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Network Attack Detection With SNMP-MIB Using Deep Neural Network
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Author(s): Mouhammd Sharari Alkasassbeh (Computer Science Department, Princess Sumaya University for Technology, Jordan)and Mohannad Zead Khairallah (Computer Science Department, Princess Sumaya University for Technology, Jordan)
Copyright: 2020
Pages: 11
Source title:
Handbook of Research on Intrusion Detection Systems
Source Author(s)/Editor(s): Brij B. Gupta (National Institute of Technology, Kurukshetra, India)and Srivathsan Srinivasagopalan (AT&T, USA)
DOI: 10.4018/978-1-7998-2242-4.ch004
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Abstract
Over the past decades, the Internet and information technologies have elevated security issues due to the huge use of networks. Because of this advance information and communication and sharing information, the threats of cybersecurity have been increasing daily. Intrusion Detection System (IDS) is considered one of the most critical security components which detects network security breaches in organizations. However, a lot of challenges raise while implementing dynamics and effective NIDS for unknown and unpredictable attacks. Consider the machine learning approach to developing an effective and flexible IDS. A deep neural network model is proposed to increase the effectiveness of intrusions detection system. This chapter presents an efficient mechanism for network attacks detection and attack classification using the Management Information Base (MIB) variables with machine learning techniques. During the evaluation test, the proposed model seems highly effective with deep neural network implementation with a precision of 99.6% accuracy rate.
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