Proceedings ArticleDOI
An Effective Intrusion Detection Classifier Using Long Short-Term Memory with Gradient Descent Optimization
Thi-Thu-Huong Le,Jihyun Kim,Howon Kim +2 more
- pp 1-6
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TLDR
This paper finds the most suitable optimizer among six optimizes for Long Short-Term Memory Recurrent Neural Network (LSTM RNN) model applied in IDS and demonstrates the approach is really efficiency to intrusion detection with accuracy is 97.54%, detection rate is 98.95%, and false alarm rate is reasonable.Abstract:
Intrusion Detection System (IDS) is one of the important issues in network security. IDSs are built to detect both known and unknown malicious attacks. Several machine learning algorithms are used widely in IDS such as neural network, SVM, KNN etc. However, these algorithms have still some limitations such as high false positive and false alarm rate. In this paper, our contribution is to build a classifier of IDS following deep learning approach. We find the most suitable optimizer among six optimizes for Long Short-Term Memory Recurrent Neural Network (LSTM RNN) model applied in IDS. Through our experiments, we found that LSTM RNN model with Nadam optimizer outperforms to previous works. We demonstrate our approach is really efficiency to intrusion detection with accuracy is 97.54%, detection rate is 98.95%, and false alarm rate is reasonable with 9.98%.read more
Citations
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Journal ArticleDOI
Machine Learning and Deep Learning Methods for Cybersecurity
Yang Xin,Lingshuang Kong,Liu Zhi,Yuling Chen,Yanmiao Li,Hongliang Zhu,Mingcheng Gao,Haixia Hou,Chunhua Wang +8 more
TL;DR: This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for network analysis of intrusion detection and provides a brief tutorial description of each ML/DL method.
Journal ArticleDOI
A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks
Kehe Wu,Zuge Chen,Wei Li +2 more
TL;DR: This paper proposes a novel network intrusion detection model utilizing convolutional neural networks (CNNs), which uses CNN to select traffic features from raw data set automatically, and sets the cost function weight coefficient of each class based on its numbers to solve the imbalanced data set problem.
Journal ArticleDOI
Deep Learning Based Multi-Channel Intelligent Attack Detection for Data Security
Feng Jiang,Yunsheng Fu,Brij B. Gupta,Yongsheng Liang,Seungmin Rho,Fang Lou,Fanzhi Meng,Zhihong Tian +7 more
TL;DR: Experimental results validate that the proposed attack detection method greatly outperforms several attack detection methods that use feature detection and Bayesian or SVM classifiers.
Journal ArticleDOI
Deep learning methods in network intrusion detection: A survey and an objective comparison
TL;DR: A taxonomy of deep learning models in intrusion detection is introduced and desirable evaluation metrics on all four datasets in terms of accuracy, F1-score and training and inference time are suggested.
Journal ArticleDOI
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade
TL;DR: This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade.
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