Assessment of Zero-Day Vulnerability using Machine Learning Approach
DOI:
https://doi.org/10.4108/eetiot.4978Keywords:
zero-day vulnerabilities, machine learning, autoencoder model, neural network, intrusion detectionAbstract
Organisations and people are seriously threatened by zero-day vulnerabilities because they may be utilised by attackers to infiltrate systems and steal private data. Currently, Machine Learning (ML) techniques are crucial for finding zero-day vulnerabilities since they can analyse huge datasets and find patterns that can point to a vulnerability. This research’s goal is to provide a reliable technique for detecting intruders and zero-day vulnerabilities in software systems. The suggested method employs a Deep Learning (DL) model and an auto-encoder model to find unusual data patterns. Additionally, a model for outlier detection that contrasts the autoencoder model with the single class-based Support Vector Machine (SVM) technique will be developed. The dataset of known vulnerabilities and intrusion attempts will be used to train and assess the models.
Downloads
References
Kaloudi, Nektaria, Li, Jingyue. The AI-Based Cyber Threat Landscape: A Survey. ACM Computing Surveys (CSUR). 2020; 53(1):1-34. DOI: https://doi.org/10.1145/3372823
Rajesh KP, Santhi P. Unified DL approach for Efficient IDS using Integrated Spatial–Temporal Features. Knowledge-Based Systems. 2021; 226:107-132.
Chen P, Lin C, Schölkopf B. A Tutorial on ν-support vector machines. Applied Stochastic Models in Business and Industry. 2005; 21:111-136. DOI: https://doi.org/10.1002/asmb.537
Jayachitra S, Prasanth A, Rafi, Shaik Mohammad, Zulaikha Beevi S. Hierarchical-Based Binary Moth Flame Optimization for Feature Extraction in Biomedical Application. In: Khare, Nilay, Tomar, Deepak S, Ahirwal, Mitul K, Semwal, Vijay B, Soni, Vaibhav, editors. Machine Learning, Image Processing, Network Security and Data Sciences. Proceedings of the 4th International Conference on ML, Image Processing, Network Security and Data Sciences; 2022. Springer Nature Switzerland; 2022. p. 27-38. DOI: https://doi.org/10.1007/978-3-031-24352-3_3
Jayachitra S, Prasanth A, Hariprasath S, Benazir Begam R, Madiajagan M. In AI Models for Blockchain-Based Intelligent Networks in IoT Systems: Concepts, Methodologies, Tools, and Applications. Springer International Publishing; 2023. Chapter 7, AI Enabled Internet of Medical Things in Smart Healthcare; pp. [141-161]. DOI: https://doi.org/10.1007/978-3-031-31952-5_7
Kavitha M, Roobini S, Prasanth A, Sujaritha M. Machine Learning and Artificial Intelligence in Healthcare Systems. 1st Edition. Boca Raton: CRC Press; 2023. Systematic View and Impact of Artificial Intelligence in Smart Healthcare Systems; pp. [25-56].
Bamidele, Awotunde, Chakraborty, Chinmay, Adeniyi, Emmanuel. Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection. Wireless Communications and Mobile Computing. 2021; 2021:1-17. DOI: https://doi.org/10.1155/2021/7154587
Peppes N, Alexakis T, Adamopoulou E, Demestichas K. The Effectiveness of Zero-Day Attacks Data Samples Generated via GANs on Deep Learning Classifiers. Sensors. 2023; 23:900. DOI: https://doi.org/10.3390/s23020900
Deldar F, Abadi M Deep Learning for Zero-day Malware Detection and Classification: A Survey. ACM Comput. Surv. 2023; 56(2):36. DOI: https://doi.org/10.1145/3605775
Pattawaro, Apichit, Polprasert, Chantri. Anomaly-Based Network Intrusion Detection System through Feature Selection and Hybrid Machine Learning Technique. In: Proceedings of the ICT Knowledge Engineering (ICTKE) Conference; November 2018. p. 1-6. DOI: https://doi.org/10.1109/ICTKE.2018.8612331
Musleh D, Alotaibi M, Alhaidari F, Rahman A, Mohammad RM. Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT. Journal of Sensor and Actuator Networks. 2023; 12(2):29. DOI: https://doi.org/10.3390/jsan12020029
Priyatharishini M, Nirmala. A DL-based malicious module identification using stacked sparse autoencoder network for VLSI circuit reliability. In: Measurement (Ed.). Measurement: Proceedings of the Elsevier Conference, 15 May 2022. International Measurement Confederation (IMEKO); 2022. p. 18. DOI: https://doi.org/10.1016/j.measurement.2022.111055
Lirim A., Cihan D. Network IDS using DL. Procedia Computer Science. 2021; 185:239-247. DOI: https://doi.org/10.1016/j.procs.2021.05.025
Ali S, Rehman SU, Imran A, Adeem G, Iqbal Z, Kim KI. Comparative Evaluation of AI-Based Techniques for Zero-Day Attacks Detection. Electronics. 2022; 11:1-17. DOI: https://doi.org/10.3390/electronics11233934
Li P, Pei Y, Li J. A comprehensive survey on design and application of autoencoder in deep learning. Appl. Soft Comput. 138(C):21. DOI: https://doi.org/10.1016/j.asoc.2023.110176
Rushdan, Huthifh, Shurman, Mohammad, Alnabelsi, Sharhabeel, Qutaibah, Althebyan. Zero-Day Attack Detection and Prevention in Software-Defined Networks. In: Proceedings of the Advanced Computer and Information Technology (ACIT) Conference, December 2019.
Akash S, Prabahara P, Vijay K, Soman KP. A Detailed Investigation and Analysis of DL Architectures and Visualization Techniques for Malware Family Identification. Cybersecurity and Secure Information Systems. 2019; 17:241-286. DOI: https://doi.org/10.1007/978-3-030-16837-7_12
Tavallaee M, Bagheri E, Lu W, Ghorbani A. A detailed analysis of the KDD CUP 99 data set. In: Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA). 2009. p. 30-36. DOI: https://doi.org/10.1109/CISDA.2009.5356528
Kanna P, Rajesh, Santhi P. Hybrid Intrusion Detection using MapReduce based Black Widow Optimized Convolutional Long Short-Term Memory Neural Networks. Expert Systems with Applications. 2022; 194:27-43. DOI: https://doi.org/10.1016/j.eswa.2022.116545
Rezaei S, Liu X. Deep Learning for Encrypted Traffic Classification: An Overview. IEEE Communications Magazine. 2019; 57(1):76-81. DOI: https://doi.org/10.1109/MCOM.2019.1800819
Aceto G, Ciuonzo D, Montieri A, Pescapè A. Toward Effective Mobile Encrypted Traffic Classification through Deep Learning. Neurocomputing. 2020; 409. DOI: https://doi.org/10.1016/j.neucom.2020.05.036
Liashchynskyi P, Liashchynskyi P. Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS. arXiv. 2019:1-8.
Abri F, Siami-Namini S, Khanghah MA, Soltani FM, Namin AS. Can Machine/Deep Learning Classifiers Detect Zero-Day Malware with High Accuracy In: Proceedings of the 2019 IEEE International Conference on Big Data (Big Data); December 2019; Los Angeles, CA, USA. p. 3252-3259. DOI: https://doi.org/10.1109/BigData47090.2019.9006514
Hindy H, Atkinson R, Tachtatzis C, Colin J-N, Bayne E, Bellekens X. Utilising Deep Learning Techniques for Effective Zero-Day Attack Detection. Electronics. 2020; 9(10):1684. DOI: https://doi.org/10.3390/electronics9101684
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Internet of Things
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
