Machine learning techniques for detecting intrusions in networks: A systematic review of the literature
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Abstract
Cybersecurity is one of the main challenges of the modern world due to the rapid technological advancement, which, although it has improved the quality of life, has also exposed networks to new threats. The objective of this study is to evaluate the impact of intrusion detection systems (IDS) on data protection and to analyze how these techniques have adapted to emerging threats, improving the detection of malicious activities. To achieve this, a systematic review of articles published between 2018 and 2024 in databases such as IEEE, ACM, ScienceDirect and Scopus was conducted, following Barbara Kitchenham's methodology, using the Parsifal tool to generate searches and formulate research questions. Initial results indicate a growing interest in the application of Machine Learning techniques for intrusion detection over the last six years, with a peak of publications in 2023, especially in the IEEE database, demonstrating a significant evolution in the effectiveness of these techniques to address cyber threats.
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