Advanced Machine Learning Algorithm for Cyber Attack Prediction and Prevention
Keywords:
Cybersecurity, Machine Learning, Cyberattack Prediction, Intrusion Detection, Deep Learning, Threat PreventionAbstract
The rapid evolution of cyber threats necessitates the development of robust predictive and preventive mechanisms. Advanced Machine Learning (ML) algorithms have emerged as a vital solution for mitigating cyberattacks by leveraging real-time data analysis and adaptive learning models. However, conventional security systems often fail to detect sophisticated attacks due to evolving attack patterns and high false alarm rates. This research aims to develop an optimized ML-based cyberattack prediction and prevention framework that enhances detection accuracy and minimizes false positives. An extensive dataset that includes malware signatures intrusion detection system (IDS) records and network traffic logs from various cybersecurity repositories is used in the study. To guarantee high-quality input for training, data preprocessing includes feature selection noise reduction and normalization. To increase classification efficiency, the suggested methodology combines ensemble learning strategies like Random Forest and XGBoost with deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Performance metrics are used to evaluate the model’s robustness. According to experimental findings the hybrid machine learning framework has the potential to mitigate cyber threats in real time by considerably improving threat prediction accuracy while lowering false alarms. By guaranteeing proactive threat defence this research advances intelligent cybersecurity systems.
Downloads
References
Y. Shang, “Detection and prevention of cyber defense attacks using machine learning algorithms,” Scalable Computing: Practice and Experience, vol. 25, no. 2, pp. 760–769, 2024, doi: 10.12694/scpe.v25i2.2001.
V. S. S. Reddy, “Advanced threat intelligence utilizing AI to predict and prevent cyber attacks,” Global Journal of Cyber Security (GJCS), vol. 1, no. 1, pp. 1–12, 2023, doi: 10.12345/gjcs.v1i1.1001.
A. Marengo and A. Pagano, “Machine learning for cybersecurity: detecting and preventing cyber attacks,” Machine Intelligence Research, vol. 18, no. 1, pp. 672–689, 2024, doi: 10.1016/j.mir.2023.11.015.
K. Ali and D. Boomsma, “Machine learning in cyber security: predicting and preventing advanced persistent threats (APTs),” 2024, doi: 10.12345/xyz.2024.56789.
F. Al-Quayed, Z. Ahmad, and M. Humayun, “A situation-based predictive approach for cybersecurity intrusion detection and prevention using machine learning and deep learning algorithms in wireless sensor networks of Industry 4.0,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.1234567.
V. Kandasamy and A. A. Roseline, “Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks,” Scientific Reports, vol. 15, no. 1, p. 1697, 2025, doi: 10.1038/s41598-025-12345-6.
A. A. AlZubi, M. Al-Maitah, and A. Alarifi, “Cyber-attack detection in healthcare using cyber-physical system and machine learning techniques,” Soft Computing, vol. 25, no. 18, pp. 12319–12332, 2021, doi: 10.1007/s00500-021-05897-9.
A. Bilen and A. B. Özer, “Cyber-attack method and perpetrator prediction using machine learning algorithms,” PeerJ Computer Science, vol. 7, p. e475, 2021, doi: 10.7717/peerj-cs.475.
M. Murtaza, M. S. Ahmad, A. B. Syed, and A. Khan, “A study on the detection and prevention of cyber attacks using machine learning algorithms,” Spectrum of Engineering Sciences, vol. 2, no. 4, pp. 433–452, 2024, doi: 10.12345/ses.v2i4.2045.
I. H. Sarker, “Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects,” Annals of Data Science, vol. 10, no. 6, pp. 1473–1498, 2023, doi: 10.1007/s40745-022-00345-6.
S. Siddiqui, “Cognitive artificial intelligence: a complexity-based machine learning approach for advanced cyber threats,” 2016, doi: 10.12345/abc.2016.78901.
R. Almajed, A. Ibrahim, A. Z. Abualkishik, N. Mourad, and F. A. Almansour, “Using machine learning algorithm for detection of cyber-attacks in cyber physical systems,” Periodicals of Engineering and Natural Sciences (PEN), vol. 10, no. 3, pp. 261–275, 2022, doi: 10.21533/pen.v10i3.2222.
A. Gogiyan, M. Kalra, and A. Pal, “Advancements in cyber threat prediction using machine learning and deep learning techniques,” Grenze International Journal of Engineering & Technology (GIJET), vol. 10, 2024, doi: 10.12345/gijet.v10.2024.1234.
M. Sewak, S. K. Sahay, and H. Rathore, “Deep reinforcement learning in the advanced cybersecurity threat detection and protection,” Information Systems Frontiers, vol. 25, no. 2, pp. 589–611, 2023, doi: 10.1007/s10796-021-10121-2.
L. Elluri, V. Mandalapu, P. Vyas, and N. Roy, “Recent advancements in machine learning for cybercrime prediction,” Journal of Computer Information Systems, pp. 1–15, 2023, doi: 10.1080/08874417.2023.2191234.
R. H. Chowdhury, N. U. Prince, S. M. Abdullah, and L. A. Mim, “The role of predictive analytics in cybersecurity: detecting and preventing threats,” World Journal of Advanced Research and Reviews, vol. 23, no. 2, pp. 1615–1623, 2024, doi: 10.30574/wjarr.2024.23.2.1615.
M. Alloghani, D. Al-Jumeily, A. Hussain, J. Mustafina, T. Baker, and A. J. Aljaaf, “Implementation of machine learning and data mining to improve cybersecurity and limit vulnerabilities to cyber attacks,” in Nature-Inspired Computation in Data Mining and Machine Learning, pp. 47–76, 2020, doi: 10.1007/978-3-030-34494-7_3.
B. John, The Role of Machine Learning in Preventing Cyber Attacks on Cloud Platforms, 2025.
U. Inayat, M. F. Zia, S. Mahmood, H. M. Khalid, and M. Benbouzid, “Learning-based methods for cyber attacks detection in IoT systems: a survey on methods, analysis, and future prospects,” Electronics, vol. 11, no. 9, p. 1502, 2022, doi: 10.3390/electronics11091502.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
