UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection

The new wave of adversarial attacks that utilize gradient-related vulnerabilities in neural network-based classifiers makes Network Intrusion Detection Systems more open to such threats. Although state-of-the-art adversarial training methods have shown promising results in producing more robust classifiers, their interpretability and defense ability are limited due to their lack of understanding of how adversarial attacks propagate in different layers of network classifiers. In this paper, we present an insightful approach, called LARAR (Layer-wise Adversarial Robustness using Adaptive Regularization), that incorporates additional layer-wise vulnerability analysis and adaptive weighting in conventional adversarial training methods. Additionally, we utilize 'Auxiliary Classifiers' in our approach. LARAR provides interpretable layer-wise vulnerability scores, achieves a clean accuracy of 95.01%, and provides better robustness against adversarial attacks (FGSM, PGD, and transfer attacks) on the UNSW-NB15 dataset. Through the identification of vulnerable layers, the proposed framework reduces computational complexity and enables the early detection of adversarial samples, thus enhancing the effectiveness and interpretability of adversarial defense mechanisms in NIDS.

Network intrusion detection systems (NIDS) have become essential for Internet of Things (IoT) environments, as malware targeting IoT devices continues to evolve in sophistication. Unsupervised learning approaches offer a promising direction by removing the dependency on labeled datasets. However, the common assumption that training data are entirely clean is often violated in practice, particularly when data samples are collected directly from deployed network devices, where anomalies are likely to be present in the training datasets. Such contamination degrades detection performance and highlights the need for robust unsupervised NIDS methods capable of operating effectively under contaminated unlabeled training data. To address this issue, we propose a robust training methodology for anomaly detection (AD) that remains effective even in the presence of unlabeled anomalies. Our method consists of two primary components. First, we exploit a known limitation of federated learning (FL), namely its tendency to underrepresent minority data. By leveraging this characteristic, we attenuate the influence of anomalous data originating from a small number of compromised clients. Second, we introduce a selective aggregation mechanism during model aggregation, which quantifies the "distance" between local client models and a global reference. Specifically, we employ the Expectation-Maximization (EM) algorithm to detect and exclude client groups whose model updates significantly diverge from the majority. This selective aggregation ensures that anomalous updates do not compromise the global model. Experiments conducted on multiple NIDS datasets demonstrate that our method outperforms existing approaches in environments contaminated with anomalous data. Furthermore, the proposed method maintains its detection performance even as the proportion of anomalies increases.

Network intrusion detection systems play a vital role in protecting networks by detecting malicious network traffic which can then be investigated by a cybersecurity operations centre. State-of-the-art approaches utilise supervised machine learning methods to train a classification model to recognise known cyberattacks; however, these models require a large labelled dataset to train and show poor performance when trained on smaller datasets. In an attempt to address this shortcoming, anomaly detection models learn the distribution of benign traffic and flag non-conforming traffic as malicious. While these methods do not require malicious examples to train, they suffer from high false-positive rates rendering them impractical. As a result, networks may be particularly vulnerable when there are insufficient labelled instances of a specific attack class to train an effective classifier. This often occurs in newly established networks or when previously unseen types of attacks emerge. To address this challenge, this work proposes the use of a triplet network, utilising online triplet mining and a KNN classifier, which is able to perform few-shot classification, enabling effective intrusion detection after being trained on a limited number of malicious examples. Various online triplet mining algorithms were explored and model design choices, such as the inference algorithm and optimised distance metrics, were compared and evaluated through a series of ablation studies. The final model was compared against other state-of-the-art approaches in few-shot binary and multiclass classification, where the proposed approach was found to be competitive with existing methods when trained on as little as 10 malicious samples of each class.