.png){kind=icon}
返回
An improved PIO feature selection algorithm for IoT network intrusion detection system based on ensemble learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2022.118745.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the rapid growth of the number of connected devices that exchange personal, sensitive, and important data through the IoT based global network, attacks that are targeting security services are increasing as well. Accordingly, there is a need for security solutions that are suitable for IoT environment. A network intrusion detection system (NIDS) is a solution that examines network traffic and alerts system administrators if there are security breaches. In this paper, an enhanced version of pigeon-inspired optimization (PIO) is proposed which enhances PIO by adding a local search algorithm named (LS-PIO). Moreover, an ensemble learning approach, which is based on multiple one-class classifiers, has been used in order to improve the performance of proposed NIDS. Four benchmark datasets were used to evaluate the LS-PIO and ensemble based NIDS which are BoT-IoT, UNSW-NB15, NLS-KDD and KDDCUPP99. The evaluation took into consideration F-score, accuracy, AUC, FPR and TPR. Results show that the suggested approach outperforms other NIDS techniques that are selected from state-of-the-art relevant research found in the literature.
Keyword:
Ensemble learning
LS-PIO
NIDS
One-class classifiers
PIO
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
分享收藏An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection基于混合元启发式算法的物联网入侵检测有效特征选择模型.png)
SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏分享收藏Bagging-TPMiner: a classifier ensemble for masquerader detection based on typical objectsSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study网络安全入侵检测的深度学习: 方法、数据集和比较研究OAAI
OAAI分享收藏A Feature Selection Method Based on Hybrid Improved Binary Quantum Particle Swarm Optimization基于混合改进二进制量子粒子群的特征选择方法IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams用于大数据流离群点检测的局部离群因子算法综述OAAI
OAAI分享收藏
查看更多内容