## 1 Introduction  
Network security ensures information confidentiality, integrity, and availability in today's increasingly digital world. As gatekeepers to a network, firewalls play a crucial role in managing and filtering network traffic, thus protecting the system from potential threats [1]. However, manually configuring and updating firewall rules can be challenging, especially given the exponential growth of data traffic and the rapid evolution of cyber threats. A firewall operates as a fundamental component of network security, meticulously regulating both inbound and outbound data traffic by applying a defined set of rules or conditions. Its primary function is safeguarding an organization's digital infrastructure from external threats. Presently, the usage of firewalls extends beyond their conventional protective role, being integrated into various internal domains of organizational networks. Illustratively, firewalls have been leveraged to restrict employee access to sensitive internal systems such as those for financial management or human resource administration [2]. Five distinct categories exist: packet filtering firewalls, circuit-level gateways, application-level gateways (commonly referred to as proxy firewalls), stateful firewalls, and the more recent incarnation known as the Next Generation Firewall (NGFW). Beyond their principal role, firewall devices and services encompass a broader spectrum of security functions. For instance, they offer systems for intrusion detection or prevention (IDS/IPS), safeguards against Denial of Service (DoS) attacks, session surveillance, and an array of additional protective services, thereby fortifying the security architecture of the network [3]. Packet filtering firewalls are a widely used tool for network security. Network administrators set firewall rules to control firewall policies. The firewall examines each packet containing user data and control information and tests them against a predetermined set of rules. If the packet completes the test, the firewall allows it to reach its target. It refuses those who fail the test. Firewalls test packets by examining rule sets, protocols, ports, and destination addresses [4]. To maintain a secure network, it is essential to understand how packet filtering firewalls work and how to set firewall rules properly [5]. Firewall logs are valuable sources of evidence for network security, but analyzing them can be challenging [6]. Research in network intrusion detection often focuses on using anomaly detection to find attacks. Anomaly detection involves monitoring a network's activity for deviations from average profiles learned from benign traffic using machine learning tools [7]. To confront this exigency, the domains of artificial intelligence, machine learning, and deep learning have surfaced as formidable technological instruments for the construction of rigorous security infrastructures capable of expeditiously managing intricate cyberattacks. Various machine learning methodologies, encompassing multiclass Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), have been efficaciously applied for packet categorization and filtration within firewall systems, exemplifying the profound potential of these techniques in fortifying cybersecurity measures [8; 9; 10; 11]. ANNs offer a promising solution among these techniques. Inspired by the functioning of the human brain, ANNs are designed to learn from data and improve their performance over time [12], making them well-suited for detecting patterns in network traffic and enhancing firewall efficiency [13]. The purpose of this research is multifaceted. It addresses critical aspects of firewall packet classification within the network security framework. The principal objective is to introduce and scrutinize a novel methodology incorporating ANNs to efficiently classify network packets within firewall systems, responding to the escalating complexities and diversities in network traffic and the intensifying spectrum of cyber threats.