  In this paper, a deep neural network approach and a neuro-symbolic one are
proposed for classification and regression. The neuro-symbolic predictive
models based on Logic Tensor Networks are capable of discriminating and in the
same time of explaining the characterization of bad connections, called alerts
or attacks, and of normal connections. The proposed hybrid systems incorporate
both the ability of deep neural networks to improve on their own through
experience and the interpretability of the results provided by symbolic
artificial intelligence approach. To justify the need for shifting towards
hybrid systems, explanation, implementation, and comparison of the dense neural
network and the neuro-symbolic network is performed in detail. For the
comparison to be relevant, the same datasets were used in training and the
metrics resulted have been compared. A review of the resulted metrics shows
that while both methods have similar precision in their predictive models, with
Logic Tensor Networks being also possible to have interactive accuracy and
deductive reasoning over data. Other advantages and disadvantages such as
overfitting mitigation and scalability issues are also further discussed.
