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Published in:
2023 | OriginalPaper | Chapter
An Efficient Cybersecurity Framework for Detecting Network Attacks Using Deep Learning
Authors : K. R. Nataraj, Manasa, M. Chandana
Published in: ICDSMLA 2021
Publisher: Springer Nature Singapore
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Abstract
In the previous decade, many studies have been proposed on intrusion detection systems that leverage machine learning techniques for attack detection. The majority of research employs manually derived features. However, this approach is time consuming, and lot of information is lost from the original data leading to inaccurate results. A neural network of hybrid convolution and long short-term memory is proposed to detect intrusions using the CICIDS dataset. CNN is used to extract spatial features, and LSTM is used to extract temporal aspects of traffic network data, thus providing an intrusion detection strategy. Compared to KDDCup99 dataset, CICIDS2017 dataset is the latest and benchmark dataset that includes all the recent cyberattacks. The proposed framework has an overall accuracy of 99.45% and F1 score of 99.4%, with each attack type having an accuracy of above 99.50%.