DOAN HOANG

BAPRP: a machine learning approach to blackhole attacks prevention routing protocol in vehicular Ad Hoc networks

Publisher: Springer Science and Business Media LLC

Date: 04-06-2023

DOI: 10.1007/S10207-023-00705-Y

Trust-Based Scheduling Framework for Big Data Processing with MapReduce

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2022

DOI: 10.1109/TSC.2019.2938959

Congestion-Aware and Energy-Aware Virtual Network Embedding

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 02-2020

DOI: 10.1109/TNET.2019.2958367

FAPRP: A Machine Learning Approach to Flooding Attacks Prevention Routing Protocol in Mobile Ad Hoc Networks

Publisher: Hindawi Limited

Date: 10-01-2019

DOI: 10.1155/2019/6869307

Abstract: Request route flooding attack is one of the main challenges in the security of Mobile Ad Hoc Networks (MANETs) as it is easy to initiate and difficult to prevent. A malicious node can launch an attack simply by sending an excessively high number of route request (RREQ) packets or useless data packets to nonexistent destinations. As a result, the network is rendered useless as all its resources are used up to serve this storm of RREQ packets and hence unable to perform its normal routing duty. Most existing research efforts on detecting such a flooding attack use the number of RREQs originated by a node per unit time as the threshold to classify an attacker. These algorithms work to some extent however, they suffer high misdetection rate and reduce network performance. This paper proposes a new flooding attacks detection algorithm (FADA) for MANETs based on a machine learning approach. The algorithm relies on the route discovery history information of each node to capture similar characteristics and behaviors of nodes belonging to the same class to decide if a node is malicious. The paper also proposes a new flooding attacks prevention routing protocol (FAPRP) by extending the original AODV protocol and integrating FADA algorithm. The performance of the proposed solution is evaluated in terms of successful attack detection ratio, packet delivery ratio, and routing load both in normal and under RREQ attack scenarios using NS2 simulation. The simulation results show that the proposed FAPRP can detect over 99% of RREQ flooding attacks for all scenarios using route discovery frequency vector of sizes larger than 35 and performs better in terms of packet delivery ratio and routing load compared to existing solutions for RREQ flooding attacks.

University Of Technology Sydney

Location: Australia

View Organisation

Discovery Projects - Grant ID: DP0449607

Start Date: 2004

End Date: 12-2008

Amount: $150,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0774207

Start Date: 02-2007

End Date: 12-2012

Amount: $310,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0211564

Start Date: 2002

End Date: 12-2004

Amount: $50,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0452394

Start Date: 03-2005

End Date: 12-2009

Amount: $257,466.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0668542

Start Date: 05-2006

End Date: 06-2009

Amount: $425,000.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0354584

Start Date: 03-2004

End Date: 11-2004

Amount: $10,000.00

Funder: Australian Research Council