Experimentations with OpenStack System Logs and Support Vector Machine for an Anomaly Detection Model in a Private Cloud Infrastructure
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020
Abstract
Anomaly detection is a crucial aspect of cloud
computing that is becoming increasingly challenging. This is
because a huge amount of system logs is usually generated in both
private and public cloud infrastructure, thereby complicating
manual inspection by System Administrators. In order to address
this challenge, an experimental investigation was carried out in
this study towards the development of an anomaly detection model
for OpenStack private cloud infrastructure. Firstly, OpenStack
system logs were curated from the Loghub corpus as experimental
dataset for the study. The logs were parsed using Iterative
Partitioning Log Mining (IPLoM) algorithm to produce
structured event log templates. Discriminative numerical features
were extracted from the event log templates using Term Frequency
Inverse Document Frequency (TF-IDF) algorithm. Thereafter,
Support Vector Machine (SVM) classifier with varying kernels
was trained to evolve an acceptable classifier experimentally. The
SVM classifier with linear and RBF kernels outperformed other
kernels with acceptable accuracy, precision, recall and Fmeasure.
Description
Keywords
Cloud Computing, IPLoM, OpenStack, TF-IDF, System Logs, Covenant University, Digital Development, ACE: Applied Informatics and Communication