Machine Learning for Cyber Threat Detection: Advancements and Challenges

Abstract

The evolution of cyber threats in today's interconnected digital landscape has necessitated the development of robust defense mechanisms. Machine Learning (ML) has emerged as a promising tool for cyber threat detection due to its capacity to analyze vast datasets and identify patterns indicative of potential attacks. This paper reviews the advancements and challenges in employing ML techniques for cyber threat detection.

Advancements in ML algorithms, particularly in supervised, unsupervised, and reinforcement learning, have enabled the creation of sophisticated models capable of recognizing anomalies, identifying malicious activities, and predicting potential threats in real-time. Additionally, the integration of deep learning methodologies such as neural networks has shown promising results in enhancing detection accuracy and scalability.

However, deploying ML for cyber threat detection also presents several challenges. Issues concerning the robustness of models against adversarial attacks, the need for labeled data for effective training, and the interpretability of complex ML models remain significant hurdles. Moreover, the rapid evolution of cyber threats demands continuous model adaptation and updates to stay ahead of sophisticated adversaries.

This paper examines these advancements and challenges in the application of ML for cyber threat detection, highlighting the need for further research to address the limitations and maximize the potential of machine learning in safeguarding digital infrastructures against cyber threats.