In todayʹs network infrastructures, Software-Defined Firewalls (SDFs) combined with adaptive, machine learning (ML)-driven rule management need to be employed in order to meet the scalability needs, real-time attack reaction, an‎d dynamic policy deployment needs. Conventional firewalls statically configured cannot be positioned to han‎dle the dynamic changes in network topologies as well as emerging cyber attacks. This seminar report proposes a machine learning-enhanced system model of SDFs that enables real-time traffic classification, anomaly detection, an‎d rule updation. The system utilizes supervised an‎d unsupervised learning-based algorithms to dynamically updat‎e to threats an‎d optimize rule sets to minimize false positives an‎d performance bottlenecks. The methodology involves developing an‎d eva‎luating a prototype in a Software-Defined Networking (SDN) setup with the assistance of tools such as Mininet an‎d NS-3,. The most important criteria for assessment are detection accuracy, scalability, an‎d adaptability. It aids in further developing intelligent firewall technologies an‎d gives a robust, proactive security solution for the security of cloud-native an‎d high-speed digital networks.

6/9/2025 12:00:00 AM

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1404/03/21

An Overview of Software-Defined Firewalls for Computer Networks

فايروال‌هاي نرم‌افزارمحور (SDF) , مديريت قوانين مبتني بر يادگيري ماشين , امنيت تطبيقي ​​شبكه، , طبقه‌بندي ترافيك و تشخيص ناهنجاري , شبكه نرم‌افزارمحور (SDN)

Software-Defined Firewalls (SDFs) , Machine Learning-Based Rule Management , Adaptive Network Security , Traffic Classification an‎d Anomaly Detection , Software-Defined Networking (SDN)