Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier
TLDR
Wang et al. as discussed by the authors proposed a new intrusion detection framework based on the feature selection and ensemble learning techniques, and this framework is able to exhibit better performance than other related and state of the art approaches under several metrics.About:
This article is published in Computer Networks.The article was published on 2020-06-19 and is currently open access. It has received 244 citations till now. The article focuses on the topics: Intrusion detection system & Ensemble learning.read more
Citations
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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.
Journal ArticleDOI
Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection
TL;DR: This paper proposes a multi-dimensional feature fusion and stacking ensemble mechanism (MFFSEM), which can detect abnormal behaviors effectively and significantly outperforms the basic and meta classifiers adopted in the method.
Journal ArticleDOI
A novel IoT network intrusion detection approach based on Adaptive Particle Swarm Optimization Convolutional Neural Network
TL;DR: A novel IoT network intrusion detection approach based on adaptive Particle Swarm Optimization Convolutional Neural Network (APSO-CNN), in which the PSO algorithm with change of inertia weight is used to adaptively optimize the structure parameters of one-dimensional CNN.
Journal ArticleDOI
Automated DDOS attack detection in software defined networking
TL;DR: This paper proposes to classify the benign traffic from DDoS attack traffic by using machine learning technique and shows that the hybrid model of Support Vector classifier with Random Forest (SVC-RF) classifies the traffic with the highest testing accuracy of 98.8% with a very low false alarm rate.
Journal ArticleDOI
Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques.
TL;DR: Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.
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