Multi-classification of UNSW-NB15 Dataset for Network Anomaly Detection System

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor’s regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

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Multi-criteria decision support for feature selection in network anomaly detection system

International Journal of Data Analysis Techniques and Strategies ◽

10.1504/ijdats.2018.094132 ◽

2018 ◽

Vol 10 (3) ◽

pp. 334 ◽

Cited By ~ 1

Author(s):

C. Seelammal ◽

K. Vimala Devi

Keyword(s):

Feature Selection ◽

Decision Support ◽

Anomaly Detection ◽

Detection System ◽

Network Anomaly Detection ◽

Anomaly Detection System

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Effective and efficient network anomaly detection system using machine learning algorithm

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v8i1.1387 ◽

2019 ◽

Vol 8 (1) ◽

pp. 46-51 ◽

Cited By ~ 7

Author(s):

Mukrimah Nawir ◽

Amiza Amir ◽

Naimah Yaakob ◽

Ong Bi Lynn

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Processing Time ◽

Learning Algorithm ◽

Detection System ◽

Malicious Code ◽

Supervised Machine Learning ◽

Machine Learning Algorithm ◽

Network Anomaly Detection ◽

Anomaly Detection System

Network anomaly detection system enables to monitor computer network that behaves differently from the network protocol and it is many implemented in various domains. Yet, the problem arises where different application domains have different defining anomalies in their environment. These make a difficulty to choose the best algorithms that suit and fulfill the requirements of certain domains and it is not straightforward. Additionally, the issue of centralization that cause fatal destruction of network system when powerful malicious code injects in the system. Therefore, in this paper we want to conduct experiment using supervised Machine Learning (ML) for network anomaly detection system that low communication cost and network bandwidth minimized by using UNSW-NB15 dataset to compare their performance in term of their accuracy (effective) and processing time (efficient) for a classifier to build a model. Supervised machine learning taking account the important features by labelling it from the datasets. The best machine learning algorithm for network dataset is AODE with a comparable accuracy is 97.26% and time taken approximately 7 seconds. Also, distributed algorithm solves the issue of centralization with the accuracy and processing time still a considerable compared to a centralized algorithm even though a little drop of the accuracy and a bit longer time needed.

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