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FortiNIDS: Defending Smart City IoT Infrastructures Against Transferable Adversarial Poisoning in Machine

Abdulaziz Alajaji¹

¹Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.

Sensors (Basel, Switzerland)

|October 16, 2025

Summary

This study introduces FortiNIDS, a framework enhancing AI-based Network Intrusion Detection Systems (NIDS) against data poisoning attacks. Defenses like adversarial training improve NIDS reliability in smart city IoT environments.

Keywords:

IoT security Network Intrusion Detection Systems (NIDS)Reject on Negative Impact (RONI)adversarial machine learning adversarial training data poisoning attacks gradient boosting random forest smart city security transferability

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Area of Science:

Cybersecurity
Artificial Intelligence
Network Security

Background:

Traditional security mechanisms are insufficient against evolving cyberattacks.
AI-based Network Intrusion Detection Systems (NIDS) offer advanced threat detection but are vulnerable to data poisoning.
Smart city Internet of Things (IoT) environments face unique security challenges.

Purpose of the Study:

To model black box poisoning attacks on tree classifiers (Random Forest, Gradient Boosting).
To introduce FortiNIDS, a framework using surrogate neural networks for transferable adversarial perturbations.
To evaluate defense strategies (adversarial training, Reject on Negative Impact) for NIDS resilience in smart cities.

Main Methods:

Modeling black box poisoning attacks on Random Forest and Gradient Boosting classifiers.
Developing the FortiNIDS framework with a surrogate neural network for adversarial example generation.
Evaluating adversarial training and Reject on Negative Impact (RONI) using the CICDDoS2019 dataset.

Main Results:

Demonstrated the transferability of adversarial examples between models.
FortiNIDS framework effectively generates perturbations to attack NIDS.
Adversarial training and RONI significantly improved NIDS detection accuracy and reliability against attacks.

Conclusions:

Targeted defense strategies enhance the robustness of AI-based NIDS against data poisoning.
FortiNIDS provides a method for understanding and defending against sophisticated adversarial attacks.
The findings contribute to securing smart city IoT networks and protecting user privacy.