X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection System
- URL: http://arxiv.org/abs/2402.00839v2
- Date: Sun, 2 Jun 2024 05:00:39 GMT
- Title: X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection System
- Authors: Kiymet Kaya, Elif Ak, Sumeyye Bas, Berk Canberk, Sule Gunduz Oguducu,
- Abstract summary: Using machine learning (ML) and deep learning (DL) models in Intrusion Detection Systems has led to a trust deficit due to their non-transparent decision-making.
This paper introduces a novel Explainable IDS approach, called X-CBA, that leverages the structural advantages of Graph Neural Networks (GNNs) to effectively process network traffic data.
Our approach achieves high accuracy with 99.47% in threat detection and provides clear, actionable explanations of its analytical outcomes.
- Score: 2.556190321164248
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The effectiveness of Intrusion Detection Systems (IDS) is critical in an era where cyber threats are becoming increasingly complex. Machine learning (ML) and deep learning (DL) models provide an efficient and accurate solution for identifying attacks and anomalies in computer networks. However, using ML and DL models in IDS has led to a trust deficit due to their non-transparent decision-making. This transparency gap in IDS research is significant, affecting confidence and accountability. To address, this paper introduces a novel Explainable IDS approach, called X-CBA, that leverages the structural advantages of Graph Neural Networks (GNNs) to effectively process network traffic data, while also adapting a new Explainable AI (XAI) methodology. Unlike most GNN-based IDS that depend on labeled network traffic and node features, thereby overlooking critical packet-level information, our approach leverages a broader range of traffic data through network flows, including edge attributes, to improve detection capabilities and adapt to novel threats. Through empirical testing, we establish that our approach not only achieves high accuracy with 99.47% in threat detection but also advances the field by providing clear, actionable explanations of its analytical outcomes. This research also aims to bridge the current gap and facilitate the broader integration of ML/DL technologies in cybersecurity defenses by offering a local and global explainability solution that is both precise and interpretable.
Related papers
- LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity [0.0]
This study introduces the Lightweight Explainable Network Security framework (LENS-XAI)
LENS-XAI combines robust intrusion detection with enhanced interpretability and scalability.
This research contributes significantly to advancing IDS by addressing computational efficiency, feature interpretability, and real-world applicability.
arXiv Detail & Related papers (2025-01-01T10:00:49Z) - Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion Detection [0.0]
We propose an Enhanced Convolutional Neural Network (EnCNN) for Network Intrusion Detection Systems (NIDS)
We compare EnCNN with various machine learning algorithms, including Logistic Regression, Decision Trees, Support Vector Machines (SVM), and ensemble methods like Random Forest, AdaBoost, and Voting Ensemble.
The results show that EnCNN significantly improves detection accuracy, with a notable 10% increase over state-of-art approaches.
arXiv Detail & Related papers (2024-09-27T11:20:20Z) - Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model [38.79241114146971]
We show how interpretability methods can increase trust in predictions of a neural network trained to classify quantum phases.
In particular, we show that we can ensure better out-of-distribution generalization in the complex classification problem.
This work is an example of how the systematic use of interpretability methods can improve the performance of NNs in scientific problems.
arXiv Detail & Related papers (2024-06-14T13:24:32Z) - Advancing Security in AI Systems: A Novel Approach to Detecting
Backdoors in Deep Neural Networks [3.489779105594534]
backdoors can be exploited by malicious actors on deep neural networks (DNNs) and cloud services for data processing.
Our approach leverages advanced tensor decomposition algorithms to meticulously analyze the weights of pre-trained DNNs and distinguish between backdoored and clean models.
This advancement enhances the security of deep learning and AI in networked systems, providing essential cybersecurity against evolving threats in emerging technologies.
arXiv Detail & Related papers (2024-03-13T03:10:11Z) - Profile of Vulnerability Remediations in Dependencies Using Graph
Analysis [40.35284812745255]
This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) model.
We analyze control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities.
Results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities.
arXiv Detail & Related papers (2024-03-08T02:01:47Z) - Uncertainty Estimation by Fisher Information-based Evidential Deep
Learning [61.94125052118442]
Uncertainty estimation is a key factor that makes deep learning reliable in practical applications.
We propose a novel method, Fisher Information-based Evidential Deep Learning ($mathcalI$-EDL)
In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes.
arXiv Detail & Related papers (2023-03-03T16:12:59Z) - Self-Supervised and Interpretable Anomaly Detection using Network
Transformers [1.0705399532413615]
This paper introduces the Network Transformer (NeT) model for anomaly detection.
NeT incorporates the graph structure of the communication network in order to improve interpretability.
The presented approach was tested by evaluating the successful detection of anomalies in an Industrial Control System.
arXiv Detail & Related papers (2022-02-25T22:05:59Z) - A new interpretable unsupervised anomaly detection method based on
residual explanation [47.187609203210705]
We present RXP, a new interpretability method to deal with the limitations for AE-based AD in large-scale systems.
It stands out for its implementation simplicity, low computational cost and deterministic behavior.
In an experiment using data from a real heavy-haul railway line, the proposed method achieved superior performance compared to SHAP.
arXiv Detail & Related papers (2021-03-14T15:35:45Z) - Information Obfuscation of Graph Neural Networks [96.8421624921384]
We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data.
We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
arXiv Detail & Related papers (2020-09-28T17:55:04Z) - Graph Backdoor [53.70971502299977]
We present GTA, the first backdoor attack on graph neural networks (GNNs)
GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features.
It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
arXiv Detail & Related papers (2020-06-21T19:45:30Z) - A cognitive based Intrusion detection system [0.0]
Intrusion detection is one of the important mechanisms that provide computer networks security.
This paper proposes a new approach based on Deep Neural Network ans Support vector machine classifier.
The proposed model predicts the attacks with better accuracy for intrusion detection rather similar methods.
arXiv Detail & Related papers (2020-05-19T13:30:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.