# AI-Based Approach to Firewall Rule Refinement on High-Performance Computing Service Network
## 2 Related Works  
Table 1 provides an overview of various aspects of cybersecurity covered by previous studies. Each row represents a specific study referenced in the related work section, while the columns represent different aspects or topics related to cybersecurity. Anomaly detection indicates whether the study contributes to techniques or methods for detecting anomalies in systems or networks [12; 13; 14; 15]. Firewall log analysis specifies if the study focuses on the analysis of firewall logs to identify patterns or anomalies. Machine learning indicates whether the study employs machine-learning algorithms or techniques for cybersecurity applications [16; 17; 18; 19; 20; 21; 22]. Deep learning implies that the study utilizes deep-learning models or techniques for cybersecurity tasks [23]. Adversarial attacks indicate whether the study addresses the challenges posed by adversarial attacks, such as evasion or poisoning attacks [24; 25; 26; 27].  
[PERSON] et al. [1] focused on anomaly detection techniques, particularly in system log analysis, emphasizing the importance of analyzing system logs for identifying potential security threats. [PERSON] et al. [4] conducted a detailed analysis of firewall logs, highlighting the traffic and overhead analysis of pre-filtering ACL firewall on HPC service networks. [PERSON] and [PERSON] [5] and [PERSON] et al. [7] explored machine-learning algorithms for the classification of firewall logs and for discovering anomalous rules in firewall logs, respectively. [PERSON] and [PERSON] [8] and [PERSON] et al. [9] utilized machine-learning techniques, including multiclass SVMs and softmax loss function improvement using scalable batch normalization, for firewall log analysis. [PERSON] and [PERSON] [6] and [PERSON] et al. [28] analyzed firewall policies, focusing on the effectiveness of different policy configurations, using machine-learning and data-mining techniques. [PERSON] et al. [11] and [PERSON] et al. [29] surveyed network attack detection methods and proposed a deep learning-enabled web application firewall for detecting web attacks, respectively, emphasizing the role of deep learning in cybersecurity. [PERSON] and [PERSON] [30] enhanced the performance of firewall filters using NNs, and [PERSON] et al. [28] developed a gradient-based approach for adversarial attacks on deep learning-based NIDSs, addressing the challenges associated with adversarial attacks in cybersecurity. [PERSON] et al. [31] surveyed intrusion detection systems based on machine-learning techniques and highlighted the importance of machine learning in protecting critical infrastructures.  
Furthermore, these related reports mention various keywords such as \"Human Error\", \"Error Prevention\", or \"Error Reduction\". [PERSON] et al. [1] focused on system log analysis for anomaly detection, which may indirectly address human errors by identifying abnormal system behaviors that could result from human mistakes. [PERSON] et al. [5] and [PERSON] et al. [7] discussed the classification of firewall logs and anomalous rules, which could involve identifying errors in log data and implementing strategies to eliminate or reduce such errors. [PERSON] et al. [31] surveyed intrusion-detection systems based on machine-learning techniques, which may include discussions on error rates and approaches to improve detection accuracy or reduce false alarms. These related studies deal with errors, whereas our study focuses on human errors that are extensively processed and analyzed. To explain the uniqueness of our study, we provide additional explanations as follows. We analyzed the data collected over a four-year period in a real service-providing environment--the operational environment of the HPC service network. Herein, we describe the configuration of the service network environment, security equipment, and abnormal-detection data collected from these devices. We also define and explain **human error** data extracted from the collected raw data.