Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with number vehicles and road side units keep fluctuating real world. susceptible to security issues, particularly DoS attacks, owing maximum unpredictability location. So, effective identification the classification attacks have major requirements for secure data transmission VANET. At same time, congestion control also one key research problems which aims at minimizing time expended on roads calculating travel as well waiting intersections, traveler. With this motivation, current paper presents intelligent attack detection Congestion Control (IDoS-CC) technique The presented IDoS-CC involves two-stage processes namely, Teaching Learning Based Optimization (TLBO)-based (TLBO-CC) Gated Recurrent Unit (GRU)-based (GRU-DoSD). goal reduce level detect exist network. TLBO algorithm involved optimization routes taken by via traffic signals minimize particular route instantaneously so assure minimal fuel utilization. applied avoid roadways. Besides, GRU-DoSD model employed effectively discriminate compromised genuine outcomes from series simulation analyses highlight supremacy proposed it reduced successfully identified