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Jamali Fard, A., Shirazi, H. (2015). Web-based Military Management Systems Security Using Combination of One-class Classifiers. Electronic and Cyber Defense, 3(3), 19-30.
Amineh Jamali Fard; Hossein Shirazi. "Web-based Military Management Systems Security Using Combination of One-class Classifiers". Electronic and Cyber Defense, 3, 3, 2015, 19-30.
Jamali Fard, A., Shirazi, H. (2015). 'Web-based Military Management Systems Security Using Combination of One-class Classifiers', Electronic and Cyber Defense, 3(3), pp. 19-30.
Jamali Fard, A., Shirazi, H. Web-based Military Management Systems Security Using Combination of One-class Classifiers. Electronic and Cyber Defense, 2015; 3(3): 19-30.

Web-based Military Management Systems Security Using Combination of One-class Classifiers

پدافند الکترونیکی و سایبری
Article 3, Volume 3, Issue 3, February 2020, Pages 19-30    PDF (1.2 M)
Authors
Amineh Jamali Fard* 1; Hossein Shirazi2
1Master of Computer Engineering, School of Command and Control, Malik Ashtar University of Technology, Tehran, Iran
2Associate Professor, Faculty of Command and Control, Malik Ashtar University of Technology, Tehran, Iran
Receive Date: 07 April 2014,  Revise Date: 21 June 2023,  Accept Date: 19 September 2018 
Abstract
Cyber attacks against the web-based military command systems is very common in the age of
electronic warfare. Web application is one of the most widely used tools in the world wide web. Because of
its dynamic nature, it is vulnerable to serious security risks. Web-based command and control systems
security considerations are very important for the modern military managers. Anomaly based intrusion
detection is an approach that focuses on new and unknown attacks.
A method for anomaly detection in web applications using a combination of one-class classifiers, is
proposed. First, in preprocessing phase, normal HTTP traffic is logged and Features vector is extracted
from each HTTP request. The proposed method consists of two steps; In the training phase, the extracted
features vectors associated with each request, enter the system and the model of normal requests , using
combination of one-class classifiers, is learned. In the detection phase, anomaly detection operation is
performed on the features vector of each each HTTP request using learned model of the training phase.
S-OWA operator is used to combine the one-class classifiers. The data used for training and test are from
CSIC2012 dataset. Detection rate and false alarm rate obtained from experiments, shows better results than
other methods.
Keywords
Military Management; Web-Applications; Intrusion Detection; Combination of One-class Classifiers; S-OWA Operator
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