Transforming Cybersecurity through Machine Learning: Opportunities and Limitation (KEY IJP************089)

• Naitik Pareek,Dr. Priya Mathur

Abstract

The rapid inception and emergence of machine learning (ML) as a powerful force in information security have brought into play many tools that will help automate threats detection, response to incidents, and mitigation of risk. This paper discusses current trends in applying ML to cybersecurity frameworks by exploring the trends opportunities, challenges, and ethical concerns. Algorithms fit for machine learning, such as random forests, convolutional neural networks, and reinforcement learning models, have shown an imposing efficiency in identifying dynamic cyber threats that require a response and that adapt to changing network conditions. These algorithms have exceptional properties in features such as anomaly detection, predictive maintenance, and real-time threat analysis. However, serious limitations remain for ML-based cybersecurity solutions, with emphasis on the issue of data dependencies, adversarial vulnerabilities, and algorithm bias that threatens the very well-being of its workings. There are also ethical and legal implications regarding the use of autonomous decision-making systems against accountability, fairness, and transparency. Using current knowledge, this paper reviews past studies, mathematical modeling, and performance metrics applicable to different machine and learning algorithms while addressing a call for advancement towards ML in cybersecurity along with tacking its own challenges.