## 2 Related Work  
The intricate confluence of machine learning and network security has spurred extensive research, underscoring the paramountcy of ANNs in network traffic classification. This area's burgeoning interest necessitates a granular examination of related works to delineate our study's contextual fabric and underscore its significance. [PERSON] et al. (2014) spearheaded investigations in this domain, providing seminal insights into leveraging diverse machine learning techniques, including neural networks, for intrusion detection systems. This research is pivotal, laying the foundational understanding of the synergy between machine learning and network security and guiding our endeavor to explore advanced techniques for enhanced efficacy. Similarly, [PERSON] and [PERSON] (2017) highlighted the pressing need for robust machine-learning models to navigate the multifaceted landscape of network traffic data. Their emphasis on resilient models informs our approach, focusing on developing ANNs to address the highlighted complexities ([PERSON] and [PERSON], 2017). Each subsequent study illuminates distinct facets of this domain. For instance, [PERSON] and [PERSON] (2015) concentrated on the adeptness of various data processing methodologies in analyzing firewall log data. On the other hand, [PERSON] and [PERSON] (2019) and [PERSON] and [PERSON] (2018) explored classification approaches, providing comparative evaluations of classifiers' efficacy using different metrics. These studies enriched our comprehension of classification efficacy, steering our selection and evaluation of techniques. [PERSON] et al. (2016) contrasted deep learning methodologies with conventional machine learning methodologies for intrusion detection, highlighting the former's superiority. This understanding motivated our deep exploration into advanced learning methodologies for optimizing anomaly detection. Moreover, a comprehensive review by [PERSON] et al. (2017) unraveled the multifaceted applications and challenges inherent in internet traffic classification, illuminating the nuances of machine learning-based approaches. [PERSON] et al. (2018) and [PERSON] (2019) probed into the enhancement of firewall systems through diverse ma chine learning models. Their findings corroborate the transformative potential of machine learning models in fortifying firewall operations and network security. [PERSON] and [PERSON] [20] and [PERSON] and [PERSON] [21] accentuated the formidable capabilities of machine learning algorithms coupled with big data in detecting anomalies in firewall policies and identifying complex Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, respectively. Integrating big data with machine learning in these studies informed our methodology, as it emphasized the value of leveraging diverse datasets and sophisticated algorithms for precise anomaly detection. [PERSON] and [PERSON] [21]'s research is pivotal as they utilized a convolutional neural network (CNN) model to detect complex DoS and DDoS attacks, achieving 98% accuracy. This research not only underscores the potential of CNNs in recognizing various attack patterns but also sets a benchmark for our study, inspiring advancements in network security. Drawing parallels, [PERSON] et al. [22] delved into a comparative study, introducing a new neural network architecture--MODLSTM. Their innovative approach demonstrated substantial potential in detecting DoS attacks, contributing pivotal insights for enhancing the reliability of firewall systems against diverse malicious traffic, which is closely related to our research focus. Building on these insights, [PERSON] and [PERSON] [23] engineered an intelligent classification model for firewall systems, utilizing a shallow neural network (SNN). Their model, attaining 98.5% accuracy, surpassed several contemporary firewall classification systems, emphasizing the significance of incorporating advanced models in firewall systems--a concept central to our study. Further, [PERSON] et al.