Machine learning for intrusion detection and network performance

Book chapter


Ibrahim Abobaker and Ahmad Musa 2021. Machine learning for intrusion detection and network performance. in: 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud)
AuthorsIbrahim Abobaker and Ahmad Musa
Abstract

Security mechanisms constitute a vital part towards the design of a computer network in modern-day organisations. In particular, the implementation of the principle of layered security to harden the network against attacks requires the introduction of checkpoints into the connectivity of components, which inevitably has an adverse impact on network performance. Moreover, advanced intrusion detection systems (IDSs) could be effectively utilised at the checkpoints of the computer network, leading to the analysis and determination of ‘optimal’ security versus performance trade-offs. To this end, a novel quantitative method is proposed for the evaluation and prediction of the aforementioned trade-offs supported by Machine Learning Algorithms (MLAs), such as Random Forest (RF) classifier, Logistic Regression (LR) and Naïve Bayes (NB) algorithms for Network Intrusion Detection Systems (NIDSs). In this context, a minimisation is employed in order to reduce the high dimensionality of datasets using Feature Selection (FS) for the dataset. Moreover, highly weighted features are used to keep false-negative (FN) low and increase the accuracy of MLAs towards the establishment of ‘optimal’ performance versus security tradeoffs. Typical numerical experiments are carried out indicating that the RF classifier is the best MLA, incorporating a subset of 19 selected features and identifying different types of attacks correctly with 99.9% of accuracy.

KeywordsComputer; Networks; Intrusion detection systems; Machine learning algorithms
Year2021
Book title2021 8th International Conference on Future Internet of Things and Cloud (FiCloud)
Output statusPublished
ISBN9781665425742
9781665425759
Publication dates
Online2021
Publication process dates
Deposited23 Jan 2023
Digital Object Identifier (DOI)https://doi.org/10.1109/FiCloud49777.2021.00020
Official URLhttps://ieeexplore.ieee.org/document/9590237
Event 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud)
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