# Polymorphic Adversarial Cyberattacks Using WGAN
## 5 Proposed Framework
### Polymorphic Engine to Generate a Polymorphic Attack
The models are then retrained with previous adversarial data from the attack cycle and again evaluated in the retraining cycle. The results indicate improvement in IDS performances. In the next attack cycle, the attacker mutates the feature profile using the value-based approach (approach 1) and relaunches the attack on the IDS. During this phase, the detection rates go down again, yet show better results than phase 1. This process is repeated until the IDS cannot be evaded by the polymorphic attacker. Figure 11 shows that RF, DT, and KNN models do not need retraining for the last two phases since these IDSs can easily identify the attack, whereas SVM and NB still have lower detection rates in the last phase of the polymorphic attack. From Figure 11, we notice that the proposed WGAN framework for polymorphic adversarial training is effective in improving the performance of the machine learning-based black-box IDSs against rapidly evolving polymorphic attacks. Further performance improvement is expected in the future when the model is trained with other classes of polymorphic network attacks. Figure 10: Polymorphic adversarial attacks using the automated feature selection (40 features). ## 8 Conclusions and Future Work

In this paper, we proposed a Wasserstein GAN-based framework to generate polymorphic adversarial DDoS/DoS attacks using a CICIDS2017 dataset. In order to synthesize these attacks, we applied two different approaches to change the feature profile of the attack. For the first approach, we employed a value-based feature mutation to synthesize new data for the attacker, which then generates a polymorphic attack. In the second approach, the feature selection-based method was employed to generate new data for the

Figure 11: Comparative analysis of adversarial polymorphic attack detection rate for multiple IDS models. The adversarial polymorphic attacks are synthesized using Algorithm 3. (**a**) Random Forest (RF). (**b**) Decision Tree (DT). (**c**) Support Vector Machine. (**d**) K-Nearest Neighbor (KNN). (**e**) Naive Bayes (NB). attacker, which can in turn synthesizes a polymorphic attack. The second approach is based on both the manual as well as the automated feature selection. From the results, we demonstrated that the Generator can produce polymorphic adversarial DDoS/DoS attacks to effectively evade the IDS during the initial phases. However, after several retraining steps, the IDS learns the pattern of attacks and eventually identifies all of the attacks. Additionally, we have provided an extensive comparison of multiple machine learning models against these attacks. The results indicate that the WGAN based adversarial training has improved the performance of several IDSs against polymorphic attacks. In this paper, the work represented the introduction of a novel concept in network security, namely, polymorphic network attacks. Therefore, this work can be extended and built upon extensively for application to different security attack types, and IDS systems. While the proposed model has employed WGAN, there are several other variants of GAN, such as DCGAN [61], Conditional GAN [62], BiGAN [63], and Cycle GAN [64] that can be used to generate adversarial network attack data. On the IDS side, our results indicate that the polymorphic attackers can evade detection, albeit for a limited number of cycles. It is imperative to develop new techniques and models for the IDS that could, if not eliminate, at least shorten the window of vulnerability against polymorphic attacks. Our work has focused on the signature-based IDS, primarily since the anomaly-based IDS is considered to have a high false positive rate, which would unnecessarily reduce the network throughput. It would be interesting to run our attack generator against the anomaly-based IDS, compare the results with the signature-based IDS, and evaluate the advantages and disadvantages.