Title
TDDEHT: Threat Detection Using Distributed Ensembles of Hoeffding Trees on Streaming Cyber Datasets
Abstract
The use of a well-known state-of-the-art classifier, Hoeffding Trees, is generally proposed in data stream mining (DSM) approaches. Most of these approaches generally address achieving improved accuracy when exceedingly complex drifts are present. Unfortunately, only a few minor DSM approaches have been proposed for anomaly-based Intrusion Detection Systems (IDS). Despite the common relation between anomalies and concept-drift. These approaches also validate with outdated cyber datasets. In this paper, we propose an enhanced IDS ensemble framework of distributed diverse Hoeffding Trees built on Spark Streaming. The pivotal component is an extensible framework to include additional Linear Classifiers and essential IDS components. To validate the efficiency of our approach, we perform several experiments using various up-to-date real-world, synthetic cyber-attack and concept-drift datasets. Our results demonstrate IDS evaluation metrics in the 80-90 percentile and an increase in speed and marginal increase in accuracy and Kappa Statistic, when compared to the current state-of-the-art DSM platform, MOA.
Year
DOI
Venue
2018
10.1109/MILCOM.2018.8599734
MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM)
Keywords
Field
DocType
anomaly-based intrusion detection systems,linear classifiers,DSM platform,IDS components,IDS ensemble framework,threat detection using distributed ensembles of Hoeffding trees,data stream mining approaches,kappa statistic,MOA,cyber datasets streaming,IDS evaluation metrics,concept-drift datasets,synthetic cyber-attack,pivotal component,Spark Streaming,TDDEHT
Data mining,Data stream mining,Spark (mathematics),Computer science,Computer network,Cohen's kappa,Classifier (linguistics),Intrusion detection system,Percentile
Conference
ISSN
ISBN
Citations 
2155-7578
978-1-5386-7186-3
0
PageRank 
References 
Authors
0.34
3
4
Name
Order
Citations
PageRank
Asmah Muallem100.34
Sachin Shetty232355.94
Liang Hong319333.79
Jan Wei Pan400.34