المفاهيم الأساسية
Proper uncertainty quantification is crucial for developing trustworthy machine learning-based intrusion detection systems that can reliably detect known attacks and identify unknown network traffic patterns.
الملخص
The content discusses the importance of enhancing the trustworthiness of machine learning-based intrusion detection systems (IDSs) by incorporating uncertainty quantification capabilities.
Key highlights:
- Traditional ML-based IDSs often suffer from overconfident predictions, even for misclassified or unknown inputs, limiting their trustworthiness.
- Uncertainty quantification is essential for IDS applications to avoid making wrong decisions when the model's output is too uncertain, and to enable active learning for efficient data labeling.
- The paper proposes that ML-based IDSs should be able to recognize "truly unknown" inputs belonging to unknown attack classes, in addition to performing accurate closed-set classification.
- Various uncertainty-aware ML models, including Bayesian Neural Networks, Random Forests, and energy-based methods, are critically compared for their ability to provide truthful uncertainty estimates and enhance out-of-distribution (OoD) detection.
- A custom Bayesian Neural Network model is developed that recalibrates the predicted uncertainty to improve OoD detection without significantly increasing computational overhead.
- Experiments on a real-world network traffic dataset demonstrate the benefits of uncertainty-aware models in improving the trustworthiness of ML-based IDSs compared to traditional approaches.
الإحصائيات
The dataset contains 43 NetFlow features extracted from network packets, describing the traffic between different sources and destinations. It includes 10 classes, with 9 attack types and benign traffic.
اقتباسات
"ML-based IDSs have demonstrated great performance in terms of classification scores [35]. However, the vast majority of the proposed methods in literature for signature-based intrusion detection rely on the implicit assumption that all class labels are a-priori known."
"We thus argue that for safety-critical applications, such as intrusion detection, the adopted ML model should be characterized not only through the lens of classification performance (accuracy, precision, recall, etc.), but it should also: 1) Provide truthful uncertainty quantification on the predictions for closed-set classification, 2) Be able to recognize as "truly unknowns" inputs belonging to unknown categories."