An MIH-enhanced fully distributed mobility management (MF-DMM) solution for real and non-real time CVBR traﬀic classes in mobile internet

SANKARANARAYANAN, Parasuraman; GAYATHRI, Rajaraman; TAMIJETCHELVY, Ramachandiran

doi:10.3906/elk-2101-123

Cited by 2 publications

(1 citation statement)

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“…Denial-of-Service Attacks in Wi-Fi Direct Device-to-Device Networks are thoroughly studied and analyzed. Such attacks force the Wi-Fi connection to drop and prevent the detection of the access point or cellular network ( Sankaranarayanan, Gayathri & Tamijetchelvy, 2021 ; Hadiks et al, 2014 ). Wi-Fi networks are susceptible to impersonation attacks, where SSID or MAC/IP addresses mimic those of legitimate devices ( Liu et al, 2019 ).…”

Section: Literature Workmentioning

confidence: 99%

Connect attack in IoT-WSN detect through cyclic analysis based on forward and backward elimination

S.,

2024

PeerJ Computer Science

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IoT-wireless sensor networks (WSN) have extensive applications in diverse fields such as battlegrounds, commercial sectors, habitat monitoring, buildings, smart homes, and traffic surveillance. WSNs are susceptible to various types of attacks, such as malicious attacks, false data injection attacks, traffic attacks, and HTTP flood attacks. CONNECT attack is a novel attack in WSN. CONNECT attack plays a crucial role through disrupting packet transmission and node connections and significantly impacts CPU performance. Detecting and preventing CONNECT attacks is imperative for enhancing WSN efficiency. During a CONNECT attack, nodes fail to respond to legitimate requests, resulting in connectivity delays, acknowledgment delays, and packet drop attacks in IoT-WSN nodes. This article introduces an Intrusion Detection Algorithm based on the Cyclic Analysis Method (CAM), which incorporates a forward selection approach and backward elimination method. CAM analyzes routing information and behavior within the WSN, facilitating the identification of malicious paths and nodes. The proposed approach aims to pinpoint and mitigate the risks associated with CONNECT attacks, emphasizing the identification of malevolent pathways and nodes while establishing multiple disjoint loop-free routes for seamless data delivery in the IoT-WSN. Furthermore, the performance of CAM is assessed based on metrics such as malicious node detection accuracy, connectivity, packet loss, and network traffic. Simulation results using Matlab software demonstrate superior accuracy in malicious node detection, achieving accuracy in attack detection of approximately 99%, surpassing traditional algorithms accuracy of attack detection.

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Section: Literature Workmentioning

confidence: 99%