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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Amiza Amir
Universiti Malaysia Perlis
Electrical & Electronics Engineering

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Effective and efficient network anomaly detection system using machine learning algorithm Mukrimah Nawir; Amiza Amir; Naimah Yaakob; Ong Bi Lynn
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (367.295 KB) | DOI: 10.11591/eei.v8i1.1387

Abstract

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.
Snake species identification by using natural language processing Nur Liyana Izzati Rusli; Amiza Amir; Nik Adilah Hanin Zahri; R. Badlishah Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp999-1006

Abstract

The paper presents the snake species identification by using natural language processing. It aims to help medical professionals in predicting the snake species for snake-bite treatments based on the patient’s description of the snake. The decision in suitable anti-venom critically depends on the type of snake species. Wrong anti-venom may result in severe morbidity and mortality. This research investigates the human perception and the selection of words in describing a snake based on their visual view. The descriptions were presented in unstructured text, and the NLP processing involves pre-processing, feature extraction and classification. Four machine learning algorithms (naïve Bayes, k-Nearest Neighbour, Support Vector Machine, and Decision Trees J48) were used during training and classification. Our results show that J48 algorithm obtained the highest classification accuracy of 71.6% correct prediction for the NLP-Snake data set with high precision and recall.
Co-Authors Mukrimah Nawir Naimah Yaakob Nik Adilah Hanin Zahri Nur Liyana Izzati Rusli Ong Bi Lynn R. Badlishah Ahmad