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ICMP-DDoS Attack Detection Using Clustering-Based Neural Network Techniques

  • 2018
  • OriginalPaper
  • Chapter
Published in:
Authors
Naorem Nalini Devi
Khundrakpam Johnson Singh
Tanmay De
Published in
Operations Research and Optimization
Publisher
Springer Singapore

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Abstract

DDoS comprises of one of the biggest problems in the network security. Monitoring the traffic is the fundamental technique used in order to discover the entity of probable irregularity in the traffic patterns. In this paper, we used SOM to divide the dataset into clusters, as analysis of clusters is easier than the whole dataset. We select the features such as mean inter-arrival time and mean probability of occurrence of the IP addresses that have the greater impact on the DDoS attack from the incoming packets. These features are given as input to the SOM to cluster the structure of similar member in a collection of unlabeled data. The comparison is made between pre-observed features from already trained datasets and features present in each cluster. MLP classifier is used to categorize the incoming clients as normal and attack. In this paper, we used CAIDA 2007 attack datasets and CAIDA 2013 anonymized trace datasets as pre-observed samples. The proposed method detects a DDoS attack with maximum efficiency of 97% and with a low false positive rate of 3.0%.

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previous chapter Ant Lion Optimization: A Novel Algorithm Applied to Load Frequency Control Problem in Power System
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Title
ICMP-DDoS Attack Detection Using Clustering-Based Neural Network Techniques
Authors
Naorem Nalini Devi
Khundrakpam Johnson Singh
Tanmay De
Copyright Year
2018
Publisher
Springer Singapore
Book
Operations Research and Optimization

Print ISBN: 978-981-10-7813-2

Electronic ISBN: 978-981-10-7814-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7814-9_16
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