Designing An Intelligent Intrusion Detection System (Ids) Using Neural Networks To Detect Zero-Day Threats
DOI:
https://doi.org/10.29284/z8c6cq50Keywords:
Zero-Day Attacks, Intrusion Detection Systems, Autoencoder, Random Forest, Anomaly Detection, Machine Learning.Abstract
The ever-growing speed of developing the network infrastructures, along with an unparalleled growth of the load of traffic flows and their complexity, has made the systems vulnerable to the advanced cyber threats. Among such, zero-day attacks present one of the most severe threats to the security requirements of a particular cybersecurity environment, being based on new vulnerabilities that may resist the signature-based defense mechanisms. The conventional intrusion detection systems (IDS) are usually unable to scale, support high-dimensional traffic, and generalize thus proving inefficient in dynamic and real-life situations.
In an attempt to overcome these issues, this research paper presents an ensemble-based intrusion detection model that will combine an autoencoder neural network and a Random Forest classifier. The suggested methodology is tested on large-scale, heterogeneous network traffic data which consists of millions of records constituting various types of attacks.
results show that the ensemble model is almost optimal and has almost 99 percent accuracy, precision, recall and F1-score of known attacks. Furthermore, when training the model to zero-day attacks in which a specific set of attack types is omitted during the training process the model still has high recall (95-97%).
References
[1] Ali, Shamshair, et al. "Comparative evaluation of ai-based techniques for zero-day attacks detection." Electronics 11.23 (2022): 3934.
[2] Sarhan, Mohanad, et al. "From zero-shot machine learning to zero-day attack detection." International Journal of Information Security 22.4 (2023): 947-959.
[3] Guo, Yang. "A review of machine learning-based zero-day attack detection: Challenges and future directions." Computer communications 198 (2023): 175-185.
[4] Zoppi, Tommaso, Andrea Ceccarelli, and Andrea Bondavalli. "Unsupervised algorithms to detect zero-day attacks: Strategy and application." Ieee Access 9 (2021): 90603-90615.
[5] Zahoora, Umme, et al. "Zero-day ransomware attack detection using deep contractive autoencoder and voting based ensemble classifier." Applied Intelligence 52.12 (2022): 13941-13960.
[6] Kumar, Vikash, and Ditipriya Sinha. "A robust intelligent zero-day cyber-attack detection technique." Complex & Intelligent Systems 7.5 (2021): 2211-2234.
[7] Deldar, Fatemeh, and Mahdi Abadi. "Deep learning for zero-day malware detection and classification: A survey." ACM Computing Surveys 56.2 (2023): 1-37.
[8] Vanin, Patrick, et al. "A study of network intrusion detection systems using artificial intelligence/machine learning." Applied Sciences 12.22 (2022): 11752.
[9] Rani, Deepa, and Narottam Chand Kaushal. "Supervised machine learning based network intrusion detection system for Internet of Things." 2020 11th International conference on computing, communication and networking technologies (ICCCNT). IEEE, 2020.
[10] Rethinavalli, S., and R. Gopinath. "Botnet attack detection in internet of things using optimization techniques." International Journal of Electrical Engineering and Technology 11.10 (2020): 412-420.
[11] Hindy, Hanan, et al. "Utilising deep learning techniques for effective zero-day attack detection." Electronics 9.10 (2020): 1684.
[12] Adefemi Alimi, Kuburat Oyeranti, et al. "Refined LSTM based intrusion detection for denial-of-service attack in Internet of Things." Journal of sensor and actuator networks 11.3 (2022): 32.
[13] Kumar, Vikash, and Ditipriya Sinha. "A robust intelligent zero-day cyber-attack detection technique." Complex & Intelligent Systems 7.5 (2021): 2211-2234.
[14] Alkahtani, Hasan, and Theyazn HH Aldhyani. "Botnet Attack Detection by Using CNN‐LSTM Model for Internet of Things Applications." Security and Communication Networks 2021.1 (2021): 3806459.
[15] Rodríguez, Eva, et al. "Transfer-learning-based intrusion detection framework in IoT networks." Sensors 22.15 (2022): 5621.
[16] https://www.kaggle.com/datasets/dhoogla/cictoniot
[17] Obi, Jude Chukwura. "A comparative study of several classification metrics and their performances on data." World Journal of Advanced Engineering Technology and Sciences 8.1 (2023): 308-314.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
