سنة النشر

2023

العنوان

Improving the AODV Routing Protocol Against Network Layer Attacks Using AntNet Algorithm in VANETs

الخلاصة

Vehicular Ad Hoc Network (VANET) is the ad hoc wireless technology which uses vehicle nodes connected with each other’s to form a network. The basic function of this network is providing a connection between vehicles to enable the transmission of data. Security is an important issue that must be provided by VANETs. Moreover, one of the most challenges of this network is their vulnerability of security since it has a large number with high speed of vehicles which may lead it to lay under different types of security attacks. Attacks of the network layer can be classified based on routing operation phase. One of the most widely used routing protocols is Ad Hoc on Demands Distance Vector (AODV). AODV is a reactive routing protocol, which is established to make connection between nodes only as required. This research aims to eliminate the effect of attacks and improve the performance of AODV in VANET. Three types of network layer attacks were used to examine their effect on VANET (blackhole, flooding, and rushing attacks). In addition to proposing the combination of more than one type of routing attacks at the same time to show their effect on AODV in VANET, we also proposed the use of AntNet algorithm to eliminate the effect of attacks. The simulation results show that the blackhole attack has a higher effect on AODV compared with the flooding and rushing attacks. The proposed combination of attacks on network showed a high damage on its performance compared with using individual network attacks on AODV in VANET. Simulation results also show various AntNet algorithm efficiencies in the improvement of VANET performance under network attacks. They show in this research that the AntNet has higher efficiency in reducing blackhole attack effect with 29.83%, then, with rushing attack, the percentage of enhancement is 16.82%, and with flooding attack, AntNet has the lowest enhancement efficiency with value of 9.04%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.