Showing papers by "Antonio Sánchez-Esguevillas published in 2023"
TL;DR: In this article , the authors proposed a network intrusion detector based on a shallow neural network (NN) architecture following a novel contrastive learning scheme, where both the network features and the labels are projected into a common representation (embedding) space where a similarity score is defined.
Abstract: IoT networks connect dispersed and heterogeneous devices with high economic impact, making them an important cybersecurity target. The changing nature of cybersecurity attacks requires intrusion detectors with the ability to detect new attacks. Our proposed model is a network intrusion detector created specifically for the needs of IoT networks with the aim of detecting previously unseen attacks. It is based on a shallow neural network (NN) architecture following a novel contrastive learning scheme. In this scheme, both the network features and the labels are projected into a common representation (embedding) space where a similarity score is defined. The labels act as a prototype for each type of traffic in embedding space, and classification is based on the proximity of samples to these class prototypes. The dimensionality and structure of the embedding space are critical. In this work, we explore the advantages of having an embedding space with expanded dimensionality using a kernel approximation technique (Random Fourier Features) that is integrated and learned within the NN. To avoid overfitting, we investigate the importance of various regularization techniques (L2 and contractive). The resulting model is tested against three network intrusion detection data sets to assess its ability to detect known and unknown attacks (zero-shot learning). The experimental results show a higher ability of the proposed model to detect unknown attacks than similar models and alternative machine learning models, in the literature.
1 citations
TL;DR: In this paper , a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN), for contextual embedding and modelling interactions among tabular features.
Abstract: All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record is composed of a number of heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted a major breakthrough for AI in fields related to human skills like natural language processing, but its applicability to tabular data has been more challenging. More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better. This paper presents a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN), for contextual embedding and modelling interactions among tabular features. Its results outperform those of a recently published survey with DL benchmark based on five public datasets, also achieving competitive results when compared to boosted-tree solutions.