DIADL: An Energy Efficient Framework for Detecting Intrusion Attack Using Deep LearnIing
Pages 138 - 142
Abstract
In today's era, the Internet's complexity, accessi- bility, and openness has greatly enhanced the security risk of information systems. The Internet's popularity entails many threats of network attacks. Detection of intrusion is one of the major research problems in network security, with the goal of detecting unauthorized access or attacks to secure internal networks. Key ideas are to discover useful patterns or features that characterize a system's user behavior and use the collection of similar features to construct classifiers that can, ideally in real time, detect anomalies and known intrusions. In this proposal, energy efficient framework is designed which detects the intrusion. We predicted the bi- nary and multi class classification by the model with tuned parameters at different levels of learning. The deep learning system enhances the intrusion detection accuracy and provides a new intrusion detection research method. Using a collection of KDD (Knowledge Discovery and Data Mining) benchmark data, we demonstrate that successful and accurate classifiers can be constructed to detect intrusions. In literature, numerous machine learning methods have addressed intrusion detection systems. By analyzing the existing intrusion detection systems, we validated the proposed framework with deep learning.
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- DIADL: An Energy Efficient Framework for Detecting Intrusion Attack Using Deep LearnIing
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Published In
June 2020
219 pages
ISBN:9781450377034
DOI:10.1145/3408066
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In-Cooperation
- Central Queensland University
- DUT: Dalian University of Technology
- University of Wollongong, Australia
- Swinburne University of Technology
- University of Technology Sydney
- National Tsing Hua University: National Tsing Hua University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 August 2020
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ICCMS '20
ICCMS '20: The 12th International Conference on Computer Modeling and Simulation
June 22 - 24, 2020
QLD, Brisbane, Australia
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