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 Improving Intrusion Detection System using an Extreme Learning Machine Algorithm

2019 ◽  
Vol 8 (2S4) ◽  
pp. 234-239
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Back Propagation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Extreme Learning Machines ◽  
Learning Machines

An Intrusion Detection System (IDS) is a system, that checks the network or data for abnormal actions and when such activity is discovered it issues an alert. Numerous IDS techniques are in use these days but one major problem with all of them is their performance. Various works have been done on this issue using support vector machine and multilayer perceptron. Supervised learning models such as support vector machines with related learning algorithms are used to analyze the data which is used for regression analysis and also classification. The IDS is used in analyzing big data as there is huge traffic which has to be analyzed to check for suspicious activities, and also be successful in doing so. Hence, an efficient and fast classification algorithm is required. Machine learning techniques such as neural networks and extreme machine learning are used. Both of these techniques are highly regarded and are considered one of the best techniques. Extreme learning machines are feed forward neural networks which have one hidden layer and no back propagation used for classification. Once the intrusion is detected using IDS through ELM then we are also going to detect the type of intrusion using the Random Forest Technique (Multi class classification) efficiently with a higher rate of accuracy and precision. The NSL_KDD dataset which is very well-known used for the training as well as testing of these IDS algorithms. This work determines that compared to artificial neural network and logistic regression extreme learning machines provide a much better rate of intrusion detection, which is 93.96% and is also proven to be more efficient in terms of execution time of 38 seconds

scholarly journals Abstracting Packet Header Information for Intrusion Detection in High-Speed Networks

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Lawrence B Adewole ◽  
Catherine R Adeyeye ◽  
Adebayo O Adetunmbi ◽  
Bosede A Ayogu ◽  
Olaiya Folorunsho
Keyword(s):  
Intrusion Detection ◽  
Sensitivity And Specificity ◽  
Intrusion Detection System ◽  
High Speed ◽  
Detection System ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Detection Techniques ◽  
Packet Header ◽  
End To End

Increase in network traffic coupled with increasing adoption of end-to-end encryption of network packets are two major factors threatening the potency, or even the relevance, of packet-based intrusion detection techniques. Also, end-to-end encryption makes it nearly impossible for network and host-based intrusion detection system to analyze traffic for potential threats and intrusion, hence, the need for an alternative approach. Flow-based intrusion detection system has been proposed as an alternative to a packet-based intrusion detection system as it relies on information embedded in packet header and various statistical analyses of network flow for detecting intrusion.  This paper proposes packet header information abstraction model for intrusion detection on the UNSW-NB15 intrusion dataset. Four existing classification algorithms which include: Classification and Regression Tree (CART), Naïve Bayes (NB), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) are used to evaluate the degree of representativeness of the proposed model using accuracy, sensitivity and specificity evaluation metrics. An average accuracy of 97.95% was recorded across the four models with the minimum accuracy of 97.76 on SVM and best accuracy of  98.05% on CART while Sensitivity of 1.0 on both CART and NB shows that the model performs well in correctly identifying attacks in the network. The average specificity of 0.98 is also an indication of low false positive.  Results obtained show that the proposed abstraction model achieves high accuracy, sensitivity and specificity. The model can be used as filter on a high-speed network whereby packets flagged as an attack can be subjected to further analysis.Keywords—Data Abstraction, Data Mining,Flow-based, Intrusion detection, Network Security

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