A Perceptive Analysis of Machine Learning Techniques for Enhancing Cybersecurity Intrusion Detection Systems
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This paper is composed of a literature review discussing the methods of improving Intrusion Detection Systems (IDS) using the UNSW-NB15 dataset to predict intrusion. The traditional IDS has the disadvantage of having too many false positives in detecting new threats. Supervised algorithms, including the Random Forest, performed well of 95.2 to eradicate all 0-day attacks and 85% of unsupervised autoencoders, as compared to the composite of the supervised and unsupervised encoders with a score of 94.8. False positives decreased to 4.2, and it supported high-rate operations at the network. Therefore, the datasets cannot be effortlessly represented, and even some tasks can be computed, although the situation has been improved. This study provides a sound ML-based IDS model that is more precise and versatile and has the potential for direct effects with regard to the implementation of cybersecurity in the real world.
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Dini, P., Elhanashi, A., Begni, A., Saponara, S., Zheng, Q. and Gasmi, K., 2023. Overview on intrusion detection systems design exploiting machine learning for networking cybersecurity. Applied Sciences, 13(13), p.7507. Retrieved from:https://www.mdpi.com/2076-3417/13/13/7507/pdf [Retrieved on: 17.10.2025]
Markevych, M. and Dawson, M., 2023, June. A review of enhancing intrusion detection systems for cybersecurity using artificial intelligence (ai). In International conference knowledge-based organization (Vol. 29, No. 3, pp. 30-37). Retrieved from : https://sciendo.com/2/v2/download/article/10.2478/kbo-2023-0072.pdf [Retrieved on: 17.10.2025]
Nabi, F. and Zhou, X., 2024. Enhancing intrusion detection systems through dimensionality reduction: A comparative study of machine learning techniques for cyber security. Cyber Security and Applications, 2, p.100033. Retrieved from :https://www.academia.edu/download/120904040/pdf.pdf [Retrieved on: 17.10.2025]
Alharbi, A., Seh, A.H., Alosaimi, W., Alyami, H., Agrawal, A., Kumar, R. and Khan, R.A., 2021. Analyzing the impact of cyber security related attributes for intrusion detection systems. Sustainability, 13(22), p.12337. Retrieved from : https://www.mdpi.com/2071-1050/13/22/12337 [Retrieved on: 17.10.2025]
Shyaa, M.A., Ibrahim, N.F., Zainol, Z., Abdullah, R., Anbar, M. and Alzubaidi, L., 2024. Evolving cybersecurity frontiers: A comprehensive survey on concept drift and feature dynamics aware machine and deep learning in intrusion detection systems. Engineering Applications of Artificial Intelligence, 137, p.109143. Retrieved from : https://www.sciencedirect.com/science/article/pii/S0952197624013010 [Retrieved on: 17.10.2025]
Sadaram, G., Sakuru, M., Karaka, L.M., Reddy, M.S., Bodepudi, V., Boppana, S.B. and Maka, S.R., 2022. Internet of Things (IoT) Cybersecurity Enhancement through Artificial Intelligence: A Study on Intrusion Detection Systems. Universal Library of Engineering Technology, (Issue). Retrieved from: https://ulopenaccess.com/papers/ULETE_SV01/ULETE2022SI_001.pdf [Retrieved on: 17.10.2025]
Ferrag, M.A., Shu, L., Friha, O. and Yang, X., 2021. Cyber security intrusion detection for agriculture 4.0: Machine learning-based solutions, datasets, and future directions. IEEE/CAA Journal of Automatica Sinica, 9(3), pp.407-436. Retrieved from : https://www.ieee-jas.net/article/id/979c0935-23fb-4117-9b2d-05b925510504?viewType=HTML&pageType=en [Retrieved on: 17.10.2025]
Pooja, T.S. and Shrinivasacharya, P., 2021. Evaluating neural networks using Bi-Directional LSTM for network IDS (intrusion detection systems) in cyber security. Global Transitions Proceedings, 2(2), pp.448-454. Retrieved from :https://www.sciencedirect.com/science/article/pii/S2666285X21000455 [Retrieved on: 17.10.2025]
Pinto, A., Herrera, L.C., Donoso, Y. and Gutierrez, J.A., 2023. Survey on intrusion detection systems based on machine learning techniques for the protection of critical infrastructure. Sensors, 23(5), p.2415. Retrieved from : https://www.mdpi.com/1424-8220/23/5/2415 [Retrieved on: 17.10.2025]
Akgun, D., Hizal, S. and Cavusoglu, U., 2022. A new DDoS attacks intrusion detection model based on deep learning for cybersecurity. Computers & Security, 118, p.102748.Retrieved from : https://acikerisim.subu.edu.tr/yayinaea/ e0e6b57a78ff79e0cd36be2cc6723cb1180586827201979da083b805bd24871c111.pdf [Retrieved on: 17.10.2025]
Pascale, F., Adinolfi, E.A., Coppola, S. and Santonicola, E., 2021. Cybersecurity in automotive: An intrusion detection system in connected vehicles. Electronics, 10(15), p.1765. Retrieved from : https://www.mdpi.com/2079-9292/10/15/1765 [Retrieved on: 17.10.2025]
Satilmiş, H., Akleylek, S. and Tok, Z.Y., 2024. A systematic literature review on host-based intrusion detection systems. Ieee Access, 12, pp.27237-27266. Retrieved from :https://ieeexplore.ieee.org/iel7/6287639/10380310/10439152.pdf [Retrieved on: 17.10.2025]
Dash, B., Ansari, M.F., Sharma, P. and Ali, A., 2022. Threats and opportunities with AI-based cyber security intrusion detection: a review. International Journal of Software Engineering & Applications (IJSEA), 13(5). Retrieved from :https://www.mdpi.com/1999-5903/15/2/62 [Retrieved on: 17.10.2025]
Apruzzese, G., Andreolini, M., Ferretti, L., Marchetti, M. and Colajanni, M., 2022. Modeling realistic adversarial attacks against network intrusion detection systems. Digital Threats: Research and Practice (DTRAP), 3(3), pp.1-19. Retrieved from :https://scholar.google.com/scholar?output=instlink&q=info:YAyTMuflFQ4J: scholar.google.com/ &hl= en&as_sdt=0,5&as_ylo=2021&scillfp=12045959072546234233&oi=lle [Retrieved on: 17.10.2025]
Alsirhani, A., Alshahrani, M.M., Hassan, A.M., Taloba, A.I., Abd El-Aziz, R.M. and Samak, A.H., 2023. Implementation of African vulture optimization algorithm based on deep learning for cybersecurity intrusion detection. Alexandria Engineering Journal, 79, pp.105-115.Retrieved from:https://www.sciencedirect.com/science/article/pii/S1110016823006671[Retrieved on: 17.10.2025]
Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A. and Anwar, A., 2020. TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems. Ieee Access, 8, pp.165130-165150.Retrieved from:https://ieeexplore.ieee.org/iel7/6287639/8948470/09189760.pdf[Retrieved on: 17.10.2025]
Kandhro, I.A., Alanazi, S.M., Ali, F., Kehar, A., Fatima, K., Uddin, M. and Karuppayah, S., 2023. Detection of real-time malicious intrusions and attacks in IoT empowered cybersecurity infrastructures. IEEE Access, 11, pp.9136-9148.Retrieved from:https://ieeexplore.ieee.org/iel7/6287639/6514899/10023499.pdf[Retrieved on: 17.10.2025]
Yadulla, A.R., Kasula, V.K., Yenugula, M. and Konda, B., 2023. Enhancing Cybersecurity with AI: Implementing a Deep Learning-Based Intrusion Detection System Using Convolutional Neural Networks. European Journal of Advances in Engineering and Technology, 10(12), pp.89-98.Retrieved from:https://www.researchgate.net/profile/Vinay-Kumar-Kasula/publication/385214025_Enhancing_Cybersecurity_with_AI_Implementing_a_Deep_LearningBased_Intrusion_Detection_System_Using_Convolutional_Neural_Networks_European_Journal_of_Advances_in_Engineering_and_Technology_2023_10128/links/671adfe0393e8533f715a84b/Enhancing-Cybersecurity-with-AI-Implementing-a-Deep-Learning-Based-Intrusion-Detection-System-Using-Convolutional-Neural-Networks-European-Journal-of-Advances-in-Engineering-and-Technology-2023-101.pdf[Retrieved on: 17.10.2025]
Gopalsamy, M., 2021. Enhanced Cybersecurity for Network Intrusion Detection System Based Artificial Intelligence (AI) Techniques. Int. J. Adv. Res. Sci. Commun. Technol, 12(01), pp.671-681.Retrieved from: https://www.researchgate.net/profile/Mani-Gopalsamy-2/publication/385614532_Enhanced Cybersecurity for Network Intrusion_Detection_System_Based_Artificial_Intelligence_AI_Techniques/links/67746efbc1b01354650698dd/Enhanced-Cybersecurity-for-Network-Intrusion-Detection-System-Based-Artificial-Intelligence-AI-Techniques.pdf[Retrieved on: 17.10.2025]
Ashiku, L. and Dagli, C., 2021. Network intrusion detection system using deep learning. Procedia Computer Science, 185, pp.239-247.Retrieved from:https://www.sciencedirect.com/science/article/ pii/S1877050921011078/ pdf?md5=a29d927241adb5b95cf867b98d641e1e&pid=1-s2.0-S1877050921011078-main.pdf[Retrieved on: 17.10.2025]
Hady, A.A., Ghubaish, A., Salman, T., Unal, D. and Jain, R., 2020. Intrusion detection system for healthcare systems using medical and network data: A comparison study. IEEE Access, 8, pp.106576-106584.Retrieved from:https://ieeexplore.ieee.org/iel7/6287639/6514899/09109651.pdf[Retrieved on: 17.10.2025]
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