Intelligent Detection and Categorization of Network Vulnerabilities Based on Advanced Machine Learning Techniques

Abstract

With the rapid advancement of network technologies and the exponential growth of internet traffic, network attacks have become increasingly common and sophisticated. A network attack refers to any unauthorized attempt to access, disrupt, or damage network resources, often resulting in severe operational and financial consequences. Traditionally, organizations have relied on conventional security mechanisms such as firewalls, encryption, and antivirus software to safeguard their systems. However, these defenses alone are insufficient to counter modern, evolving threats. To overcome these limitations, researchers have increasingly turned to intelligent computational models. Machine Learning (ML) and Deep Learning (DL), two prominent domains of Artificial Intelligence (AI), enable systems to learn from data and identify complex attack patterns with greater accuracy. This study presents a comprehensive review of various ML and DL techniques applied to the detection and classification of cyberattacks, highlighting their potential to strengthen network intrusion detection systems and improve overall cybersecurity resilience.

Introduction

The study focuses on network attack detection and classification, emphasizing the need to secure networks against unauthorized access and malicious activities. Network attacks may originate internally or externally and threaten systems in banking, e-commerce, healthcare, and government sectors. With the growth of cloud computing and networked services, the importance of network security has intensified. Key threats include Denial-of-Service (DoS) attacks and man-in-the-middle attacks, which disrupt services or intercept communications.

Intrusion Detection Systems (IDS), both host-based (HIDS) and network-based (NIDS), enhance security by monitoring network activity, detecting anomalies, and responding in real time. Traditional machine learning (ML) methods have limitations such as manual feature engineering, limited generalization, and adaptability issues, particularly when processing large-scale, dynamic network data.

Deep learning (DL) approaches—such as DNNs, CNNs, LSTMs, BiLSTMs, and GRUs—offer advantages by automatically learning hierarchical features from raw data, capturing complex patterns, and improving detection accuracy in real-time environments. DL is especially effective for active attacks, where attackers attempt to alter or disrupt systems.

Network attacks are broadly categorized into:

• Denial of Service (DoS): Overwhelms system resources, disrupting services.

• Remote-to-Local (R2L): Unauthorized remote access attempts.

• User-to-Root (U2R): Privilege escalation to gain administrative access.

• Probe attacks: Network scanning to identify vulnerabilities.

The problem statement highlights that traditional ML struggles with growing cyber threats due to feature selection challenges, low detection accuracy, and high false positive rates. Deep learning-based methods are proposed as a more robust solution to detect and classify sophisticated attacks efficiently, including those overlooked by conventional approaches.

Conclusion

Network attacks represent deliberate or unauthorized efforts to infiltrate, interrupt, or damage digital communication systems, often leading to serious operational and financial risks. Conventional defense mechanisms—such as firewalls, encryption standards, and antivirus software—continue to provide baseline protection; however, they are increasingly inadequate against the complexity and adaptability of modern threats. To counter these evolving challenges, researchers have embraced intelligent and data-centric security frameworks driven by Artificial Intelligence (AI). Machine Learning (ML) and Deep Learning (DL) have emerged as transformative tools that empower systems to recognize patterns, adapt to new behaviors, and detect abnormal activities in real time. Their ability to analyze massive datasets and uncover subtle correlations enables faster, more accurate attack identification compared to traditional rule-based systems. By integrating ML and DL into network defense, organizations can achieve proactive threat detection and reduce their reliance on static, reactive measures. This study presents a comprehensive evaluation of diverse ML and DL algorithms applied to network intrusion detection and vulnerability classification. The findings emphasize that intelligent learning models significantly enhance the accuracy, efficiency, and resilience of cybersecurity infrastructures, marking a crucial step toward autonomous and adaptive network protection.

References

[1] Abbas, S., Bouazzi, I., Ojo, S., Al Hejaili, A., Sampedro, G. A., Almadhor, A., & Gregus, M. (2024). Evaluating deep learning variants for cyber-attacks detection and multi-class classification in IoT networks. PeerJ Computer Science, 10, 1–23. https://doi.org/10.7717/peerj-cs.1793 [2] Aftergood, S. (2017). The Cold War Online. Nature, 547, 30–31. https://www.nature.com/articles/547030a [3] Ahmad, I., Imran, M., Qayyum, A., Ramzan, M. S., & Alassafi, M. O. (2023). An Optimized Hybrid Deep Intrusion Detection Model (HD-IDM) for Enhancing Network Security. Mathematics, 11(21). https://doi.org/10.3390/math11214501 [4] Al?shehari, T., & Alsowail, R. A. (2021). An insider data leakage detection using one?hot encoding, synthetic minority oversampling and machine learning techniques. Entropy, 23(10). https://doi.org/10.3390/e23101258 [5] Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. In Journal of Big Data (Vol. 8, Issue 1). Springer International Publishing. https://doi.org/10.1186/s40537-021-00444-8 [6] Anwer, M., Umer, M., Khan, S. M., & Waseemullah. (2021). Attack Detection in IoT using Machine Learning. Engineering, Technology and Applied Science Research, 11(3), 7273–7278. https://doi.org/10.48084/etasr.4202 [7] Bai, Y. (2022). RELU-Function and Derived Function Review. SHS Web of Conferences, 144, 02006. https://doi.org/10.1051/shsconf/202214402006 [8] Boehmke, B., & Greenwell, B. (2019). Hands-On Machine Learning with SKLerni, Keras and TensorFlow. In Hands-On Machine Learning with R. [9] Bonaparte, Y. (2024). Global Financial Stability Index. In SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2753667 [10] Butun, I., Morgera, S. D., & Sankar, R. (2014). A survey of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys and Tutorials, 16(1), 266–282. https://doi.org/10.1109/SURV.2013.050113.00191 [11] Chalapathy, R., & Chawla, S. (2019). Deep Learning for Anomaly Detection: A Survey. 1–50. http://arxiv.org/abs/1901.03407 [12] Chatterjee, A., & Ahmed, B. S. (2022). IoT anomaly detection methods and applications: A survey. Internet of Things (Netherlands), 19(October 2021), 100568. https://doi.org/10.1016/j.iot.2022.100568 [13] Churcher, A, Ullah, R, Ahmad, J, Ur Rehman, S, Masood, F, Gogate, M, Alqahtani, F, Nour, B & Buchanan, WJ 2021,An experimental analysis of attack classification using machine learning in IoT networks‘, Sensors, vol. 21, no. 2, p. 446. [14] Das, H. P., & Spanos, C. J. (2022). Improved dequantization and normalization methods for tabular data pre-processing in smart buildings. BuildSys 2022 - Proceedings of the 2022 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, 168–177. https://doi.org/10.1145/3563357.3564072 [15] De Lucia, M., Maxwell, P. E., Bastian, N. D., Swami, A., Jalaian, B., & Leslie, N. (2021). Machine learning raw network traffic detection. April, 24. https://doi.org/10.1117/12.2586114 [16] Hartwig, R. P., & Wilkinson, C. (2014). Cyber Risks?: the Growing. Insurance Information Institute, June, 1–27. https://doi.org/10.1726/IJNRD.17046 [17] G Ajeetha and G Madhu Priya. Machine learning based ddos attack detection. In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), volume 1, pages 1–5. IEEE, 2019. [18] Hsu, C. M., Hsieh, H. Y., Prakosa, S. W., Azhari, M. Z., & Leu, J. S. (2019). Using long-short-term memory based convolutional neural networks for network intrusion detection. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 264). Springer International Publishing. https://doi.org/10.1007/978-3-030-06158-6_9 [19] Hutchison, D. (2017). Barocchetto. In Oxford Art Online. https://doi.org/10.1093/gao/9781884446054.article.t006431 [20] Ieracitano, C., Adeel, A., Morabito, F. C., & Hussain, A. (2020). A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing, 387, 51–62. https://doi.org/10.1016/j.neucom.2019.11.016 [21] Iung, B. (2013). Cœur et grossesse. EMC - Traité de Médecine AKOS, 8(2), 1–4. https://doi.org/10.1016/s1634-6939(13)59289-1 [22] Judith, A., Kathrine, G. J. W., Silas, S., & J, A. (2023). Efficient Deep Learning-Based Cyber-Attack Detection for Internet of Medical Things Devices †. Engineering Proceedings, 59(1). https://doi.org/10.3390/engproc2023059139 [23] Kamyab, M., Liu, G., & Adjeisah, M. (2021). Attention-Based CNN and Bi-LSTM Model Based on TF-IDF and GloVe Word Embedding for Sentiment Analysis. Applied Sciences (Switzerland), 11(23). https://doi.org/10.3390/app112311255 [24] Kim, A, Park, M & Lee, DH 2020, AI-IDS: Application of deep learning to real-time web intrusion detection‘, In IEEE Access, vol. 8, pp. 70245-70261. [25] Konatham, B. R. (2023). a Secure and Efficient Iiot Anomaly Detection Approach Using a Hybrid Deep Learning Technique. [26] Kumar, R. (2023). An Overview of Computer Networking As an Introduction OF. July.\' [27] Lee, A., Wang, X., Nguyen, H., & Ra, I. (2018). A hybrid software defined networking architecture for next-generation IoTs. KSII Transactions on Internet and Information Systems, 12(2), 932–945. https://doi.org/10.3837/tiis.2018.02.024 [28] Mousa Al-Akhras, Mohammed Alawairdhi, Ali Alkoudari, and Samer Atawneh. Using machine learning to build a classification model for iot networks to detect attack signatures. Int. J. Comput. Netw. Commun.(IJCNC), 12:99–116, 2020. [29] Md Abdullah Al Ahasan, Mengjun Hu, and Nashid Shahriar. Ofmcdm/irf: A phishing website detection model based on optimized fuzzy multi-criteria decision-making and improved random forest. In 2023 Silicon Valley Cybersecurity Conference (SVCC), pages 1–8. IEEE, 2023. [30] Ni, M. (2023). A review on machine learning methods for intrusion detection system. Applied and Computational Engineering, 27(1), 57–64. https://doi.org/10.54254/2755-2721/27/20230148 [31] Pang, G., Shen, C., Cao, L., & Hengel, A. Van Den. (2021). Deep Learning for Anomaly Detection: A Review. ACM Computing Surveys, 54(2), 1–36. https://doi.org/10.1145/3439950 [32] Pattawaro, A., & Polprasert, C. (2018). Anomaly-Based Network Intrusion Detection System through Feature Selection and Hybrid Machine Learning Technique. https://doi.org/10.1109/ICTKE.2018.8612331 [33] Ramaswamy, S. L., & Chinnappan, J. (2022). RecogNet-LSTM+CNN: a hybrid network with attention mechanism for aspect categorization and sentiment classification. Journal of Intelligent Information Systems, 58(2), 379–404. https://doi.org/10.1007/s10844-021-00692-3 [34] Sarumi, OA, Adetunmbi, AO & Adetoye, FA 2020, Discovering computer networks intrusion using data analytics and machine intelligence‘, Scientific African, vol. 9. [35] Salih, A. A., Ameen, S. Y., Zeebaree, S. R. M., Sadeeq, M. A. M., Kak, S. F., Omar, N., Ibrahim, I. M., Yasin, H. M., Rashid, Z. N., & Ageed, Z. S. (2021). Deep Learning Approaches for Intrusion Detection. Asian Journal of Research in Computer Science, June, 50–64. https://doi.org/10.9734/ajrcos/2021/v9i430229 [36] Sahoo, KS, Tripathy, BK, Naik, K, Ramasubbareddy, S, Balusamy, B, Khari, M & Burgos, D 2020, An evolutionary SVM model for DDOS attack detection in software defined networks‘, IEEE Access, vol. 8, pp. 132502-132513

Copyright

Copyright © 2025 Tesfaye Workineh Dinegde, Karthikeyan K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.