# Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms
## 2 Related Work  
In recent years, [PERSON] et al. [16] studied attack detection models in IoT sensors using machine learning methods. They accurately compared the performance of several machine learning techniques in predicting attacks and violations in IoT systems. A robust algorithm was developed for detecting IoT cyberattacks, particularly focusing on virtual environments. The proposed system demonstrated better detection accuracy compared to existing models. [PERSON] et al. [17] proposed DDoS attack mitigation and learning-driven detection in IoT through SDN-Cloud architecture. This approach aims at detecting DDoS attacks launched in IoT servers using malicious wireless IoT. [PERSON] et al. [18] proposed an effective method for classifying network traffic, which uses principal components analysis (PCA) to remove irrelevant features and Gaussian Parsimonious Bayes as a classifier. However, machine learning methods have certain limitations. Firstly, their performance is heavily dependent on the robustness of the feature engineering techniques employed. Secondly, their effectiveness diminishes when applied to large-scale and high-dimensional data. Lastly, their learning capability is insufficient to effectively handle unknown attacks in the IoT environment. The most important advantage of deep learning over traditional machine learning is its superior performance on large datasets. The use of IoT systems usually generates a large amount of data, which is more complex and diverse, and contains a variety of intrusive behaviors. The ability of deep learning to automatically model complex feature sets from sample data makes deep learning more relevant in IoT security applications. For example, [PERSON] et al. [19] proposed the use of DeepIFS integrated with gated recurrent units and a multi-headed attention mechanism to detect intrusions. [PERSON] et al. [20] introduced a federated learning approach for collaborative and decentralized training on edge devices. They utilized LSTM to capture temporal representations and employed attention-enhanced convolutional neural networks to learn important spatial information. The study of Recurrent Neural Networks and their variants is important for improving the security of IoT systems, especially against time-series-based threats. For example, [PERSON] et al. [21] used a variational autoencoder (VAE) as a network baseline and then learned potential temporal representations of the input time series for intrusion detection by RNNs. They also used FNN to parameterize the mean and variance of each time window to provide a non-smooth architecture that can operate in the absence of persistent noise. [PERSON] et al. [22] introduced the LSTM-GaussianNB architecture for evaluating the probability of outliers in IoT data. These research works further demonstrate the suitability of recurrent neural networks as an IoT intrusion detection model. Therefore, some researchers have gone a step further and combined recurrent neural networks with other methods to achieve better detection in the field of IoT intrusion detection. For example, [PERSON] et al. [23] proposed a distributed cloud-based approach based on CNN and LSTM to detect and mitigate phishing and botnet attacks on client devices, which outperformed a single LSTM model. In the last two years of research work, [PERSON] et al. [24] proposed an efficient model called XSRU-IoMT for the effective and timely detection of complex attack traffic in medical IoT. However, the authors tested it using only a single dataset, which was not sufficient to demonstrate the effectiveness of the model. [PERSON] et al. [25] proposed a hierarchical CNN-RNN neural network LuNet, but the generalization ability of the model was poor. Later,Wu et al. proposed a dense residual network Densely-ResNet [15] based on LuNet for secure detection of edge, cloud, and fog layers. Although the generalization ability of the model was improved, it was slightly inadequate and the structure of the model was too complex. [PERSON] et al.