# Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
## 1 Introduction
### Contributions
The behavior analysis-based method analyzes the behavior of equipment, users, and networks and detects any abnormal behavior [25]. The behavior analysis-based method may have a higher false positive rate because normal behavior may be mistaken for abnormal behavior, thus increasing management and maintenance costs. Artificial intelligence uses machine learning and deep learning algorithms to monitor the behavior of IIoT systems and detect abnormal activities [26]. Machine learning and deep learning algorithms may generate false positives or false negatives, so the algorithms need to be continuously improved and trained. In conclusion, these detection methods require sufficient labeled data in industrial environments, which increases costs. In addition, attackers can use adversarial techniques to deceive detection methods, further increasing the difficulty of defending against APTs in IIoT. 3. Using hardware security, firewalls, and vulnerability management for the security of IIoT equipment and data [16]. Hardware security refers to the use of hardware equipment and security chips to protect the security of IIoT [14; 18]. This method can provide high security but is costly and also carries the risk of supply chain attacks and physical attacks. Firewalls refer to the use of firewalls between the IIoT system and external networks to limit access and traffic [27]. The disadvantage is that firewalls may not be able to prevent all attacks, such as APT attackers who may use disguised traffic or application-based attacks. Vulnerability management refers to the timely discovery and repair of vulnerabilities in the system to avoid APT attacks [28; 29]. This method requires continuous vulnerability scanning and management and timely patching, but this may take a lot of time and effort, and attackers may find new vulnerabilities to attack the system. Overall, there are many methods to prevent APT attacks in IIoT systems, but each method has its own limitations and flaws. Therefore, it is necessary to comprehensively apply these methods and continuously improve and optimize the security protection mechanisms of IIoT systems based on practical situations. To provide a more detailed description and analysis of the APT activities faced by IIoT, this paper proposes a series of recommendations for the security of IIoT networks through a layered study of APTs in industrial IoT. Specifically, the main contributions of this paper are as follows:  
1. A comprehensive introduction to the definition and classification of APTs in industrial IoT. This survey lists the differences between APTs and traditional attacks from different dimensions and explores typical examples of APT attacks. 2. A deep understanding of APTs in IIoT, and a layered study can help reveal the APT attacks faced by IIoT. This survey identifies potential weaknesses and vulnerabilities and APT attacks faced by each layer by analyzing attacks at different levels, such as the perception layer, transmission layer, platform layer, and application layer. 3. Providing effective defense strategies for IIoT security. This survey provides multi-layer security defense mechanisms for IIoT security and helps propose security reinforcement strategies for APTs in IIoT through various models to characterize and analyze APT activities fundamentally. 4. Outlook on the trends and research directions of APTs in future IIoT. This survey points out the current hotspots and challenges of APT research in IIoT, as well as possible solutions and development directions in the future, such as artificial intelligence and machine learning. In a nutshell, this survey conducts a comprehensive and in-depth study of APTs in IIoT and proposes a series of feasible solutions and future research directions. These contributions will help improve the security of IIoT and ensure the stability and reliability of the production process.