Anomaly detection in cyber security attacks on networks using MLP deep learning

被引：0
|
作者：
Teoh, T. T. [1 ]
Chiew, Graeme [1 ]
Franco, Edwin J. [1 ]
Ng, P. C. [1 ]
Benjamin, M. P. [1 ]
Goh, Y. J. [1 ]
机构：
[1] Univ Technol & Design, Singapore, Singapore
来源：
2018 INTERNATIONAL CONFERENCE ON SMART COMPUTING AND ELECTRONIC ENTERPRISE (ICSCEE) | 2018年
关键词：
Multilayer Perceptron (MLP); cyber security; information security; machine learning; deep learning; big data; decision trees; WEKA; J48; C4.5; ID3;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Malicious traffic has garnered inure attention in recent years, owing to the rapid growth of information technology in today's world. In 2(1417 alone, an estimated loss of 13 billion dollars was made from task are attacks. Malware data in today's contest is massive. To understand such information using primitive methods would be a tedious task, In this publication we demonstrate some of the most advanced deep learning techniques available, multilayer perceptron (MLP) and J48 (also known as C4.5 or ID3) on our selected dataset, Advanced Security Network Metrics & Non-Payload-Based Obfuscations (ASNM-NPBO) to show that the answer to managing cyber security) threats lie in the fore-mentioned methodologies.
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页数：5
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