An adaptive hybrid feature representation framework for modelling of complex network attacks and classifications
*Arikatla AnnapurnaCorresponding authorarikatlaannapurna4@gmail.comDepartment of Computer Science & EngineeringAnnamalai UniversityAnnamalainagar, Tamil Nadu, 608002, IndiaView full profile → , M. Thillaikarasiselvathillai69@gmail.comDepartment of Computer Science & EngineeringAnnamalai UniversityAnnamalainagar, Tamil Nadu, 608002, IndiaView full profile → , K. Subba Reddymrsubbareddy@yahoo.comDepartment of Computer Science and EngineeringRajeev Gandhi Memorial College of Engineering and TechnologyNandyal, Andhra Pradesh, 518501, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Mar 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2481
- Pages:
- 3535–3544
Abstract
Network intrusion detection systems are playing a significant role in protecting communication infrastructures against sophisticated cyber-attacks. Existing machine learning classification methods are needed to achieve accurate classifications of network data. Network cyber-attacks are diverse and dynamic in nature. Thus, existing methods sometimes fail due to a lack of multi-view learning for cyber-attacks, which captures the complex, high-dimensional, and non-linear relationships inherent in network traffic data. These limitations lead to reduced detection accuracy and poor generalisation for diverse and evolving attack patterns. This paper proposes a Hybrid Feature Representation Framework (HFRF) that integrates multi-stage feature refinement, multi-view transformation, and adaptive feature selection for effective network data classification. The proposed framework initially performs variance- and mutual information-based filtering to retain relevant features, followed by dual transformations using Principal Component Analysis (PCA) and Non-Negative Matrix Factorization (NMF) to capture both global and localised data structures. These complementary representations are then fused and optimised using an ensemble-based feature-importance mechanism to select the most discriminative features dynamically. Extensive experiments are conducted on benchmark datasets, including CICIDS2018 and UNSW-NB15, to demonstrate the efficacy of the proposed method compared to other methods.
Keywords
Subject Classifications
References
1] A. M. Alsaffar, M. Nouri-Baygi, and H. M. Zolbanin, “Shielding networks: Enhancing intrusion detection with hybrid feature selection and stack ensemble learning,” Journal of Big Data, vol. 11, Art. no. 133 (2024), doi: 10.1186/s40537-024-00994-7.
[2] P. H. H. N. de Araujo, A. Silva, N. Ferraz Junior, F. Cabrini, A. Santiago, A. Guelfi, and S. Kofuji, “Impact of feature selection methods on the classification of DDoS attacks using XGBoost,” Journal of Communication and Information Systems, vol. 36, no. 1, pp. 200–214 (2021).
[3] M. A. Hossain, “Enhanced ensemble-based distributed denial-of-service (DDoS) attack detection with novel feature selection: A robust cybersecurity approach,” Artificial Intelligence Evolution, vol. 4, no. 2, pp. 165–186 (2023), doi: 10.37256/aie.4220233337.
[4] S. Ahmed, Z. A. Khan, S. M. Mohsin, S. Latif, S. Aslam, H. Mujlid, M. Adil, and Z. Najam, “Effective and efficient DDoS attack detection using deep learning algorithm, multi-layer perceptron,” Future Internet, vol. 15, no. 2, Art. no. 76 (2023), doi: 10.3390/fi15020076.
[5] J. Cheng, Y. Liu, X. Tang, V. S. Sheng, M. Li, and J. Li, “DDoS attack detection via multi-scale convolutional neural network,” Computers, Materials & Continua, vol. 62, no. 3, pp. 1317–1333 (2020).
[6] I. O. Lopes, D. Zou, F. A. Ruambo, S. Akbar, and B. Yuan, “Towards effective detection of recent DDoS attacks: A deep learning approach,” Security and Communication Networks, vol. 2021, Art. no. 5710028 (2021), doi: 10.1155/2021/5710028.
[7] A. D. Jadhav and V. Pellakuri, “Highly accurate and efficient two phase-intrusion detection system (TP-IDS) using distributed processing of HADOOP and machine learning techniques,” Journal of Big Data, vol. 8, Art. no. 131 (2021), doi: 10.1186/s40537-021-00521-y.
[8] S. S. Samaan and H. A. Jeiad, “Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 4, pp. 2302–2312 (2023).
[9] R. Abu Bakar, X. Huang, M. S. Javed, S. Hussain, and M. F. Majeed, “An intelligent agent-based detection system for DDoS attacks using automatic feature extraction and selection,” Sensors, vol. 23, no. 6, Art. no. 3333 (2023), doi: 10.3390/s23063333.
[10] M. S. Akter, H. Shahriar, I. Iqbal, M. D. Hossain, M. A. Karim, V. Clincy, and R. Voicu, “Exploring the vulnerabilities of machine learning and quantum machine learning to adversarial attacks using a malware dataset: A comparative analysis,” in Proc. 2023 IEEE Int. Conf. Software Services Engineering (SSE), pp. 222–231 (2023).
[11] I. H. Sarker, “CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks,” Internet of Things, vol. 14, Art. no. 100393 (2021), doi: 10.1016/j.iot.2021.100393.
[12] K. B. Dasari and N. Devarakonda, “Detection of different DDoS attacks using machine learning classification algorithms,” Ingénierie des Systèmes d’Information, vol. 26, no. 5, pp. 461–468 (2021), doi: 10.18280/isi.260505.
[13] S. Rajesh, M. Clement, S. S. B. Sooraj, S. H. Al Shifan, and J. Johnson, “Real-time DDoS attack detection based on machine learning algorithms,” pp. 1–5 (2021), doi: 10.2139/ssrn.3974241.
[14] M. J. Pasha, K. P. Rao, A. MallaReddy, and V. Bande, “LRDADF: An AI-enabled framework for detecting low-rate DDoS attacks in cloud computing environments,” Measurement: Sensors, vol. 28, Art. no. 100828 (2023), doi: 10.1016/j.measen.2023.100828.
[15] A. Aljuhani, “Machine learning approaches for combating distributed denial of service attacks in modern networking environments,” IEEE Access, vol. 9, pp. 42236–42264 (2021).
[16] F. Musumeci, A. C. Fidanci, F. Paolucci, F. Cugini, and M. Tornatore, “Machine-learning-enabled DDoS attacks detection in P4 programmable networks,” Journal of Network and Systems Management, vol. 30, no. 1, Art. no. 21 (2022), doi: 10.1007/s10922-021-09633-5.
[17] M. A. Hossain and M. S. Islam, “Ensuring network security with a robust intrusion detection system using ensemble-based machine learning,” Array, vol. 19, Art. no. 100306 (2023), doi: 10.1016/j.array.2023.100306.
[18] S. Sambangi and L. Gondi, “A machine learning approach for DDoS (distributed denial of service) attack detection using multiple linear regression,” Proceedings, vol. 63, no. 1, Art. no. 51 (2020).
[19] I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” in Proc. 4th International Conference on Information Systems Security and Privacy (ICISSP), Funchal, Madeira, Portugal, pp. 108–116 (2018).
[20] N. Moustafa and J. Slay, “UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems,” in Proc. 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, Australia, pp. 1–6 (2015), doi: 10.1109/MilCIS.2015.7348942.
[21] A. P. Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms,” Pattern Recognition, vol. 30, no. 7, pp. 1145–1159 (1997), doi: 10.1016/S0031-3203(96)00142-2.
[22] A. F. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. A. Przybocki, “The DET Curve in Assessment of Detection Task Performance,” in Proc. Eurospeech ’97, Rhodes, Greece, pp. 1895–1898 (1997).
[23] D. M. W. Powers, “Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation,” Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63 (2011).
[24] M. S. Basha, K. R. Prasad, S. K. Mouleeswaran, R. C. Poonia, and S. Sebastian, “Multi-disease detection system with X-ray images using deep learning techniques,” J. Inf. Optim. Sci., vol. 45, no. 5, pp. 1379–1388 (2024).
