Extreme learning machines for Internet traffic classification
نویسندگان
چکیده
Network packet transport services (namely the Internet) are subject to significant security issues. This paper aims to apply Machine Learning methods based on Neural Networks (Extreme Learning Machines or ELM) to analyze the Internet traffic in order to detect specific malicious activities. This is performed by classifying traffic for a key service run over the internet: the Domain Name System (DNS). The ELM models and algorithms are run on DNS traffic data extracted from operating networks for botnet detection.
منابع مشابه
A Comparative Study of Extreme Learning Machines and Support Vector Machines in Prediction of Sediment Transport in Open Channels
The limiting velocity in open channels to prevent long-term sedimentation is predicted in this paper using a powerful soft computing technique known as Extreme Learning Machines (ELM). The ELM is a single Layer Feed-forward Neural Network (SLFNN) with a high level of training speed. The dimensionless parameter of limiting velocity which is known as the densimetric Froude number (Fr) is predicte...
متن کاملStable Rough Extreme Learning Machines for the Identification of Uncertain Continuous-Time Nonlinear Systems
Rough extreme learning machines (RELMs) are rough-neural networks with one hidden layer where the parameters between the inputs and hidden neurons are arbitrarily chosen and never updated. In this paper, we propose RELMs with a stable online learning algorithm for the identification of continuous-time nonlinear systems in the presence of noises and uncertainties, and we prove the global ...
متن کاملA New Method for Detecting Ships in Low Size and Low Contrast Marine Images: Using Deep Stacked Extreme Learning Machines
Detecting ships in marine images is an essential problem in maritime surveillance systems. Although several types of deep neural networks have almost ubiquitously used for this purpose, but the performance of such networks greatly drops when they are exposed to low size and low contrast images which have been captured by passive monitoring systems. On the other hand factors such as sea waves, c...
متن کاملSupport Vector Machines for TCP traffic classification
Support Vector Machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to the problem of traffic classification in IP networks. In the case of SVMs, there are still open questions that need to be addressed before they can be generally applied to traffic classifiers. Having being designed essentially as techniques for binary classification, their genera...
متن کاملOutlier Detection Using Extreme Learning Machines Based on Quantum Fuzzy C-Means
One of the most important concerns of a data miner is always to have accurate and error-free data. Data that does not contain human errors and whose records are full and contain correct data. In this paper, a new learning model based on an extreme learning machine neural network is proposed for outlier detection. The function of neural networks depends on various parameters such as the structur...
متن کامل