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

Table 1

Summary of drawbacks of related works for detecting flooding attacks.

Ref	Name	Year	Method	Drawback
[15]	Proposed-AODV	2004	Fixed threshold	It uses static threshold value which is not suitable for high mobility environment.; Malicious node can pass the security mechanism by transmitting RREQ packets at a frequency lower than the threshold.
[13]	FAP	2005
[16]	EFS	2006
[18]	B-AODV	2016	Dynamic threshold	It can drop valid request packets of the node moving with high mobility speed if the number of request packets is greater than BI value.; Malicious node can pass the security mechanism by transmitting RREQ packets at a frequency lower than the threshold.
[19]	F-IDS	2017	Dynamic threshold	Performance varies. Using new control packets (ALERT) will increase communication overhead and limit the performance when operating in network environment without attacks.; Malicious node can pass the security mechanism by transmitting RREQ packets at a frequency lower than the threshold.
[20]	SMA2AODV	2017	Dynamic threshold	Malicious node can pass the security mechanism by transmitting the RREQ packets at a frequency lower than the threshold.
[21]	SVMT	2013	SVM	The proposed algorithm uses fixed threshold to detect malicious nodes.
[22]	kNN-AODV	2014	kNN	The algorithm for building training data sets was not presented or justified.