Hybrid ECD model for firewall tuning and attack detection

The rigorous security requirements and domain experts are necessary for the tuning of firewalls and for the detection of attacks. Those firewalls may create an incorrect sense or state of protection if they are improperly configured. One of the major configuration problems in firewalls is related to misconfiguration in the access control roles added to the firewall that will control network traffic. Furthermore, Software-Defined Networking (SDN) has greatly improved the network management. In this research, a hybrid Deep Learning (DL)-based firewall is designed. The request log is sent to the primary firewall, which tracks the network traffic and restricts the vulnerabilities and undesirable traffic. The EfficientNet-B3-Attn-2 fused Cascade Neuro-Fuzzy Network (ECD) is developed for network security whenever the primary firewall fails to regulate the network traffic. Furthermore, the devised framework is evaluated in terms of accuracy, sensitivity and specificity metrics that yield values like 0.885, 0.946 and 0.915.