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Regression coefficients of traffic flow metrics (RCTFM) for DDOS defense in IoT networks
Author(s) -
Kalathiripi Rambabu,
N. Venkatram
Publication year - 2021
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4330
Subject(s) - computer science , denial of service attack , frame (networking) , internet of things , scalability , metric (unit) , application layer ddos attack , botnet , computer network , computer security , the internet , world wide web , operations management , database , economics
Summary The term that makes physical objects and devices connect over each other through the wireless networks is called the Internet of Things (IoT). The IoT networks are highly vulnerable to DDoS attacks due to their property of dynamic adapting new devices, even though several existing contributions depicted DDoS defense techniques. However, the features deliberated for notifying DDoS attacks are not competent and constant to attain minimum false alarming and optimum detection accuracy. This article portrayed a novel method called regression coefficient of traffic flow metric (RCTFM) for DDoS defense in IoT networks. This method is used for performing predictive analysis in detecting the scope of DDoS attacks from transactions buffered during a specified static time frame. Unlike the contemporary models, the proposed model considers the traffic that buffered under a given time frame as input and derives regression coefficients of the parameters portrayed as metrics of the corresponding buffered traffic flow. The simulation study evinces the scalability and significance of the proposal that scaled against other existing methods.