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An Adversarial Approach for Explainable AI in Intrusion Detection Systems

Daniel L. Marino, Chathurika S. Wickramasinghe, Milos Manic

arXiv:1811.11705v1cs.LGcs.CRstat.ML

TL;DR

Data-driven IDS models can be black boxes, and unseen attacks may be misclassified as normal. The paper uses adversarial modifications to explain such errors, finding explanations that match expert knowledge. The approach requires model gradients but does not require modifying the model.

  • Problem

    Data-driven IDS models are often black boxes, and unseen attacks may be misclassified as normal, making it important to understand individual errors.

    Source: §I · p. 1

  • Method

    An adversarial interface finds minimum input modifications that correct misclassified IDS samples and uses the changed features as explanations.

    Source: §I · pp. 1–2

  • Results

    The explanations were satisfactory and matched expert knowledge; misclassifications often involved samples with conflicting characteristics between classes.

    Source: §I · p. 2, §V · p. 6

  • Takeaways & Limitations

    The approach requires model gradients but no model modifications, and can incorporate non-continuous functions such as rounding for integer or categorical features.

    Source: §VI · p. 6

Abstract

from arXiv · show

Despite the growing popularity of modern machine learning techniques (e.g. Deep Neural Networks) in cyber-security applications, most of these models are perceived as a black-box for the user. Adversarial machine learning offers an approach to increase our understanding of these models. In this paper we present an approach to generate explanations for incorrect classifications made by data-driven Intrusion Detection Systems (IDSs). An adversarial approach is used to find the minimum modifications (of the input features) required to correctly classify a given set of misclassified samples. The magnitude of such modifications is used to visualize the most relevant features that explain the reason for the misclassification. The presented methodology generated satisfactory explanations that describe the reasoning behind the mis-classifications, with descriptions that match expert knowledge. The advantages of the presented methodology are: 1) applicable to any classifier with defined gradients. 2) does not require any modification of the classifier model. 3) can be extended to perform further diagnosis (e.g. vulnerability assessment) and gain further understanding of the system. Experimental evaluation was conducted on the NSL-KDD99 benchmark dataset using Linear and Multilayer perceptron classifiers. The results are shown using intuitive visualizations in order to improve the interpretability of the results.

I. INTRODUCTION

Data-driven IDSs face black-box interpretability concerns, and unseen attacks may be misclassified as normal; this paper proposes an adversarial interface to explain such errors.

  • Data-driven models are increasingly used in cyber-security, but they often function as black boxes for users.Source: §I · p. 1
  • An unseen attack misclassified as normal can breach a system, making explanations relevant to debugging and prevention decisions.Source: §I · p. 1
  • The paper presents an explainable AI interface for diagnosing incorrect classifications made by data-driven IDSs through an adversarial approach.Source: §I · p. 1
  • Minimum input modifications needed to correct misclassified samples identify relevant features, and the resulting explanations match expert knowledge.Source: §I · p. 2

II. ADVERSARIAL MACHINE LEARNING

Adversarial samples modify references to alter model outputs; this paper repurposes the adversarial objective to correct misclassified samples rather than deceive the classifier.

  • Adversarial samples use small modifications to change model outputs and can help defenders study robustness and debug machine-learning models.Source: §II · p. 2
  • Attackers use adversarial samples to evade detection, confuse classifiers, degrade performance, or gain information about models or training data.Source: §II · p. 2
  • The paper repurposes adversarial crafting to minimize modifications that correct a misclassified sample rather than maximize attack impact while avoiding detection.Source: §II · p. 2

III. EXPLAINING MISCLASSIFICATIONS USING ADVERSARIAL MACHINE LEARNING

The methodology explains misclassifications by correcting samples adversarially and examining their feature changes; its classifier component estimates class probabilities from learned parameters.

III. EXPLAINING MISCLASSIFICATIONS USING ADVERSARIAL MACHINE LEARNING

  • The interface modifies misclassified samples until the classifier assigns the correct class, using feature differences to explain its output.Source: §III · pp. 2–3

A. Data-driven classifier

  • A data-driven classifier estimates the probability that sample x belongs to class k using parameters learned from data through supervised learning.Source: A. Data-driven classifier · p. 3
  • Classifier parameters are obtained by minimizing cross-entropy using one-hot class labels, and models may misclassify samples depending on model complexity and dataset.Source: A. Data-driven classifier · p. 3

B. Modifying misclassified samples

The paper repurposes adversarial machine learning to understand misclassifications by locating minimal input changes that move a sample across the classifier’s decision boundary. It uses those changes to explain the model’s output while keeping the modified sample close to the original and within valid input bounds.

C. Explaining incorrect estimations

The method explains incorrect estimations by correcting misclassified samples toward their true class and visualizing the feature differences. For groups of samples, it uses the average deviation; a t-SNE view shows the real and modified MLP samples without a clear visual distinction.

A. Dataset

The experiments use NSL-KDD records from the normal, DOS, and probe classes to evaluate linear and MLP classifiers. The MLP achieves higher accuracy than the linear classifier on both training and testing datasets.

A. Dataset

  • NSL-KDD is a revised KDD99 benchmark with redundant and duplicate records removed; the experiments include normal, DOS, and probe classes.Source: A. Dataset · p. 4
  • The dataset has 124926 training samples and 16557 testing samples; the models are trained with equal class ratios in sampled mini-batches, and features are normalized using training-set statistics.Source: A. Dataset · p. 4

B. Classifiers

  • The methodology applies to classifiers with a defined input gradient; experiments use a linear classifier and an MLP with ReLU, both trained with weight decay and early stopping.Source: B. Classifiers · p. 4
  • The MLP classifier provides higher accuracy than the linear classifier on the training and testing datasets.Source: B. Classifiers · p. 4, Table I · p. 4

C. Modified misclassified samples

The MLP t-SNE visualization places modified samples close to the real misclassified samples, with no clear visual distinction between them. The figure is used to illustrate that small changes can alter classifier output.

D. Explaining incorrect estimations

The explanations identify different feature patterns behind Normal-to-DOS and DOS-to-Normal errors, and show how categorical features affect the analysis. The reported patterns are presented as consistent with expert expectations.

D. Explaining incorrect estimations

  • Normal samples misclassified as DOS showed high same-host and destination counts, low duration, few root operations, and low login success.The explanations for the Linear and MLP models were qualitatively similar.Source: D. Explaining incorrect estimations · p. 5
  • The explanations also identified a high same-source-port connection rate, low different-service rate, and low same-destination-port count among Normal-to-DOS errors.The accompanying observations include guest-login status.Source: D. Explaining incorrect estimations · p. 5
  • All misclassified Normal samples had zero-second connections, which the classifier treated as suspicious; modified samples had increased duration.Figure 5 compares the original and modified duration values.Source: D. Explaining incorrect estimations · p. 5, Figure 5 · p. 5
  • The plots indicate that the classifier considers a low percentage of connections directed to different services suspicious.The authors describe the graphs as a way to extract knowledge about concepts learned by the model.Source: D. Explaining incorrect estimations · p. 5
  • DOS samples misclassified as Normal had lower connection error rates and higher login success.The paper describes these as features usually expected in normal connections.Source: D. Explaining incorrect estimations · pp. 5–6
  • When categorical features were included, protocol type was unchanged while service changed in almost all samples; many Normal-to-DOS samples used a private service.The classifier treated communication with a private service as suspicious; the analysis rounded inputs to account for categorical features.Source: D. Explaining incorrect estimations · p. 6

V. CONCLUSION

The approach produced explanations for misclassified samples that the authors describe as insightful and consistent with expert knowledge. It uses minimal feature modifications and visualizes the relevant features, with experiments using Linear and Multilayer perceptron classifiers.

V. CONCLUSION

  • Experiments used Linear and Multilayer perceptron classifiers on an Intrusion Detection benchmark dataset, with results shown in interpretable plots.The conclusion describes the plots as intuitive and easily interpreted by users.Source: §V · p. 6
  • The adversarial method finds the minimum modifications needed to correctly classify misclassified samples and visualizes the relevant features.The paper notes that discontinuous operations such as rounding can be included because the indicator's gradient is not used during optimization.Source: §V · p. 6
  • The explanations matched expert knowledge, including Normal connections with low duration and login success misclassified as attacks and attacks with low error and higher login success misclassified as Normal.The authors report that misclassified samples often had characteristics conflicting with their true classes.Source: §V · p. 6

VI. DISCUSSION

The approach applies across differentiable models and classification tasks without modifying the model. It can also support vulnerability assessment and future accuracy improvements without adding training data.

VI. DISCUSSION

  • The approach works with any differentiable model and classification task without requiring model modifications.It requires only gradients of the model's cross-entropy with respect to inputs.Source: §VI · p. 6
  • Non-continuous operations, including rounding for integer and categorical features, can be incorporated.Source: §VI · p. 6
  • The approach can be extended to vulnerability assessment by using modifications that deceive the classifier.Source: §VI · p. 6
  • Future work will incorporate explanations to improve model accuracy without adding training data.Source: §VI · p. 6
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