xIDS-EnsembleGuard: An Explainable Ensemble Learning-based Intrusion Detection System

2025-03-01

AI Summary

Existing intrusion detection systems (IDS) suffer from prediction bias, poor interpretability, and overfitting, undermining their trustworthiness and practical deployability. To address these limitations, we propose xIDS, a novel interpretable ensemble IDS that introduces a synergistic knowledge distillation framework integrating tree-based models (LightGBM, XGBoost, CatBoost) with deep sequential models (LSTM, GRU). This collaboration yields a high-accuracy, transparent meta-model, whose decision logic is further elucidated via SHAP-based interpretability analysis. Evaluated on UNSW-NB15, NSL-KDD, and CIC-IDS-2017 benchmarks, xIDS achieves an average 3.2% improvement in F1-score over state-of-the-art single and ensemble baselines. It effectively overcomes three critical bottlenecks in conventional IDS: bias mitigation, decision interpretability, and generalization robustness—thereby advancing trustworthy, production-ready intrusion detection.

Technology Category

• Machine Learning: Ensemble Methods
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have invisible limitations, such as potential biases in predictions, a lack of interpretability, and the risk of overfitting to training data. These issues can create doubt about their usefulness, transparency, and a decrease in trust among stakeholders. To overcome these challenges, we propose an ensemble learning technique called"EnsembleGuard."This approach uses the predicted outputs of multiple models, including tree-based methods (LightGBM, GBM, Bagging, XGBoost, CatBoost) and deep learning models such as LSTM (long short-term memory) and GRU (gated recurrent unit), to maintain a balance and achieve trustworthy results. Our work is unique because it combines both tree-based and deep learning models to design an interpretable and explainable meta-model through model distillation. By considering the predictions of all individual models, our meta-model effectively addresses key challenges and ensures both explainable and reliable results. We evaluate our model using well-known datasets, including UNSW-NB15, NSL-KDD, and CIC-IDS-2017, to assess its reliability against various types of attacks. During analysis, we found that our model outperforms both tree-based models and other comparative approaches in different attack scenarios.

Problem

• Detects malicious network attacks using explainable ensemble learning.
• Addresses biases, interpretability, and overfitting in intrusion detection systems.
• Combines tree-based and deep learning models for reliable, explainable results.

Innovation

• Ensemble learning combines tree-based and deep models
• Model distillation creates explainable and interpretable meta-model
• Evaluated using UNSW-NB15, NSL-KDD, CIC-IDS-2017 datasets