Human-Centered Explainable AI for Security Enhancement: A Deep Intrusion Detection Framework
arXiv:2602.13271v1 Announce Type: new Abstract: The increasing complexity and frequency of cyber-threats demand intrusion detection systems (IDS) that are not only accurate but also interpretable. This paper presented a novel IDS framework that integrated Explainable Artificial Intelligence (XAI) to enhance transparency in deep learning models. The framework was evaluated experimentally using the benchmark dataset NSL-KDD, demonstrating superior performance compared to traditional IDS and black-box deep learning models. The proposed approach combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies in traffic sequences. Our deep learning results showed that both CNN and LSTM reached 0.99 for accuracy, whereas LSTM outperformed CNN at macro average precision, recall, and F-1 score. For weighted average precision, recall, and F-1 score, both models scored almost similarly. To ensure interpretability, the XAI model SHapley
arXiv:2602.13271v1 Announce Type: new Abstract: The increasing complexity and frequency of cyber-threats demand intrusion detection systems (IDS) that are not only accurate but also interpretable. This paper presented a novel IDS framework that integrated Explainable Artificial Intelligence (XAI) to enhance transparency in deep learning models. The framework was evaluated experimentally using the benchmark dataset NSL-KDD, demonstrating superior performance compared to traditional IDS and black-box deep learning models. The proposed approach combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies in traffic sequences. Our deep learning results showed that both CNN and LSTM reached 0.99 for accuracy, whereas LSTM outperformed CNN at macro average precision, recall, and F-1 score. For weighted average precision, recall, and F-1 score, both models scored almost similarly. To ensure interpretability, the XAI model SHapley Additive exPlanations (SHAP) was incorporated, enabling security analysts to understand and validate model decisions. Some notable influential features were srv_serror_rate, dst_host_srv_serror_rate, and serror_rate for both models, as pointed out by SHAP. We also conducted a trust-focused expert survey based on IPIP6 and Big Five personality traits via an interactive UI to evaluate the system's reliability and usability. This work highlighted the potential of combining performance and transparency in cybersecurity solutions and recommends future enhancements through adaptive learning for real-time threat detection.
Executive Summary
The article presents a novel intrusion detection system (IDS) framework that integrates Explainable Artificial Intelligence (XAI) to enhance the transparency and interpretability of deep learning models. The framework, evaluated using the NSL-KDD benchmark dataset, demonstrates superior performance compared to traditional IDS and black-box deep learning models. It combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks to capture temporal dependencies in traffic sequences. The study highlights the importance of interpretability in cybersecurity solutions, using SHapley Additive exPlanations (SHAP) to enable security analysts to understand and validate model decisions. A trust-focused expert survey was conducted to evaluate the system's reliability and usability, emphasizing the potential of combining performance and transparency in cybersecurity solutions.
Key Points
- ▸ Integration of XAI in deep learning models for enhanced transparency.
- ▸ Superior performance of the proposed IDS framework compared to traditional and black-box models.
- ▸ Use of CNN and LSTM networks to capture temporal dependencies in traffic sequences.
- ▸ Incorporation of SHAP for interpretability and validation of model decisions.
- ▸ Conducted a trust-focused expert survey to evaluate system reliability and usability.
Merits
Innovative Framework
The integration of XAI with deep learning models is a significant advancement in the field of cybersecurity, providing both high accuracy and interpretability.
Superior Performance
The framework demonstrated superior performance metrics, including accuracy, precision, recall, and F-1 score, compared to traditional and black-box models.
Comprehensive Evaluation
The study included a thorough evaluation using the NSL-KDD dataset and a trust-focused expert survey, providing a holistic assessment of the system's effectiveness.
Demerits
Dataset Limitations
The use of the NSL-KDD dataset, while benchmark, may not fully represent the diversity and complexity of real-world cyber threats.
Survey Bias
The expert survey, though valuable, may be subject to bias based on the participants' backgrounds and the specific traits evaluated.
Real-Time Adaptability
The framework's adaptability to real-time threat detection needs further exploration and enhancement.
Expert Commentary
The article presents a significant contribution to the field of cybersecurity by integrating Explainable AI (XAI) with deep learning models to enhance the transparency and interpretability of intrusion detection systems (IDS). The use of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture temporal dependencies in traffic sequences demonstrates a robust approach to improving the accuracy and reliability of IDS. The incorporation of SHapley Additive exPlanations (SHAP) is particularly noteworthy, as it enables security analysts to understand and validate model decisions, a critical aspect in high-stakes cybersecurity applications. The study's evaluation using the NSL-KDD dataset and the trust-focused expert survey provides a comprehensive assessment of the system's performance and usability. However, the reliance on a single benchmark dataset and the potential bias in the expert survey are limitations that should be addressed in future research. The article's recommendations for future enhancements, such as adaptive learning for real-time threat detection, highlight the need for continuous improvement in cybersecurity solutions. Overall, the study underscores the potential of combining performance and transparency in cybersecurity solutions, setting a precedent for future research and practical applications.
Recommendations
- ✓ Future research should explore the use of diverse and more recent datasets to evaluate the framework's performance in real-world scenarios.
- ✓ Conducting larger and more diverse expert surveys to mitigate potential biases and provide a more comprehensive assessment of the system's reliability and usability.