Wireless intrusion detection based on different clustering approaches

Nambiar, Athira; Vijayan, Asha; Nandakumar, A. N.

doi:10.1145/1858378.1858420

Cited by 7 publications

(1 citation statement)

References 3 publications

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“…To create an NIDS module for LoRa,we explored the IDS of wifi from the works of Athira [9]which proposed an clustering approach on this.The effective approach in LoRa is initially identified based on these works values.The NIDS formulated from the work paper by Danish published in 2018 IEEE conference [10].The paper clearly defines about jamming about detection using NIDS in LoRaWAN protocol.…”

Section: Related Workmentioning

confidence: 99%

LoPT : LoRa Penetration Testing Tool

2019

IJITEE

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The advent of Wireless technologies and IOT are currently ruling the modern world. Everything is going to become Things in future. As the technology progresses , the security of those technologies must also progress with an steady rate. Security tools which will help us to analyze these advanced security enhancements and protocols implemented. In this study , we are going to implement new security tool which concentrates on penetration testing of one such IOT protocol. This tool concentrates on the protocol named LoRa used for wireless long range communication in IOT. The proposed tool will explore all the possible attacks on LoRa protocol which we will see about in detail in the upcoming sections. LoPT is a new penetration testing tool which will work on LoRa (Long Range),a wireless standard used for long range low power communication on IOT devices primarily. This newly bloomed flower performs an effective domination on the field of IOT. Currently there is no existing penetration testing tool for LoRa. Though LoRa has its inbuilt security , there are major vulnerabilities which can be explored . This tool is built primarily on the concept of There's no such thing as 100Index Terms-LoRa , Pentest Tool for LoRa , LoRa Attack tool, LoRa vulnerabilities explorer 1.

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Section: Related Workmentioning

confidence: 99%