DETEKSI SERANGAN DENIAL OF SERVICE MENGGUNAKAN RULE BASED SIGNATURE ANALYSIS PADA JARINGAN INTERNET OF THINGS
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Institusion
Universitas Sriwijaya
Author
DIMAS WAHYUDI (STUDENT ID : 09011281320004)
Deris Stiawan (LECTURER ID : 0003047905)
Deris Stiawan (LECTURER ID : 0003047905)
Subject
TA174.A385 Engineering design--Data processing. Manufacturing processes--Data processing. Computer integrated manufacturing systems. Manufacturing processes--Automation. CAD/CAM systems.
Datestamp
2019-10-02 01:20:00
Abstract :
This research focus on pattern recognition of TCP FIN flood and zbassocflood/association flooding attacks on Internet of Things (IoT) network using rule based signature analysis method. The research was conducted on WiFi and IEEE 802.15.4 communication with normal traffic, attack traffic and combined normal ? attack traffic, fifteen different datasets were generated from these schemes, consisting of normal datasets, attack datasets and normal-attack datasets. The testing was performanced on two stages, there are : (i) testing with Snort Rules as Intrusion Detection System (IDS), and (ii) testing with rule based signature analysis method using Intrusion Detection Engine (IDE) naive string matching. In this research, the measurement of detection result using confusion matrix detection rate method bases on Snort IDS and Intrusion Detection Engine (IDE) naive string matching are presented. The Snort IDS shows that has average 17,7845% of TPR, 0,0266% FPR, 79,9734% TNR, 62,2155% for FNR and the detection accuracy is 26,3268%. While the Intrusion Detection Engine (IDE) using naive string matching that has average percentage 99,9131% of TPR, 0% FPR, 100% TNR, 0,0869% FNR and the detection accuracy is 99,9199%.
This research focus on pattern recognition of TCP FIN flood and zbassocflood/association flooding attacks on Internet of Things (IoT) network using rule based signature analysis method. The research was conducted on WiFi and IEEE 802.15.4 communication with normal traffic, attack traffic and combined normal ? attack traffic, fifteen different datasets were generated from these schemes, consisting of normal datasets, attack datasets and normal-attack datasets. The testing was performanced on two stages, there are : (i) testing with Snort Rules as Intrusion Detection System (IDS), and (ii) testing with rule based signature analysis method using Intrusion Detection Engine (IDE) naive string matching. In this research, the measurement of detection result using confusion matrix detection rate method bases on Snort IDS and Intrusion Detection Engine (IDE) naive string matching are presented. The Snort IDS shows that has average 17,7845% of TPR, 0,0266% FPR, 79,9734% TNR, 62,2155% for FNR and the detection accuracy is 26,3268%. While the Intrusion Detection Engine (IDE) using naive string matching that has average percentage 99,9131% of TPR, 0% FPR, 100% TNR, 0,0869% FNR and the detection accuracy is 99,9199%.
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RAMA_56201_09011281320004_0003047905_01_front_ref.pdf
RAMA_56201_09011281320004_0003047905_02.pdf
RAMA_56201_09011281320004_0003047905_03.pdf
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RAMA_56201_09011281320004_0003047905_05.pdf
RAMA_56201_09011281320004_0003047905_06_ref.pdf
RAMA_56201_09011281320004_0003047905_07_lamp.pdf
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