Machine Learning in Cyber Security
Article Sidebar
Main Article Content
Abstract — A component of artificial intelligence (AI), machine learning (ML) enables computers to learn from historical data, identify trends, and make judgments with little to no assistance from humans. Protecting computers, smartphones, servers, networks, and data from malicious attacks is the goal of cyber security. There are two ways that machine learning and cyber security can work together: by protecting machine learning systems and by leveraging machine learning to enhance cyber security. This combination has the potential to improve cyber security technologies, detect unknown and novel threats (known as zero-day attacks), and lessen the need for human intervention. Protecting critical data and systems becoming more difficult as technology advances quickly. In order to improve cyber security, this project intends to use machine learning to develop three distinct systems.
Downloads
References
Anti-Phishing Working Group, “Phishing and Fraud solutions”. [Online]. Available: http://www.antiphishing.org/. [Accesses: April 4, 2013].
Bharadiya, J. P. (2023). A Comprehensive Survey of Deep Learning Techniques Natural Language Processing. European Journal of Technology, 7(1), 58 - 66. https://doi.org/10.47672/ejt.1473
Bharadiya, J. P. (2023). Convolutional Neural Networks for Image Classification. International Journal of Innovative Science and Research Technology, 8(5), 673 - 677. https://doi.org/10.5281/zenodo.7952031
Bharadiya, J. P., Tzenios, N. T., & Reddy, M. (2023). Forecasting of Crop Yield using Remote Sensing Data, Agrarian Factors and Machine Learning Approaches. Journal of Engineering Research and Reports, 24(12), 29–44. https://doi.org/10.9734/jerr/2023/v24i12858
Densham B. Three cyber-security strategies to mitigate the impact of a data breach. Netw Secur. 2015;2015(1):5–8.
Hariri RH, Fredericks EM, Bowers KM. Uncertainty in big data analytics: survey, opportunities, and challenges. J Big Data. 2019;6(1):44.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.