# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 6 Research Works in ML-Based Security Systems of IoT Networks
[129] conducted an experiment to assess the efficiency of LSTM, CNN, and Gated Recurrent Unit (GRU) models against adversarial attacks created by FGSM. The evaluation was performed on the CSE-CIC-IDS2018 dataset [143], utilizing three distinct training configurations: training with normal samples, training with adversarial samples, and a hybrid approach involving pretraining with normal samples followed by training with adversarial samples. The results revealed that adversarial training enhanced the robustness of the models, with LSTM showing the most significant enhancement. However, it was observed that adversarial training also led to a reduction in the accuracy of the models when dealing with normal examples. This phenomenon occurred because adversarial training makes the models' decision boundaries more adaptable to adversarial examples, but at the same time, it results in a more fragile decision boundary for normal samples. As a result, the ability of the models to correctly classify normal examples was relatively undermined. [PERSON] et al. [130] assessed the efficiency of the popular adversarial attacks, JSMA, FGSM, and C&W against various ML-based IDSes, such as SVM, Decision Tree (DT), and Random Forest (RF), using multi-class contemporary datasets, BoT-IoT [125] and UNSW-NB15 [27], that represents the contemporary IoT network environment. The study's agenda is to reveal how those several attacks can effectively degrade the detection performance of the three selected target models in comparison to the baseline model Multilayer Perceptron (MLP), and how the performance results vary over the two datasets. The results of the experiment validated the potency of the aforementioned adversarial attacks to decrease the overall effectiveness of SVM, DT, and RF classifiers, respectively for both datasets. However, the decrease in all metrics was less pronounced in the UNSW-NB15 dataset when compared to the Bot-IoT dataset. The limited feature set of Bot-IoT renders it more vulnerable to adversarial attacks. Regarding the attacks, C&W proved to be the most impactful when used with the UNSW-NB15 dataset. In contrast, the FGSM technique displayed robust effectiveness on the Bot-IoT dataset. However, the JSMA had a lesser impact on both datasets. From the classifier's model robustness perspective, the SVM classifier experienced the most significant impact, resulting in an accuracy reduction of roughly 50% in both datasets. Conversely, the RF classifier demonstrated remarkable robustness compared to other classifiers, with only a 21% decrease in accuracy. [PERSON] et al. [131] proposed to evaluate the vulnerability of ML-based IDSes in an IoT smart home network. Various pre-trained supervised ML models, namely J48 DT, RF, SVM, and Naive Bayes (NB) are proposed for DoS attack detection. Using a Smart Home Testbed dataset [144], the authors suggested a Rule-based method to create indiscriminate adversarial samples. For adversarial exploratory attack, the authors proposed to use the Information Gain Filter [145], a feature importance ranking method, to select the crucial features that best distinguish malicious from benign packets. Then, the adversary proceeded to manually manipulate the values of these features, together and one at a time, to force IDSes to wrongly classify the incoming packet. The experiential outcomes revealed that the performance of all IDSes models was impacted by the presence of adversarial packets, resulting in a maximum decrease of 47.2%. On the flip side, the use of adversarial training defense by injecting 10% of generated adversarial samples into the original datasetimproved the models' robustness against adversarial attacks by 25% in comparison to the performance results in the absence of adversarial defense. The approach proposed in this study is restricted to the generation of adversarial examples specifically for DoS attacks, with an exclusive focus on supervised ML-based IDSes. [PERSON] et al.