# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 6 Research Works in ML-Based Security Systems of IoT Networks
For that, the authors launch a single-step attack technique, FGSM, along with three iterative attack techniques, i.e., BIM, PGD, and MIM (Momentum Iterative Method) in targeted and non-targeted scenarios on CNN-based DIS leveraging on a Complex Value Neural Network (CVNN) model [162]. In their experiments, the authors created a generated dataset that contains four main features: Signal Source, Power Amplifier, Channel Attenuation, and Receiver Device. The generated dataset will serve as the foundation for training the CVNN model, which will then be applied for device identification purposes. Leveraging a combined set of evaluation criteria to better assess the model's performance, the study finds that iterative attack methods typically perform better than one-step attacks in fooling ML-based DIS models. However, as perturbation levels increase, their success rate becomes stable. The outcomes also revealed the ML models' susceptibility to targeted attacks. [PERSON] et al. [138] suggested a novel method to produce real-time adversarial examples using heatmaps from Class Activation Mapping (CAM) and Grad-CAM++. They explored the vulnerabilities of ML-based IoT DISes using payload-based IoT identification models such as Fully Connected Neural Network (FCN), CNNs, and Global Average Pooling (GAP). Using a portion of the publicly accessible IoT Trace dataset [161], these models processed the first 784 bytes within the TCP payload and converted them into a 28 \(\times\) 28 greyscale image. Experiments involved manipulating unauthorized IoT device data and altering a specific number of bytes to see how these adversarial examples perform when exposed to the target models. Surprisingly, adversarial examples were transferable to varied model architectures. The GAP model displayed unique behavior against these samples, hinting at its defensive potential. Despite vulnerabilities in the target models, advanced architecture like Vision Transformer [163] might resist these adversarial attacks better. The researchers in [139] delved deep into the performance of ML-based IoT DIS using hardware behavior identification. Therefore, the authors proposed a combined LSTM and CNN (LSTM-1 DCNN) model for IoT DIS and evaluated its robustness against adversarial attacks where adversaries alter device environmental and contextual conditions such as temperature changes, CPU load, and device rebooting to hinder its proper identification. To assess the effectiveness of LSTM-1 DCNN, the model was trained and tested using the LwHBench dataset [164] and exposed to various adversarial attacks like FGSM, BIM, MIM, PGD, JSMA, Boundary Attack, and C&W. The LSTM-CNN model showcased superior performance, achieving F1-Score of 0.96 in average, identifying all devices with a True Positive Rate (TPR) of 0.80 as threshold for device identification. When exposed to various evasion adversarial attacks, the model remained resilient to temperature-based attacks. However, certain evasion techniques such as FGSM, BIM, and MIM were successful in fooling the identification process. In response, the researchers employed adversarial training and model distillation as defense mechanisms. These mechanisms enhanced the model's robustness. The combination of adversarial training and model distillation provides strong protection against various evasion attacks.