IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection (1809.02077v5)

Abstract: As an essential tool in security, the intrusion detection system bears the responsibility of the defense to network attacks performed by malicious traffic. Nowadays, with the help of machine learning algorithms, intrusion detection systems develop rapidly. However, the robustness of this system is questionable when it faces adversarial attacks. For the robustness of detection systems, more potential attack approaches are under research. In this paper, a framework of the generative adversarial networks, called IDSGAN, is proposed to generate the adversarial malicious traffic records aiming to attack intrusion detection systems by deceiving and evading the detection. Given that the internal structure and parameters of the detection system are unknown to attackers, the adversarial attack examples perform the black-box attacks against the detection system. IDSGAN leverages a generator to transform original malicious traffic records into adversarial malicious ones. A discriminator classifies traffic examples and dynamically learns the real-time black-box detection system. More significantly, the restricted modification mechanism is designed for the adversarial generation to preserve original attack functionalities of adversarial traffic records. The effectiveness of the model is indicated by attacking multiple algorithm-based detection models with different attack categories. The robustness is verified by changing the number of the modified features. A comparative experiment with adversarial attack baselines demonstrates the superiority of our model.

• The paper introduces IDSGAN, a novel GAN framework that generates adversarial network traffic to bypass IDS in black-box scenarios.
• It employs a restricted modification mechanism to alter non-functional features while preserving malicious intent.
• Empirical results on the NSL-KDD dataset show near-zero detection rates for attacks, outperforming existing adversarial methods.

Overview of "IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection"

The paper presents a novel approach called IDSGAN, which utilizes Generative Adversarial Networks (GANs) to craft adversarial examples aimed at evading intrusion detection systems (IDS). This methodology addresses the critical security concern of whether machine learning-based IDS can withstand adversarial attacks, especially when such systems are treated as black-boxes.

Methodology and Contributions

IDSGAN leverages the foundational GAN architecture introduced by Goodfellow et al., specifically utilizing enhancements from Wasserstein GANs to improve training stability. It comprises two key components: a generator that mutates malicious traffic into adversarial samples, and a discriminator that learns to identify these adversarial instances by mimicking the behavior of a targeted black-box IDS model.

1. Black-box Attack Strategy: The work primarily focuses on black-box scenarios where the internal parameters of the IDS are unknown. IDSGAN accomplishes this through an adaptive learning mechanism in the discriminator, which utilizes output queries from the IDS to refine its model of the black-box system behavior.
2. Restricted Modification Mechanism: To ensure that generated adversarial traffic retains its original malicious functionalities, IDSGAN incorporates a restricted modification mechanism. This constraint ensures only non-functional features are altered, thereby maintaining the efficacy of the attack types such as DoS, U2R, and R2L.
3. Empirical Validation: The effectiveness of IDSGAN is validated across multiple IDS models utilizing different underlying algorithms such as SVM, Naive Bayes, and Random Forest. Evaluations on the NSL-KDD dataset demonstrate significant reductions in detection rates, showing near-zero detection for adversarial examples, and marking substantial improvements over state-of-the-art adversarial methods like JSMA, FGSM, and CW attacks.

Numerical Outcomes

The results highlight remarkable success in lowering detection rates of attacks like DoS and U2R by IDS trained on NSL-KDD data. For instance, adversarial detection rates for DoS traffic dropped to below 1% across various algorithms, with evasion increase rates consistently exceeding 98%. This performance underscores IDSGAN's strength in generating adversarial examples that effectively bypass IDS.

Implications and Future Directions

The demonstration of IDSGAN's effectiveness in provoking misclassification in IDS suggests significant implications for cybersecurity practices. It underscores the need for enhancing IDS resilience against adversarial learning tactics and potentially retraining models in a manner akin to adversarial training.

Future work can explore the scalability of IDSGAN to more complex network environments and datasets, along with integrating dynamic malware generation. Furthermore, a promising avenue lies in evaluating its applicability in real-time network monitoring scenarios, subject to ethical guidelines and institutional oversight.

In conclusion, IDSGAN exemplifies advanced adversarial methodologies capable of challenging the robustness of existing security frameworks. It prompts a reevaluation of defensive strategies in intrusion detection, emphasizing the dynamic interplay between attack generation and threat mitigation.