Network intrusion detection problem is an ongoing challenging research area because of a huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks, constantly changing the nature of new at...
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Network intrusion detection problem is an ongoing challenging research area because of a huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks, constantly changing the nature of new attacks and the attackers' methods. Since the traditional network protection methods fail to adequately protect the computer networks, the need for some sophisticated methodologies has been felt. In this paper, we develop a precise, sparse and robust methodology for multi-class intrusion detection problem based on the Ramp Loss k-support vector classification-regression, named "Ramp-kSVCR". The main objectives of this research are to address the following issues;1) Highly imbalanced and skewed attacks' distribution;hence, we utilized the k-SVCR model as a core of our model;2) Sensitivity of SVM and its extensions to the presence of noises and outliers in the training sets, to cope with this problem, Ramp loss function is implemented to our model;3) and since the proposed Ramp-kSVCR model is a non-differentiable non-convex optimization problem, we took Concave-Convex Procedure (CCCP) to solve this model. Furthermore, we introduced Alternating Direction Method of Multipliers (ADMM) procedure to make our model well-adapted to be applicable in the large-scale setting and to reduce the training time. The performance of the proposed method has been evaluated by some artificial data and also by conducting some experiments with the NSL-kDD data set and UNSW-NB15 as a recently published intrusion detection data set. Experimental results not only demonstrate the superiority of the proposed method over the traditional approaches tested against it in terms of generalization power and sparsity but also saving a considerable amount of computational time. (C) 2017 Elsevier B.V. All rights reserved.
The generalization is one of the main concerns of machine learning method. k-SVCR is an important multi-class classification algorithm, but there is no theoretical analysis on the generalization of k-SVCR algorithm up...
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The generalization is one of the main concerns of machine learning method. k-SVCR is an important multi-class classification algorithm, but there is no theoretical analysis on the generalization of k-SVCR algorithm up to now. Therefore, in this paper, we consider multi-class supportvectorclassification and regression (MSVCR) algorithm. We first establish the generalization bounds of MSVCR based on uniformly ergodic Markovian chain (u.e.M.c.) samples, and obtain its fast learning rate of MSVCR. As applications, we estimate the generalization of MSVCR for independent and identically distributed (i.i.d.) observations and strongly mixing observations, respectively. We also propose a new MSVCR algorithm based on q-times Markovian resampling (MSVCR-Mar-q). The experimental studies indicate that compared to the classical k-SVCR and other multi-class SVM algorithms, the proposed algorithm not only has smaller misclassification rate, but also has less sampling and training total time.
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