Indian Journal of Science and Technology

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ICMPv6 Flood Attack Detection using DENFIS Algorithms


Affiliations
1 National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
2 National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800 USM, Penang
3 Department of Computer Engineering, National Institute of Technology Kurukshetra, Malaysia
 

This paper proposed ICMPv6 Flood Attack Detection using DENFIS algorithms to detect denial of service (DoS) attacks in IPv6 networks. We developed C# application to send the ICMPv6 flood attack packets the flooding packets were generated using different attack rates starting from 1000 Pings to 1500 Pings, and the normal traffic packets were generated using different ping rates starting from 10 Pings to 15 Pings, for each ICMPv6 Packet, RTT was calculated. The dataset consists of 2000 recorded, which divided into two sets: 80% for training and 20% for testing, the proposed proved that we can detect ICMPv6 Flood Attack with low ischolar_main mean square error which about 0.26.

Keywords

Dynamic Evolving Neural Fuzzy Inference System (DENFIS), Denial of service Attack, ICMPv6
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  • ICMPv6 Flood Attack Detection using DENFIS Algorithms

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Authors

Redhwan M. A. Saad
National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
Ammar Almomani
National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
Altyeb Altaher
National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800 USM, Penang
B. B. Gupta
Department of Computer Engineering, National Institute of Technology Kurukshetra, Malaysia
Selvakumar Manickam
National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800 USM, Penang, Malaysia

Abstract


This paper proposed ICMPv6 Flood Attack Detection using DENFIS algorithms to detect denial of service (DoS) attacks in IPv6 networks. We developed C# application to send the ICMPv6 flood attack packets the flooding packets were generated using different attack rates starting from 1000 Pings to 1500 Pings, and the normal traffic packets were generated using different ping rates starting from 10 Pings to 15 Pings, for each ICMPv6 Packet, RTT was calculated. The dataset consists of 2000 recorded, which divided into two sets: 80% for training and 20% for testing, the proposed proved that we can detect ICMPv6 Flood Attack with low ischolar_main mean square error which about 0.26.

Keywords


Dynamic Evolving Neural Fuzzy Inference System (DENFIS), Denial of service Attack, ICMPv6



DOI: https://doi.org/10.17485/ijst%2F2014%2Fv7i2%2F50249