Survey of intrusion detection systems: techniques, datasets and challenges
Cyber-attacks are becoming more sophisticated and thereby presenting increasing
challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade …
challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade …
Large language models on graphs: A comprehensive survey
Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …
advancements in natural language processing, due to their strong text encoding/decoding …
Graph contrastive learning automated
Self-supervised learning on graph-structured data has drawn recent interest for learning
generalizable, transferable and robust representations from unlabeled graphs. Among …
generalizable, transferable and robust representations from unlabeled graphs. Among …
Parameterized explainer for graph neural network
Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by
GNNs remains a challenging open problem. The leading method mainly addresses the local …
GNNs remains a challenging open problem. The leading method mainly addresses the local …
Simgrace: A simple framework for graph contrastive learning without data augmentation
Graph contrastive learning (GCL) has emerged as a dominant technique for graph
representation learning which maximizes the mutual information between paired graph …
representation learning which maximizes the mutual information between paired graph …
A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges
A Khraisat, A Alazab - Cybersecurity, 2021 - Springer
Abstract The Internet of Things (IoT) has been rapidly evolving towards making a greater
impact on everyday life to large industrial systems. Unfortunately, this has attracted the …
impact on everyday life to large industrial systems. Unfortunately, this has attracted the …
Weisfeiler and lehman go cellular: Cw networks
Abstract Graph Neural Networks (GNNs) are limited in their expressive power, struggle with
long-range interactions and lack a principled way to model higher-order structures. These …
long-range interactions and lack a principled way to model higher-order structures. These …
Self-supervised learning on graphs: Contrastive, generative, or predictive
Deep learning on graphs has recently achieved remarkable success on a variety of tasks,
while such success relies heavily on the massive and carefully labeled data. However …
while such success relies heavily on the massive and carefully labeled data. However …
Graph matching networks for learning the similarity of graph structured objects
This paper addresses the challenging problem of retrieval and matching of graph structured
objects, and makes two key contributions. First, we demonstrate how Graph Neural …
objects, and makes two key contributions. First, we demonstrate how Graph Neural …
Knowledge graph-enhanced molecular contrastive learning with functional prompt
Deep learning models can accurately predict molecular properties and help making the
search for potential drug candidates faster and more efficient. Many existing methods are …
search for potential drug candidates faster and more efficient. Many existing methods are …