Quantum beetle swarm algorithm optimized extreme learning machine for intrusion detection

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yumin Dong ◽  
Wanbin Hu ◽  
Jinlei Zhang ◽  
Min Chen ◽  
Wei Liao ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Swarm Algorithm ◽  
Learning Machine

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

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1223 ◽  
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.

Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space

2018 ◽  
Vol 10 (5) ◽  
pp. 848-863 ◽  
Author(s):  
Buse Gul Atli ◽  
Yoan Miche ◽  
Aapo Kalliola ◽  
Ian Oliver ◽  
Silke Holtmanns ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Network Traffic ◽  
Probability Space ◽  
Learning Machine

scholarly journals An Application and Performance Evaluation of Twin Extreme Learning Machine Classifier for Intrusion Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 219-222
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Intrusion Detection System ◽  
Detection System ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Detection Techniques ◽  
Learning Machines ◽  
And Performance ◽  
Learning Machine

Network along with Security is most significant in the digitalized environment. It is necessary to secure data from hackers and intruders. A strategy involved in protection of information from hackers will be termed as Intrusion Detection System (IDS).By taking into nature of attack or the usual conduct of user, investigation along with forecasting activities of the clients will be performed by mentioned system.Variousstrategies are utilized for the intrusion detection system. For the purpose of identification of hacking activity, utilization of machine learning based approach might be considered as novel strategy.In this paper, for identification of the hacking activity will be carried out by Twin Extreme Learning Machines (TELM).Employing the concept of Twin Support Vector Machine with the fundamental structure of Extreme Learning Machine is considered in the establishment of Twin Extreme Learning Machine (TELM).Also, its performance and accuracy are compared with the other intrusion detection techniques

scholarly journals Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Jivitesh Sharma ◽  
Charul Giri ◽  
Ole-Christoffer Granmo ◽  
Morten Goodwin
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Detection System ◽  
Binary Classification ◽  
Detection Systems ◽  
Weighted Extreme Learning Machine ◽  
Three Stages ◽  
Learning Machine ◽  
Multi Class Classification

Abstract Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect each type of attack separately. Finally, the results of all ELMs are combined using a softmax layer to refine the results and increase the accuracy further. The intuition behind our system is that multi-class classification is quite difficult compared to binary classification. So, we divide the multi-class problem into multiple binary classifications. We test our method on the UNSW and KDDcup99 datasets. The results clearly show that our proposed method is able to outperform all the other methods, with a high margin. Our system is able to achieve 98.24% and 99.76% accuracy for multi-class classification on the UNSW and KDDcup99 datasets, respectively. Additionally, we use the weighted extreme learning machine to alleviate the problem of imbalance in classification of attacks, which further boosts performance. Lastly, we implement the ensemble of ELMs in parallel using GPUs to perform intrusion detection in real time.

Sign in / Sign up

Export Citation Format

Share Document