Exploring the Intersection of Machine Learning and Cybersecurity

By: Jampula Navaneeth¹

¹Vel Tech University, Chennai, India

²International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan Email: navaneethjampula@gmail.com

Abstract

This article will provide an understanding of the relationship between cybersecurity and machine learning. It is common knowledge that if there is anything about protecting against cybercrime, then it is founded on cyber security principles which include phishing, hacking etc. In order to combat every kind of fraud, especially on the internet, thus plays the most important part. Similarly, machine learning has a unique power to develop a computer system that can able to learn and adapt without any further instructions. So in this article we are going to see how machine learning and cybersecurity both intersect each other.

Keywords: Machine Learning, Cyber Security, Phishing, Computer systems

Introduction

Computer cybercrime has been on the rise in the recent past because of evolution in technology introduction of the internet. Since cybercrime may bring numerous and severe consequences, this issue became one of the most crucial for all the stakeholders, such as persons, companies, and states. There is a need to know the interconnection between these two areas, considering the increasing use of machine learning in various fields such as cybersecurity [1].

AI is an advanced intelligence that develops from experiencing new knowledge without being taught by a human or human authorities; one development of AI is Machine learning. It also included in the scope cyber security and natural language processing and was also used in computer vision. Thus, as the ability of helping companies solve cyber threats has become higher, the value of applying machine learning in cybersecurity also became greater [1].

ML in Cybersecurity

Study after study has triggered a fresh wave of anxiety around the use and potential misuse of machine learning in cybercrime, but as you grow up with tech that is pretty much older than the wheel or fire, it may bring back memories. AI, specifically under the branch of machine learning which makes use of statistical techniques to grant machines the ability to learn and improve from experience without being programmed by humans, has found its way into various sectors, include cybersecurity [2]. Using machine learning, cybercriminals can craft threat attacks more easily e.g., algorithms to study data for patterns and anomalies used in phishing campaigns, financial crimes etc. The integration of machine learning in cybercrime is problematic for cybersecurity as the algorithms themselves can be crafted to sidestep traditional security and as technology changes constantly, new tactics are difficult to counter [1].

Applications of ML in cybersecurity

The relevance of model training based upon machine learning has increased greatly as far as contesting cyber offences is concerned. These techniques can be applied to analyse huge amounts of information, find trends and outliers within that data, as well as record occurrences before they happen. As a result, it becomes an ideal approach towards recognizing and preventing attacks conducted through cyberspace while also improving general online safety measures [1].

Proactive Defence and Predictive Analysis

Another major benefit of the application of machine learning in cybersecurity is the ability of making predictions. In daily life, the ML model can analyse the history data to find out the future threats and vulnerabilities. Due to this characteristic, these models are useful in helping organizations understand the likelihood of being attacked at some time and put up precaution mechanisms in place [3].

It is also possible to forecast the risk of certain actions or changes within a network in its application [2]. For instance, when planning to implement an upgrade for a given application, an ML model can assess the probable risks on security that are likely to be realized, provided that the upgrade is implemented [3], [4].

Intersection of ML and Cybersecurity

The intersection of cybersecurity and machine learning refers to the use of machine learning algorithms and techniques by cyber criminals in execution of their evil deeds. There are new and more complex types of cyberattacks that have assumed recently as a result of increased usage of machine learning in cyberspace criminality [5]. There are many cyberattacks such as Advanced persistent threats, Phishing attacks, Fraud and Financial crimes are developing after implementing the ML models [1].

Conclusion

The convergence between machine learning and cyber security is a dynamic and evolving field, which has tremendous potential for the enhancement of digital assets protection. By capitalizing on ML’s ability in threat detection, predictions and automatic responses, organizations can improve their defence strategies while remaining ahead of new cyber threat’s development. But for this strong force to fully benefit from it all, then data quality issues; clarity-ness as well as ever-changing tactics adopted by cyber adversaries must be dealt with first.

References

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2. L. Triyono, R. Gernowo, P. Prayitno, M. Rahaman, and T. R. Yudantoro, “Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment,” JOIV : International Journal on Informatics Visualization, vol. 7, no. 3, pp. 726–732, Sep. 2023, doi: 10.30630/joiv.7.3.1243.
3. R. Colbaugh and K. Glass, “Proactive defense for evolving cyber threats,” in Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, Jul. 2011, pp. 125–130. doi: 10.1109/ISI.2011.5984062.
4. P. L. Bokonda, K. Ouazzani-Touhami, and N. Souissi, “Predictive analysis using machine learning: Review of trends and methods,” in 2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Nov. 2020, pp. 1–6. doi: 10.1109/ISAECT50560.2020.9523703.
5. “AI Safety and Security: Computer Science & IT Book Chapter | IGI Global.” Accessed: Oct. 04, 2024. [Online]. Available: https://www.igi-global.com/chapter/ai-safety-and-security/354401
6. Li, K. C., Gupta, B. B., & Agrawal, D. P. (Eds.). (2020). Recent advances in security, privacy, and trust for internet of things (IoT) and cyber-physical systems (CPS).
7. Chaudhary, P., Gupta, B. B., Choi, C., & Chui, K. T. (2020). Xsspro: Xss attack detection proxy to defend social networking platforms. In Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9 (pp. 411-422). Springer International Publishing.

Cite As

Navaneeth J. (2024) Exploring the Intersection of Machine Learning and Cybersecurity, Insights2Techinfo, pp.1

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