An overview of cyber security data science from a perspective of machine learning

Machine learning tasks in cyber security

Machine learning (ML) is sometimes regarded as a subset of "Artificial Intelligence," and it is strongly related to data science, data mining, and computational statistics. It focuses on teaching computers to recognize patterns from data. Machine learning models, which could be crucial in the field of cyber security, often consist of a collection of rules, techniques, or intricate "transfer functions" that can be used to uncover interesting data patterns or to recognize or anticipate behavior. Here, we’ll go through various approaches for handling machine learning problems and how they relate to cyber security issues (Assistance, 2022).

Neural networks and deep learning

Deep learning is a type of machine learning, a subset of artificial intelligence that takes cues from biological neural networks seen in the human brain. The most widely used neural network algorithm is back propagation, and artificial neural networks (ANN) are extensively employed in deep learning (Aversano et al., 2021). It executes learning on an input layer, one or more hidden layers, and an output layer of a multi-layer feed-forward neural network. Deep learning performs better as the volume of security data increases, which is the primary distinction between it and traditional machine learning. Typically, deep learning algorithms work best with vast amounts of data, whereas machine learning techniques work well with smaller datasets.

Unsupervised learning

Finding patterns, frameworks, or knowledge in unlabeled data, or using a data-driven strategy, are the main objective in unsupervised learning problems. Malware, a form of cyber-attack, hides itself in some ways, changing its behavior constantly and autonomously to evade detection. Unsupervised learning methods like clustering can be used to extract hidden structures and patterns from datasets to find clues to such complex attacks. Similar to this, clustering approaches can be helpful in locating anomalies, finding and removing rules breaches, and noisy examples in data. The well-liked hierarchical clustering techniques employed in numerous application domains include single linkage or complete linkages, K-means, and K-medoids.

Conclusion and future work

The implementation of a strong framework that allows data-driven decision making is the most crucial task for a smart cyber security system (Assistance, 2021). To make such a framework capable of minimizing these problems and offering automated and intelligent security services, enhanced data analytics based on machine learning approaches must be taken into account. As a result, developing a data-driven security model for a specific security issue as well as related empirical evaluation to gauge the model’s efficacy and efficiency and determine its suitability for use in actual application domains may be future works.

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