Optimal Attack or Malicious Activity Detection in VANET Using Ensemble Machine Learning Approach
Keywords:
Malicious Activity Detection, Vehicular Ad-Hoc Network, Ensemble Machine Learning ModelAbstract
In a Vehicular Ad-Hoc Network (VANET), a large number of moving and stationary automobiles form a wireless network. It's a cheap and straightforward way to get data on traffic and cars to command centers. VANET employs a set of protocols to safely transmit data and connect vehicle nodes to the internet. It's not uncommon for VANETs to make use of the Ad-hoc on-demand Distance Vector Protocol (AODV). It is a machine language paradigm that requires minimal processing time and memory. OBUs on vehicles carry out the necessary protocols and procedures for sending messages between vehicles, while RSUs are the fixed links that allow vehicles to communicate with one another. When multiple vehicles transmit data on a single vehicle at the same time, some of the data may be garbled or lost. However, the nodes' roles shift regularly, making routing difficult when a vehicle's software or hardware fails. New attack detection in VANET is developed to address the aforementioned problems. At first, we compiled the information from several online resources. Data cleansing is the process of scrubbing information of duplicates or irrelevant details. By contrasting the results of the developed machine learning-based attack detection in VANET with those of previously established methods and algorithms, we can verify the effectiveness of the latter.
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