## 2 Related Works
The suggested framework achieves a 96.5% attack prediction ratio, a 97.8% efficiency ratio, a 98.2% accuracy ratio, a 21.3% reduction in latency, and an 18.9% reduction in communication cost, according to the experimental data. Elsewhere, the authors of [21] proposed a model that combined the capabilities of hybrid convolutional long short-term memory (ConvLSTM) methods with correlations in the various physiological data intended to detect contextual and datapoint abnormalities in the massive WBAN datastream. The results of the experiments using the proposed model reported an average accuracy of 99% and a 98% F1-measure on various dataset subjects, compared to 64% for the CNN and LSTM when used separately. However, that study did not examine adversarial attacks. As a result, this paper will extend their contribution to the literature in order to investigate the ability of the model regarding adversarial attack detection using deep learning techniques. Furthermore, it suggests suitable countermeasures for enhancing the ability of the model against the adversarial examples introduced in the training or testing phases. Table 1 summarizes the existing studies and provides an analysis in terms of the mechanism for anomaly detection, dealing with adversarial attacks, and proposing a defense method. An investigation of the existing literature shows that in order to increase the efficacy of anomaly detection strategies in WBANs, several methodologies, including statistical and ML techniques, have been developed by researchers. However, these methods have some drawbacks. For instance, statistical approaches cannot handle the dynamic nature of WBANs, making it challenging to identify a suitable evaluation threshold value. In addition, due to their processing complexity, non-parametric statistical methods are unsuitable for real-time applications. Numerous contemporary methodologies have employed ML algorithms, including decision trees, linear regression, ANN, and the k-nearest neighbor and random forest algorithms. In a sensitive domain that needs great accuracy and good performance, such as in healthcare, better solutions may be found than the deployment of machine learning. These algorithms have drawbacks when dealing with complicated and large datasets, which slow down processing, and are not effective in anticipating new anomalous patterns. Deep learning is recommended by the research community due to its better performance; it reduces the necessity of handcrafted feature engineering and increases performance compared with traditional machine learning. Evaluating the model's performance is considered the most significant step in deep learning development. However, adversarial attacks have recently been compromising its effectiveness and have led to misclassification in deep learning models, affecting the model's performance. After reviewing the published literature, it is clear that most studies have not fully investigated the scenario of adversarial attacks, such as in [4; 5; 7]. Although the machine and deep learning techniques used will encounter several types of possible security concerns in the various phases, one of the most pressing concerns is the adversarial attack, wherein the system's adversaries are highly motivated to change the outcomes of models or to source personal data for their own gain. For example, attackers can cause misclassification by manipulating the data samples. In addition, few studies have investigated adversarial attacks, such as in [17; 18; 20], but the authors have not proposed any defensive method by which to protect the models. Defense methods in intelligent networks aim to distinguish malicious activity from the common