# Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms
## 1 Introduction  
With the leap forward in next-generation information technology such as 5G, artificial intelligence, and big data, IoT has entered a new era of development. However, connected devices in IoT systems often have low computing and storage capacity and lack uniform standards and protocols, making them easy targets for cyber attacks. Secondly, IoT device manufacturers do not implement strict security measures when producing devices, which makes current IoT devices have many vulnerabilities, which also leads to many potential security threats in the IoT system composed of IoT devices [1]. For example, the Mirai botnet is capable of executing large-scale distributed denial of service attacks via IoT devices [2]; IoT devices are usually connected through wireless networks, and intruders can obtain private information from communication channels through eavesdropping [3]. These cyberattacks not only disrupt the availability of device functionality but may also steal important information and data about the device and the users who use it. Therefore, designing a security approach that can effectively address the cyberattacks faced by IoT has been the focus of research [4]. Implementing security measures such as cryptographic authentication, access control, and firewalls for IoT devices does not fully address the cyberattack problem or guarantee absolute security in the IoT environment [5]. To improve the defense capability of IoT systems, many studies have focused on machine learning-based or deep learning-based intrusion detection methods [6]. An intrusion detection system (IDS) [7] is a networksecurity device that continuously monitors network traffic and issues alerts or takes proactive measures when suspicious activities are detected. The difference with other network security measures is that IDS is a proactive security protection technology. IDS solutions can be classified into three approaches: signature-based, anomaly-based, and hybrid [6]. In general, signature-based approaches are effective against known attacks. However, due to the heterogeneity, dynamics, and complexity of IoT networks, the signature-based approach is less efficient for IoT because it requires continuous human intervention to extract attack patterns and update the IDS model. On the other hand, anomaly-based approaches are effective for unknown attacks, making them advantageous in IoT as they can detect zero-day attacks with minimal human intervention. The hybrid approach combines signature-based and anomaly-based techniques. However, the use of signature-based intrusion detection methods in IoT environments is limited due to their inability to detect unknown attacks. Therefore, anomaly-based intrusion detection systems play a crucial role in IoT security. Some existing anomaly intrusion detection systems use traditional machine learning techniques to build IDS models [8]. These traditional techniques, however, encounter challenges with the high speed and volume of data generated by IoT devices. They rely extensively on feature engineering to extract representative features from the unstructured data, leading to performance degradation when applied to large-scale and high-dimensional datasets. Consequently, several studies have shifted focus toward employing deep learning techniques to develop more effective solutions [9]. Deep learning automates feature extraction and classification by designing multilayer neural networks into architectures and using large amounts of data and computation. The advantage of this approach is that it can perform feature learning without human intervention and can achieve good results when dealing with large or complex datasets. The powerful data processing and feature learning capabilities and the ability to detect unknown attacks that deep learning has can provide advanced solutions for IoT intrusion detection [9; 10; 11; 12]. Therefore, researchers have applied deep learning to the field of IoT intrusion detection and have achieved good results [9; 10; 11; 12; 13; 14].