intrusion detection efficiency for IoT networks using extreme learning machine”. – In: Discover Internet Things, July 2024, volume 4. https://doi.org/10.1007/s43926-024-00060-x.
[9] Gupta, C., Kumar, A. & Jain, N.K. Intelligent intrusion detection system based on crowd search optimization for attack classification in network security. EURASIP J. on Info. Security 2025, 22 (2025). https://doi.org/10.1186/s13635-025-00205-7.
[10] Ajmal, S., Ashfaq, R.A.R., Raza, A. et al. “IDS-FRNN: an intrusion detection system with optimized fuzziness-based sample selection technique”. – In: Neural Computing & Applications. September 2024. https://doi.org/10.1007/s00521-024-10333-9.
[11] Patthi, S., Singh, S. et al. “2-layer classification model with correlated common feature selection for intrusion detection system in networks”. – In: Multimedia Tools and Applications January 2024 Volume 83, pages 61213–61238. https://doi.org/10.1007/s11042-023-17781-w.
[12] Al-Haija Qasem A, Saleh E et al. “Detecting port scan attacks using logistic regression”. – In: 4th International symposium on advanced electrical and communication technologies (ISAECT), pages 1–5. IEEE. https://doi.org/10.1109/ISAECT53699.2021.9668562.
[13] Zaben, S.O. “IDC-insight: boosting intrusion detection accuracy in IoT networks with Naïve Bayes and multiple classifiers”. – In: International Journal of Information Technology June 2024. https://doi.org/10.1007/s41870-024-02026-2.
[14] Al-Haija Qasem A, McCurry Charles D, et al. “Intelligent self-reliant cyber-attacks detection and classification system for IOT communication using deep convolutional neural network”. – In: 12th international networking conference: INC 2020 12, pages 100–116. Springer.
[15] Saurabh, K., Sharma, V., Singh, U. et al. “HMS-IDS: Threat Intelligence Integration for Zero-Day Exploits and Advanced Persistent Threats in IoT”. – In: Arabian Journal for Science and Engineering, July 2024. https://doi.org/10.1007/s13369-024-08935-5.
[16] Gupta, C., Kumar, A., Jain, N.K. (2023). A Detailed Analysis on Intrusion Detection Systems, Datasets, and Challenges. “Advances in Data Science and Computing Technologies”. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_26.
[17] Roshan, K. et al. Ensemble adaptive online machine learning in data stream: a case study in cyber intrusion detection system. – In: International Journal of Information Technology, February 2024. https://doi.org/10.1007/s41870-024-01727-y.
[18] Najafli, S., Toroghi Haghighat, A. et al. “A novel reinforcement learning-based hybrid intrusion detection system on fog-to-cloud computing”. – In: The Journal of Supercomputing, August 2024, Volume 80, pages 26088–26110. https://doi.org/10.1007/s11227-024-06417-x.
[19] Wang, K., Li, J. & Wu, W. “A novel transfer extreme learning machine from multiple sources for intrusion detection”. – In: Peer-to-Peer Networking and Applications. October 2024, Volume 17, pages 33–47. https://doi.org/10.1007/s12083-023-01569-8.
[20] Ngo, VD. Vuong, TC, Van Luong, T. et al. “Machine learning-based intrusion detection feature selection versus feature extraction”. – In: Cluster Computing, July 2024, Volume 27, pages 2365–2379. https://doi.org/10.1007/s10586-023-04089-5.
[21] Mustafa, Z., Amin, R., Aldabbas, H. et al. “Intrusion detection systems for software-defined networks: a comprehensive study on machine learning-based techniques”. – In: Cluster Computing, April 2024Volume 27, pages 9635–9661. https://doi.org/10.1007/s10586-024-04430-6.
[22] Madhuri, S., Lakshmi, S.V. “A machine learning-based normalized fuzzy subset linked model in networks for intrusion detection”. – In: Soft Computing. May 2023. https://doi.org/10.1007/s00500-023-08160-6.
[23] Dubey, S., Gupta, C. (2024). An Effective Model for Binary and Multi-classification Based on RFE and XGBoost Methods. “Intrusion Detection System. Cyber Security and Digital Forensics”. Lecture Notes in Networks and Systems, vol. 896. Springer. https://doi.org/10.1007/978-981-99-9811-1_3.
[24] Liu, Y., Zhang, K. & Wang, Z. “Intrusion detection of manifold regularized broad learning system based on LU decomposition”. – In: The Journal of Supercomputing, June 2023 Volume 79, pages 20600–20648. https://doi.org/10.1007/s11227-023-05403-z.
[25] Gupta, C., Kumar, A., Jain, N.K. (2025). Optimization Accuracy of Intrusion Detection System Based on Multilayered Neural Network. “Business Intelligence, Computational Mathematics, and Data Analytics. IBCD”. Communications in Computer and Information Science, vol 2413. Springer, Cham. https://doi.org/10.1007/978-3-031-87511-3_14.
[26] Wang, X., Dai, L. & Yang, G. “A network intrusion detection system based on deep learning in the IoT”. – In: The Journal of Supercomputing July 2024, Volume 80, pages 24520–24558. https://doi.org/10.1007/s11227-024-06345-w.
[27] Merzouk, M.A., Neal, C., Delas, J. et al. “Adversarial robustness of deep reinforcement learning-based intrusion detection”. – In: International Journal of Information Security August 2024 Volume 23, pages 3625–3651. https://doi.org/10.1007/s10207-024-00903-2.
[28] Maseno, Jain, T., Gupta, C. (2022). Multi-Agent Intrusion Detection System Using Sparse PSO K-Mean Clustering and Deep Learning. “International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems”. Springer, Singapore. https://doi.org/10.1007/978-981-16-6332-1_10.
[29] Bhattacharya, S., S, S. R. K., Maddikunta, P. K. R., Kaluri, R., Singh, S., Gadekallu, T. R., Alazab, M., & Tariq, U. (2020). A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU. Electronics, 9(2), 219. https://doi.org/10.3390/electronics9020219.
[30] Amaouche, S., AzidineGuezzaz, Benkirane, S. et al. IDS-XGbFS: a smart intrusion detection system using XGboostwith recent feature selection for VANET safety. Cluster Comput 27, 3521–3535 (2024). https://doi.org/10.1007/s10586-023-04157-w.
[31] Amaouche, S., AzidineGuezzaz, Benkirane, S. et al. IDS-XGbFS: a smart intrusion detection system using XGboostwith recent feature selection for VANET safety. Cluster Comput 27, 3521–3535 (2024). https://doi.org/10.1007/s10586-023-04157-w.
[32] Pourahmad, Zahra, Hooshmand, R.,Madani,S. Mohammad. (2024). “Strengthening of Power Grid Protection Systems Against Cyber-Attacks: A Comprehensive Review” Iranian Journal of Electrical and Computer Engineering.
[33] Abolfazl Sajadi,Bijan Alizadeh, (2024). “SQ-PUF: A Resistant PUF-Based Authentication Protocol against Machine-Learning Attack” Iranian Journal of Electrical and Computer Engineering.
[34] Boshra Pishgoo, Ahmad akbari azirani. (2022). “Improving IoT Botnet Anomaly Detection Based on Dynamic Feature Selection and Hybrid Processing”, Iranian Journal of Electrical and Computer Engineering, B- Computer Engineering, Issue 2.