65. Introduction to Machine Learning in Cybersecurity
Introduction
As the digital world continues to evolve and expand, the importance of cybersecurity cannot be overstated. Among the numerous methodologies, technologies, and strategies employed to fortify our digital spaces, machine learning stands out as a key player. This lesson aims to provide an introduction to machine learning, its relevance, and how it applies to cybersecurity.
What is Machine Learning?
Simply put, machine learning (ML) refers to a type of artificial intelligence (AI) that essentially provides systems with the ability to learn, improve, and make decisions from data without being explicitly programmed to do so. It achieves this through pattern recognition, enabling machines to adapt to new scenarios and improve their performance over time[1].
Machine Learning and Cybersecurity: A Match Made in the Digital World
The impressive capabilities of machine learning make it ideal for cybersecurity applications. Due to the constantly evolving nature of cyber threats, traditional rule-based approaches can’t keep up. However, with its capacity for recognising patterns and adapting in real time, machine learning meets the task with aplomb.
Machine learning algorithms are excellent at detecting anomalies. Once trained on ‘normal behaviour’ data, they can easily flag activities deviating from this established pattern. This makes ML ideal for identifying potential security breaches[2].
Applications of Machine Learning in Cybersecurity
Primarily, Machine Learning can be utilised in cybersecurity in two main ways: threat detection and response orchestration.
1. Threat Detection:
Detecting threats and malicious activities is perhaps the most obvious application of ML in cybersecurity. By studying system logs, network traffic, and other parameters, ML can detect suspicious activities promptly. This can potentially prevent a cybersecurity breach, even when dealing with zero-day exploits[3].
2. Response Orchestration:
Response orchestration involves automating response actions to specific types of threats. Machine learning can be employed here to learn from previous incidents and respond more efficiently during future incidents. In this way, ML can help reduce response time drastically[4].
Final Remarks
The use of machine learning in cybersecurity presents a great potential for the future. As cyber threats become more complex and multifaceted, the need for adaptive, learning-based solutions becomes even more pressing. However, it’s important to remember that ML is simply a tool. A proper cybersecurity framework needs to include not only advanced technologies like ML and AI, but also basic measures like prompt software updates, strong password policies, and user cybersecurity awareness.
References:
[1] Samuel, A.L., (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 3(3), pp.210–229. https://ieeexplore.ieee.org/document/5389202
[2] Anderson, H.S., (2016). DeepDGA: Adversarially-Tuned Domain Generation and Detection. https://arxiv.org/abs/1610.01969
[3] Jordaney, R., et al. (2017) Transcend: Detecting Concept Drift in Malware Classification Models. https://dl.acm.org/doi/10.5555/3128572.3140448
[4] Kehl, S., (2017). Learning to Win: Statistical Sampling in Distributional Reinforcement Learning for the Cyber Security Domain. https://dl.acm.org/doi/10.1145/3067695.3082472
