## 5 Challenges
### Security Issues of DL Applications in IoMT
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DL Models** & **Application in IoMT** & \begin{tabular}{c} **Tractional Technology** \\ **Performance** \\ \end{tabular} & \begin{tabular}{c} **Enhanced Performance** \\ **with DL** \\ \end{tabular} \\ \hline \begin{tabular}{c} Gated Recurrent Unit \\ (GRU) [100] \\ \end{tabular} & \begin{tabular}{c} Sequential data analysis in \\ real-time systems \\ \end{tabular} & \begin{tabular}{c} With RNN, the accuracy rate is \\ 88.25\% [207]. Traditional \\ method has difficulty in \\ detecting complex patterns \\ \end{tabular} & With GRU, 95.15\% accuracy rate in predicting heart diseases [207] \\ \hline \begin{tabular}{c} Bidirectional Recurrent \\ Neural Networks (BRNNs) [101] \\ \end{tabular} & \begin{tabular}{c} Sequence learning for tasks \\ requiring context from both \\ past and future \\ \end{tabular} & \begin{tabular}{c} Tradition method has difficulty \\ detecting complex patterns \\ \end{tabular} & 95.47\% accuracy rate [208] \\ \hline \begin{tabular}{c} Graph Neural Networks \\ (GNNs) [102] \\ \end{tabular} & \begin{tabular}{c} Modeling relationships \\ between entities in \\ medical data \\ \end{tabular} & \begin{tabular}{c} Manual analysis, long \\ R\&D cycles \\ \end{tabular} & \begin{tabular}{c} Accelerated drug discovery with \\ AI-driven insights, 99.98\% \\ accuracy [209] \\ \end{tabular} \\ \hline \begin{tabular}{c} Generative Adversarial \\ Networks (GANs) [103] \\ \end{tabular} & \
\begin{tabular}{c} Data augmentation, \\ synthetic image generation \\ \end{tabular} &
\begin{tabular}{c} High radiation dose required \\ for clarity \\ \end{tabular} & 4x resolution improvement with \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._behaviors, such as excessive packet drops and unusual route changes, can be flagged as a potential hole attack [214]. Attack by a blackhole: Within this attack, a malicious node, known as a blackhole, discards all packets received from its neighboring nodes that are intended for further transmission. The attack becomes even more severe when the blackhole node acts as a sinkhole, disrupting all data flow in its vicinity. Consequently, communication in the affected area halts entirely. This type of attack is often referred to as \"selfishness\" in the literature [215]. Since blackhole attacks manipulate network routing, GNNs could be leveraged to analyze network topologies and detect inconsistencies in routing behavior. When node traffic is missing, it will be flagged as suspicious [214; 216]. Grayhole attack: This variation of the blackhole attack, often referred to as \"selective forwarding\" or \"select and forwarding\", involves a rogue node selectively dropping only a portion of the packets it receives. This strategy makes identifying the attack challenging for an IDS. In multi-hop networks, packet forwarding is a crucial responsibility of routing nodes. However, malicious nodes intentionally discard specific messages in a selective forwarding attack, ensuring they are not passed to neighboring nodes. Unlike traditional blackhole attacks, which risk detection as neighboring nodes may seek alternative routes, the grayhole attack mitigates suspicion by forwarding some of the traffic to neighboring nodes, thereby reducing the likelihood of being exposed [217]. A wormhole attack occurs when an attacker creates malicious connections in the network, enabling it to transmit packets faster than the usual data transfer rate. This creates a \"wormhole\" in the network, leading to significant disruption [136]. Since grayhole attacks involve selective packet drops, a traditional rule-based IDS may struggle to detect them. DL-based IDS models can learn subtle traffic anomalies and flag suspicious nodes. For instance, DL can be used to train network nodes to bypass grayhole attackers by learning the safest routes and rewarding nodes that maintain a high packet delivery ratio while penalizing malicious nodes [218].