Decision Tree and Neural Network Based Hybrid Algorithm for Detecting and Preventing Ddos Attacks in VANETS
Kaushik Adhikary1, Shashi Bhushan2, Sunil Kumar3, Kamlesh Dutta4
1Kaushik Adhikary*, Computer Science and Engineering, I.K.Gujral Punjab Technical University, Kapurthala, India.
2Shashi Bhushan, Computer Science and Engineering, Chandigarh Group of Colleges, Landran, India.
3Sunil Kumar, Computer Science and Engineering, Maharaja Agrasen University, Baddi, India.
4Kamlesh Dutta, Computer Science and Engineering, National Institute of Technology, Hamirpur, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 10, 2020. | PP: 669-675 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2652039520/2020©BEIESP | DOI: 10.35940/ijitee.E2652.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The demand of Vehicular Adhoc Networks (VANETs) has been increasing in the area of vehicular and infrastructure communications. It has been felt that there is requirement of sharing of critical information related to safety and traffic management among different types of vehicles in a secure way. To ensure the smooth operation of the network, the availability of network resources is needed. The presence of either malicious vehicles or inaccessibility of network services makes VANET easy target for denial of service (DoS) attacks. The sole purpose of DoS attacks is to prevent the intended users from accessing the available resources and services. When the DoS attack is carried out by multiple vehicles distributed throughout the network, it is referred as Distributed DoS (DDoS) attack. The DDoS attacks are very dangerous and hard to be addressed in real time. The machine learning based DDoS attack detection algorithms have been proposed and presented by the research community in literature. In this paper, a hybrid algorithm of Decision Tree and Neural Network is presented for detecting and preventing different types of DDoS attacks in VANETs with highly efficient results. The simulation based experiments are carried out in order to evaluate and compare the performance of proposed hybrid algorithm with respect to different performance parameters. Based on experiments results, it has been found that the performance of hybrid algorithm has been increased significantly
Keywords: VANETs, DDoS Attack, Decision Tree, Neural Network, Machine learning Algorithm, Hybrid Model etc.
Scope of the Article: Machine learning