Network Intrusion Detection Based on Lightning Search Algorithm Optimized Extreme Learning Machine

Network Intrusion Detection Systems (IDSs) have been widely used to monitor and manage network connections and prevent unauthorized connections. Machine learning models have been utilized to classify the connections into normal connections or attack connections based on the users' behavior. One of the most common issues facing the IDSs is the detection system's low classification accuracy and high dimensionality in the feature selection process. However, the feature selection methods are usually used to decrease the datasets' redundancy and enhance the classification performance. In this paper, a Chaotic Salp Swarm Algorithm (CSSA) was integrated with the Extreme Learning Machine (ELM) classifier to select the most relevant subset of features and decrease the dimensionality of a dataset. Each Salp in the population was represented in a binary form, where 1 represented a selected feature, while 0 represented a removed feature. The proposed feature selection algorithm was evaluated based on NSL-KDD dataset, which consists of 41 features. The results were compared with others and have shown that the proposed algorithm succeeded in achieving classification accuracy up to 97.814% and minimized the number of selected features.

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Network Intrusion Detection Based on Kernel Principal Component Analysis and Extreme Learning Machine

2018 IEEE 18th International Conference on Communication Technology (ICCT) ◽

10.1109/icct.2018.8600104 ◽

2018 ◽

Author(s):

Yuan Zhou ◽

Le Yu ◽

Mingshan Liu ◽

Yuanyuan Zhang ◽

Helin Li

Keyword(s):

Principal Component Analysis ◽

Intrusion Detection ◽

Extreme Learning Machine ◽

Principal Component ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Learning Machine

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Network Intrusion Detection Method Based on Stacked Denoising Sparse Autoencoder and Extreme Learning Machine

2020 2nd International Conference on Information Technology and Computer Application (ITCA) ◽

10.1109/itca52113.2020.00048 ◽

2020 ◽

Author(s):

Guoling Zhang ◽

Xiaodan Wang ◽

Rui Li ◽

Jie Lai ◽

Qian Xiang ◽

...

Keyword(s):

Intrusion Detection ◽

Extreme Learning Machine ◽

Detection Method ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Sparse Autoencoder ◽

Learning Machine

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Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

Energies ◽

10.3390/en12071223 ◽

2019 ◽

Vol 12 (7) ◽

pp. 1223 ◽

Cited By ~ 10

Author(s):

Jianlei Gao ◽

Senchun Chai ◽

Baihai Zhang ◽

Yuanqing Xia

Keyword(s):

Intrusion Detection ◽

Extreme Learning Machine ◽

Detection System ◽

Principal Component ◽

Detection Accuracy ◽

Network Attack ◽

Network Intrusion ◽

Learning Machine ◽

Malicious Behaviors ◽

Adaptive Principal Component Analysis

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.

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