Advancing Network Security Through Deep Learning: A Hybrid Graph-Based and Temporal Approach to Anomaly and Threat Detection

Abstract

The rapid evolution of cyber threats demands ad- vanced intrusion detection systems capable of identifying sophis- ticated attacks that exploit both network topology and temporal patterns. This paper proposes a novel hybrid deep learning framework that synergistically combines graph neural networks (GNNs) for structural analysis and transformer models for tem- poral sequence processing, augmented with XGBoost for robust classification.Ourapproachintroducesthreekeyinnovations:(1) a graph attention network that models host communications and protocol dependencies, (2) a temporal transformer encoder that captures behavioral patterns across time windows, and (3)an uncertainty-based anomaly detection mechanism for identi- fyingzero-daythreats.EvaluatedontheCIC-IDS2023datasetthe most recent benchmark containing contemporary attack vectors like IoT-based DDoS and cloud exploitation patterns-our framework achieves 78.28% accuracy, outperforming con- ventionalCNN-LSTMbaselinesby2.16%,whilemaintaining an F1-score of 0.7704. The system successfully identifies 18,753 anomalous events with a precision of 89.7% using an optimized detectionthresholdof0.3383.Featureimportanceanalysisreveals that protocol types (21.41%) and TCP flag patterns (30.28% combined) serve as the most discriminative indicators for attack classification. Experimental results demonstrate that our hybrid approachreducesfalsepositivesby35%comparedtostandalone models while effectively detecting multi-stage attacks. The pro- posedarchitectureofferssignificantpracticaladvantagesforreal- world deployment, including interpretable feature engineering and computational efficiency, making it particularly suitable for enterprise network environments.

Introduction

Problem Context

The rapid expansion of IoT devices and cloud infrastructure has led to a sharp increase in cyberattacks, including:

• 67% rise in zero-day exploits

• 82% growth in IoT-based botnets

• 91% increase in cloud infrastructure attacks

Traditional signature-based IDS and machine learning methods are inadequate, as they process traffic as static or isolated events and fail to detect complex, multi-stage attacks. There's a growing need for models that integrate both spatial (network topology) and temporal (attack timing) aspects of threats.

Proposed Solution: Hybrid-GTX-IDS

A novel intrusion detection system that combines the strengths of Graph Neural Networks (GNNs) and transformers, optimized for real-time deployment.

Key Innovations:

1. Dynamic Graph Learning: Adapts network graph edges in real time using attention mechanisms to reflect evolving traffic patterns.

2. Hierarchical Transformer Module: Captures both short-term (packet-level) and long-term (session-level) behaviors, enabling detection of rapid and slow-developing attacks.

3. Uncertainty-Aware XGBoost Ensemble: Combines deep embeddings with human-interpretable features for high accuracy and explainability.

4. Efficient Real-Time Deployment: Processes up to 12,000 packets/sec on consumer-grade hardware.

Performance Highlights:

• 15.2% improvement in multi-stage attack detection over isolated GNN/Transformer models

• 18.3% higher detection of evolving attacks using dynamic graphs

• First comprehensive benchmark on CICIDS 2023, identifying encrypted traffic features as highly important (28.7% contribution)

• Capable of flagging anomalies using entropy-based uncertainty scoring

Architecture Summary:

1. Data Preparation

• Imputation for missing values

• Label encoding

• Synthetic timestamp generation and cyclic temporal features

• Train/validation/test split (70/15/15)

2. Feature Engineering

• Graph construction from traffic relationships

• Temporal embedding using time-series features

• UMAP for dimensionality reduction

• SHAP analysis for feature interpretability

3. Model Components

• GNN Module: Learns structural relationships in network traffic

• Transformer Module: Models sequential data using multi-scale attention

• XGBoost: Fuses outputs with handcrafted features for final prediction

4. Anomaly Detection

• Uses entropy-based uncertainty thresholding for adaptive alerting

Related Work & Research Gaps

• Earlier models like CNN-LSTM and XGBoost-CNN-LSTM focused on either spatial or temporal features but ignored their combination.

• GNNs often used static graphs, failing to adapt to real-time network changes.

• Limited interpretability in most deep models, reducing trust from analysts.

• Many models are not scalable or fast enough for enterprise use.

Conclusion

The Hybrid-GTX-IDS framework represents a significant advancement in network intrusion detection, addressing criti- cal gaps in existing systems through its innovative integration ofspatial-temporaldeeplearning.Bysynergizinggraphneural networks(GNNs)forstructuralanalysisandtransformermod- els for temporal sequence processing, the framework achieves 78.28% accuracy and 77.04% F1-score on the CIC-IDS2023 dataset, outperforming state-of-the-art baselines like CNN- LSTM and pure GNNs. Key innovations include dynamic graph construction for adaptive network topology modeling, hierarchical attention mechanisms for multi-scale temporal analysis, and uncertainty-aware anomaly detection that flags 18,753 anomalies with 89.7% precision at a 5% false positive rate. Featureimportanceanalysisrevealsprotocol-centricindica- tors (e.g., SYN counts, ICMP ratios) as critical discrimina- tors, providing actionable insights for security analysts while maintaining interpretability. Notably, the ensemble approach reduces false positives by 35%, a crucial advantage for enter- prise deployment. Despite these strengths, limitations persist, including per- formance degradation on encrypted TLS 1.3 traffic (12% precision drop) and dependency on labeled data for rareattack classes. Future work will focus on adversarial training to counter evasion tactics, federated learning for collabora- tive threshold tuning across distributed networks, and self- supervised techniques to mitigate label dependency. In conclusion, Hybrid-GTX-IDS bridges the gap between theoretical accuracy and practical deployability, offering a scalable, interpretable solution for modern network defense.Its hybrid architecture not only advances intrusion detection capabilities but also provides a foundation for adaptive cy- bersecurity systems capable of evolving alongside emerging threats

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Copyright

Copyright © 2025 Abhirup Arindam, Anurag Singh Baghel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.