Proceedings Article10.1109/CIAPP.2017.8167184
Machine learning based network intrusion detection
Chie-Hong Lee,Yann-Yean Su,Yu-Chun Lin,Shie-Jue Lee +3 more
- 01 Sep 2017
- pp 79-83
43
TL;DR: Experimental results show the equality constrained-optimization-based extreme learning machine applied to network intrusion detection is effective in building models with good attack detection rates and fast learning speed.
read more
Abstract: Network security has become a very important issue and attracted a lot of study and practice. To detect or prevent network attacks, a network intrusion detection (NID) system may be equipped with machine learning algorithms to achieve better accuracy and faster detection speed. One of the major advantages of applying machine learning to network intrusion detection is that we don't need expert knowledge as much as the black or white list model. In this paper, we apply the equality constrained-optimization-based extreme learning machine to network intrusion detection. An adaptively incremental learning strategy is proposed to derive the optimal number of hidden neurons. The optimization criteria and a way of adaptively increasing hidden neurons with binary search are developed. The proposed approach is applied to network intrusion detection to examine its capability. Experimental results show our proposed approach is effective in building models with good attack detection rates and fast learning speed.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection
TL;DR: A Multi-Class Combined performance metric is proposed to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters and a uniform distribution based balancing approach is developed to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset.
305
Intrusion detection based on Machine Learning techniques in computer networks
Ayesha S Dina,Dakshnamoorthy Manivannan +1 more
- 01 Dec 2021
TL;DR: A comprehensive survey of ML-based intrusion detection approaches presented in the literature in the last ten years can be found in this article, where the authors present a comprehensive critical survey of machine learning techniques used for intrusion detection.
89
Machine learning models to detect the blackhole attack in wireless adhoc network
TL;DR: From this work it is understood that, in IDSs while implementing the feature selection technique the accuracy and detection rates are improved, and this work aims to attain better accuracy and speed.
85
Intrusion Detection System Using Random Forest on the NSL-KDD Dataset
Prashil Negandhi,Yash Trivedi,Ramchandra S. Mangrulkar +2 more
- 01 Jan 2019
TL;DR: Taking advantage of the robust NSL-KDD dataset, the supervised learning algorithm random forests is employed to train a model to detect various networking attacks and shows that the model not only runs faster but also performs with a higher accuracy.
70
Intrusion Detection System using Machine Learning Techniques: A Review
Usman Shuaibu Musa,Megha Chhabra,Aniso Ali,Mandeep Kaur +3 more
- 01 Sep 2020
TL;DR: An overview of various works being done on building an efficient IDS using single, hybrid and ensemble machine learning (ML) classifiers, evaluated using seven different datasets are presented to give a clear path and guide for future work.
70
References
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Least Squares Support Vector Machine Classifiers
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
Extreme Learning Machine for Regression and Multiclass Classification
Guang-Bin Huang,Hongming Zhou,Xiaojian Ding,Rui Zhang +3 more
- 01 Apr 2012
TL;DR: ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
5.7K
Optimization method based extreme learning machine for classification
TL;DR: Under the ELM learning framework, SVM's maximal margin property and the minimal norm of weights theory of feedforward neural networks are actually consistent and ELM for classification tends to achieve better generalization performance than traditional SVM.
927
CANN: An intrusion detection system based on combining cluster centers and nearest neighbors
TL;DR: A novel feature representation approach, namely the cluster center and nearest neighbor (CANN) approach, which shows that the CANN classifier not only performs better than or similar to k-NN and support vector machines trained and tested by the original feature representation in terms of classification accuracy, detection rates, and false alarms.
522