# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 6 Research Works in ML-Based Security Systems of IoT Networks
[132] suggested a pioneering image restoration defense mechanism to answer the problem of high susceptibility and fragility of modern DNNs to the state-of-the-art OnePixel adversarial attacks within IIoT IDSes. The authors argue that the existing solutions either result in image quality degradation through the removal of adversarial pixels or outright rejection of the adversarial sample. This can have a substantial impact on the accuracy of DNNs and might result in a hazard for some critical IoT use cases, such as healthcare and self-driving vehicles. The proposed defense mechanism leverages on Accelerated Proximal Gradient approach to detect the malicious pixel within an adversarial image and subsequently restore the original image. In their demonstration experiments, the researchers chose two DNNs-based IDS, LeNet [146] and ResNet [147], and they trained them using the CIFAR-10 [148] and MNIST [149] datasets. The experimental outcomes revealed a high efficacy of the suggested defensive approach against One-Pixel attacks, achieving detection and mitigation accuracy of 98.7% and 98.2%, respectively, on CIFAR-10 and MNIST datasets. [PERSON] et al. [115] suggested an adversarial training approach to enhance the efficiency of hybrid CNNLSTM-based IDS by leveraging C-GAN. The authors introduce the C-GAN in the training pipeline to handle classes with limited samples and address the data imbalance of the BoT-IoT dataset [125]. First, the IDS model is trained on the BoT-IoT dataset, and specific classes with low performance, often those with sparse samples, are identified. Subsequently, C-GAN is trained using these identified classes, and the generator from C-GAN is utilized to retrain the IDS model, thereby improving the performance of the identified classes. The authors plan to further enhance their model by exploring strategies to defend against adversarial attacks to improve the CNNLSTM-based IDS's robustness. In their other work, the authors conducted a similar approach to enhance the robustness and effectiveness of IDS in the IIoT [113]. The study suggests the application of DRL in conjunction with a GAN to boost the IDS's efficiency. By using the Distributed Smart Space Orchestration System (DS2 OS) dataset [150], the author's experiments showed that the proposed DRL-GAN model outperforms standard DRL in detecting anomalies in imbalanced dataset within the IIoT. However, the proposed model demands substantial computational resources during the training phase. [PERSON] et al. [133] introduced an innovative framework called Feature Grouping and Multi-model Fusion Detector (FGMD) for IDS against adversarial attacks in IoT networks. The framework integrates different models, with each model processing unique subsets of the input data or features to better resist the effects of adversarial attacks. The authors used two existing IoT datasets, MedBIoT [151] and IoTID [152], to validate their model in comparison with three baseline models DT, LSTM, and Recurrent Neural Network (RNN) against adversarial examples which are generated based on a rule-based approach that selects, alters and modifies the features of data samples. The experimental outcomes validated the efficacy of FGMD in countering adversarial attacks, exhibiting a superior detection rate when compared to the baseline models. [PERSON] et al. [134] introduced a state-of-the-art adversarial attack generation approach called the Hierarchical Adversarial Attack (HAA). This approach aims to implement a sophisticated, level-aware black-box attack strategy against GNN-based IDS in IoT networks while operating within a defined budget constraint. In their approach, the authors used a saliency map method to create adversarial instances by detecting and altering crucial feature complements with minimal disturbances. Then, a hierarchical node selection strategy based on the Random Walk with Restart (RWR) algorithm is used to prioritize the nodes with higher attack vulnerability.