# Impacting Robustness in Deep Learning-Based NIDS through Poisoning Attacks
## 1 Introduction
Subsequently, these poisoned instances are introduced into the original training dataset with diverse poisoning rates ranging from 1% to 10%. The injection involves altering the distance among the injected poisoned samples, randomly placing them within the original training locations. Given the substantial data requirements for DL techniques and hyperparameter adjustment, our research leverages extensive datasets provided by the Canadian Institute of Cybersecurity (CIC), specifically the Communications Security and Establishment dataset (CIC2019 or CICIDS2019) [38]. This choice showcases the efficacy of our proposed assault approach in a real-world context. Feature selection crucially relied on [PERSON]'s Correlation technique to discern relationships between features, acknowledging the stability of associations in larger datasets compared to smaller ones [39]. Leveraging a high-dimensional dataset encompassing recent network attacks, we rigorously assessed the robustness of the system and presented our findings based on multiple metrics, such as accuracy, True Positive Rate (TPR), and True Negative Rate (TNR). This comprehensive evaluation offers a comprehensive insight into the efficacy of the proposed approach. This paper contributes significantly by bridging critical gaps in the field of network security. Specifically, this work presents a strong poisoning-based attacks method designed explicitly for DL to address a prominent deficiency in most recent literature that focuses on orthodox ML techniques. Our research provides an evaluation on the resilience and susceptibility of DNNs towards poisoning-based attacks via meticulous experiments leveraging the novel DeepFool method to create poisoned data. Furthermore, the study presents a comprehensive analysis of the attack's influence on the performance of the system by injecting various amounts of such manipulated samples into the training datasets. Employing inclusive dataset provided by the Canadian Institute of Cybersecurity (CIC), notably as CIC2019 (CICIDS2019), offers real-world pertinence and validity to the findings of this study. Applying [PERSON]'s Correlation method to select features in datasets with high-dimensions further enhances the accuracy and depth of this investigation. Ultimately, the paper's contribution lies in its meticulous exploration of data poisoning-based attacks on DNNs, shedding light on their implications for network security and offering insights into fortifying systems against such threats. The article is structured into distinct sections, each dedicated for specific facets of the research. Section 2 delves into a review of related work. Following this, Section 3 elucidates the fundamental concepts introduced in this paper. Section 4 intricately details the experimental setup. The methodological implementation is thoroughly outlined in Section 5, providing insight into the research approach. Section 6 examines and presents the findings derived from the study's experimental cases. Finally, Section 7 offers insightful concluding remarks and outlines potential avenues for future research endeavors.