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In the recent years, cybersecurity has gained high relevance, converting thedetection of attacks or intrusions into a key task. In fact, a small breach ina system, application, or network, can cause huge damage for the companies.However, when this attack detection encounters the Artificial Intelligenceparadigm, it can be addressed using high-quality classifiers which often needhigh resource demands in terms of computation or memory usage. This situationhas a high impact when the attack classifiers need to be used with limitedresourced devices or without overloading the performance of the devices, as ithappens for example in IoT devices, or in industrial systems. For overcomingthis issue, NBcoded, a novel light attack classification tool is proposed inthis work. NBcoded works in a pipeline combining the removal of noisy dataproperties of the encoders with the low resources and timing consuming obtainedby the Naive Bayes classifier. This work compares three different NBcodedimplementations based on three different Naive Bayes likelihood distributionassumptions (Gaussian, Complement and Bernoulli). Then, the best NBcoded iscompared with state of the art classifiers like Multilayer Perceptron andRandom Forest. Our implementation shows to be the best model reducing theimpact of training time and disk usage, even if it is outperformed by the othertwo in terms of Accuracy and F1-score (~ 2%).

NBcoded: network attack classifiers based on Encoder and Naive Bayes model for resource limited devices