## 3 Deep-Learning-Empowered Security Solutions for IoT Systems
### Abnormality and Intrusion Detection  
Several machine-learning-based strategies are still being used today to detect outliers, anomalies, and intrusions in various IoT platforms [56]. For instance, traffic filtering is a popular method to detect intrusions, which separates legitimate packets from malicious packets using per-packet or batch analysis. Traffic classification is effective, but the process generates more false positives than is typical, making the method less reliable than it would otherwise be. Behavior-based models, on the other hand, are used in the process of locating network intrusions. Both algorithms are adopted in the context of the IoT. For example,  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs. ** & **Techniques** & **Dataset** & **Accuracy** & **Limitations** \\ \hline
[55] & CNN & Bot-IoT & 91.27\% &
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \multicolumn{1}{p{113.8 pt}}{} \\ \multicolumn{1}{p{113. [PERSON] [63] presented a trust-based model to detect intrusions in IoT systems. In contrast to classic methods, the developed approach considers not only the nature and category of traffic but also the level of trust that one has in a given device. As a method for detecting intrusions in IoT networks, combining trust management and traffic classification was suggested in the paper. In light of the fact that traditional methods have been shown to be ineffective against adversarial attacks, the authors of [56] implemented a type of deep learning called a self-normalizing neural network (SNN) and a feed-forward neural network (FNN) to detect intrusions. A new method of network intrusion detection and traffic analysis using an FNN was proposed in [55]. Using the Bot-IoT dataset, the authors compared the performance difference between FNN and SVM models. The results showed the proposed solution was less effective at preventing key-logging attacks and theft of sensitive data via binary classification. As previously mentioned, deep learning has become an important tool in intrusion detection systems (IDS), especially when it comes to heterogeneous IoT networks. The typical structure of deep-learning-based IDS is shown in Figure 5. For instance, [PERSON] et al. [64] applied long short-term memory (LSTM), a type of Recurrent neural network (RNN), to detect intrusions. In a similar study, [PERSON] et al. [65] considered the low computing powers of IoT networks and developed a neural network in a two-tiered structure that can efficiently detect anomalies without consuming a large amount of power. In particular, the first layer in their neural network aims to teach the system normal operation, and the second layer looks out for illegal memory access (IMA). [PERSON] et al. [55] used deep learning methods such as CNNs. [PERSON] et al. [66] suggested intrusion detection by combining a deep belief network (DBN) with a probabilistic neural network (PNN). These studies, to some extent, do not adequately cover the entire spectrum of attacks, including zero-day exploits. Figure 5: IDS based on deep learning. With an increasing number of devices hooked up to the IoT, the danger of zero-day attacks is introduced. In this context, [PERSON] et al. [60] proposed the use of LSTM for detection of unknown network attacks. They examined this method alongside four other deep learning models and five IoT datasets: GRU, LSTM, CNN, CNN-LSTM, and DNN. The proposed model requires an extremely large set of training data and considerable time for training. Network traffic over IoT networks presents a risk, as discussed by the authors of [67]. Several machine learning techniques were adopted for the testing in this work, such as decision trees, naive Bayes, and deep learning. On the other hand, the distributed and decentralized structure is a new challenge for traditional security approaches.