Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms

Chang, Liyan; Branco, Paula

doi:10.48550/arxiv.2111.13597

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…Using four benchmark datasets, the simulation results show that the E-GraphSAGE performs better than ML-based classifiers such as XGBoost. Similarly, Chang and Branco [86] extend E-GraphSAGE by proposing an edge-based residual graph attention network (E-ResGAT). E-ResGAT uses an attention mechanism supporting edge features and embedding residual learning to enhance malicious traffic detection.…”

Section: Intrusive Detectionmentioning

confidence: 99%

Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities

Ngo,

Aouedi,

Piamrat

et al. 2023

Future Internet

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As the complexity and scale of modern networks continue to grow, the need for efficient, secure management, and optimization becomes increasingly vital. Digital twin (DT) technology has emerged as a promising approach to address these challenges by providing a virtual representation of the physical network, enabling analysis, diagnosis, emulation, and control. The emergence of Software-defined network (SDN) has facilitated a holistic view of the network topology, enabling the use of Graph neural network (GNN) as a data-driven technique to solve diverse problems in future networks. This survey explores the intersection of GNNs and Network digital twins (NDTs), providing an overview of their applications, enabling technologies, challenges, and opportunities. We discuss how GNNs and NDTs can be leveraged to improve network performance, optimize routing, enable network slicing, and enhance security in future networks. Additionally, we highlight certain advantages of incorporating GNNs into NDTs and present two case studies. Finally, we address the key challenges and promising directions in the field, aiming to inspire further advancements and foster innovation in GNN-based NDTs for future networks.

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Section: Intrusive Detectionmentioning

confidence: 99%

Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities

Ngo,

Aouedi,

Piamrat

et al. 2023

Future Internet

View full text Add to dashboard Cite

show abstract

“…the botnet malware try to launch DDoS attacks to the victims) as a more global view of the network and traffic flow is required. As a result, it is worth exploring the edge-based graph neural approaches such as E-GraphSAGE [40], E-ResGAT [41] to perform Android malware detection based on malicious network flows and combine with GNN-Based FCG's approaches for Android malware detection.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Graph Neural Network-based Android Malware Classification with Jumping Knowledge

Lo¹,

Layeghy²,

Sarhan³

et al. 2022

Preprint

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This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set of program functions and their interprocedural calls. Thus, this paper proposes a GNN-based method for Android malware detection by capturing meaningful intraprocedural call path patterns. In addition, a Jumping-Knowledge technique is applied to minimize the effect of the over-smoothing problem, which is common in GNNs. The proposed method has been extensively evaluated using two benchmark datasets. The results demonstrate the superiority of our approach compared to state-of-the-art approaches in terms of key classification metrics, which demonstrates the potential of GNNs in Android malware detection and classification.

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“…Graph representation learning is a fast growing area of research that can be applied to various applications such as telecommunication and molecular networks. Recently, GNNs have achieved state-of-the-art performance in cyberattack detection, such as network intrusion detection [4,20].…”

Section: Introductionmentioning

confidence: 99%

Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks

Caville¹,

Lo²,

Layeghy³

et al. 2022

Preprint

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This paper investigates Graph Neural Networks (GNNs) application for self-supervised intrusion and anomaly detection in computer networks. GNNs are a deep learning approach for graph-based data that incorporate graph structures into learning to generalise graph representations and output embeddings. As traffic flows in computer networks naturally exhibit a graph structure, GNNs are a suitable fit in this context. The majority of current implementations of GNN-based Network Intrusion Detection Systems (NIDSs) rely heavily on labelled network traffic which can not only restrict the amount and structure of input traffic, but also the NIDS' potential to adapt to unseen attacks. To overcome these restrictions, we present Anomal-E, a GNN approach to intrusion and anomaly detection that leverages edge features and graph topological structure in a self-supervised process. This approach is, to the best our knowledge, the first successful and practical approach to network intrusion detection that utilises network flows in a self-supervised, edge leveraging GNN. Experimental results on two modern benchmark NIDS datasets not only clearly display the improvement of using Anomal-E embeddings, rather than raw features, but also the potential Anomal-E has for detection on wild network traffic.

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Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms

Cited by 6 publications

References 21 publications

Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities

Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities

Graph Neural Network-based Android Malware Classification with Jumping Knowledge

Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks

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