Seyed Mojtaba Hosseini Bamakan

An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization

Many organizations recognize the necessities of utilizing sophisticated tools and systems to protect their computer networks and reduce the risk of compromising their information. Although many machine-learning-based data classification algorithm has been proposed in network intrusion detection problem, each of them has its own strengths and weaknesses. In this paper, we propose an effective intrusion detection framework by using a new adaptive, robust, precise optimization method, namely, time-varying chaos particle swarm optimization (TVCPSO) to simultaneously do parameter setting and feature selection for multiple criteria linear programming (MCLP) and support vector machine (SVM). In the proposed methods, a weighted objective function is provided, which takes into account trade-off between the maximizing the detection rate and minimizing the false alarm rate, along with considering the number of features. Furthermore, to make the particle swarm optimization algorithm faster in searching the optimum and avoid the search being trapped in local optimum, chaotic concept is adopted in PSO and time varying inertia weight and time varying acceleration coefficient is introduced. The performance of proposed methods has been evaluated by conducting experiments with the NSL-KDD dataset, which is derived and modified from well-known KDD cup 99 data sets. The empirical results show that the proposed method performs better in terms of having a high detection rate and a low false alarm rate when compared with the obtained results using all features. Time-varying inertia weight and acceleration coefficients is introduced to CPSO.Feature selection and parameter setting applied simultaneously to MCLP and SVM.A weighted objective function is proposed to evaluate the proposed IDSs framework.Penalized MCLP is introduced to deal with unbalanced datasets.Proposed IDSs framework obtained low false alarm rate and high detection rate.

A New Intrusion Detection Approach Using PSO based Multiple Criteria Linear Programming

Abstract Intrusion detection system (IDS) is an inseparable part of each computer networks to monitor the events and attacks, which many researchers proposed variety of models to improve the performance of IDS. In this paper we present a new method based on multiple criteria linear programming and particle swarm optimization to enhance the accuracy of attacks detection. Multiple criteria linear programming is a classification method based on mathematical programming which has been showed a potential ability to solve real-life data mining problems. However, tuning its parameters is an essential steps in training phase. Particle swarm optimization (PSO) is a robust and simple to implement optimization technique has been used in order to improve the performance of MCLP classifier. KDD CUP 99 dataset used to evaluate the performance of proposed method. The result demonstrated the proposed model has comparable performance based on detection rate, false alarm rate and running time compare to two other benchmark classifiers.

Ramp loss K-Support Vector Classification-Regression; a robust and sparse multi-class approach to the intrusion detection problem

A robust and sparse multi-class approach for Multi-Class classification is proposed.The proposed method is based on Ramp loss K-Support Vector Classification-Regression.The CCCP procedure is used to solve a non-differentiable non-convex optimization problem.ADMM is adopted to make our model well-adapted for the large-scale setting.The results of Ramp-KSVCR show superior generalization power and low computational cost. 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 ConcaveConvex 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.

A Novel Feature Selection Method based on an Integrated Data Envelopment Analysis and Entropy Model

Abstract Data mining is a one of the growing sciences in the world that can play a competitive advantages rule in many firms. Data mining algorithms based on their functions can be divided in four categories; Classification, Feature selection, Assassination rules and Clustering. One of the most important of these functions is feature selection which has been increasingly developed and many researchers provide variety of algorithms to deal with this function in recent years. Feature selection algorithms mostly used for obtaining more precise and strong machine learning algorithms along with reducing the computation time. Another growing science is Multiple Criteria Decision Making techniques witch it also has variety of methods. In this paper, we use both Data Envelopment Analysis which is a useful technique for determining the efficiency of decision-making units and Entropy method which its function is weighting the criteria to selecting the appropriate features. Hence, our novel integrated method has been analyzed by implementing in a testing environment and we apply it on three datasets of UCIs datasets, so the result showed our innovated approach has comparable accuracy with the other feature selections algorithms.

A Weighted Monte Carlo Simulation Approach to Risk Assessment of Information Security Management System

In recent decades, information has become a critical asset to various organizations, hence identifying and preventing the loss of information are becoming competitive advantages for firms. Many international standards have been developed to help organizations to maintain their competitiveness by applying risk assessment and information security management system and keep risk level as low as possible. This study aims to propose a new quantitative risk analysis and assessment methodology which is based on AHP and Monte Carlo simulation. In this method, AHP is used to create favorable weights for Confidentiality, Integrity and Availability CIA as security characteristic of any information asset. To deal with the uncertain nature of vulnerabilities and threats, Monte Carlo simulation is utilized to handle the stochastic nature of risk assessment by taking into account multiple judges opinions. The proposed methodology is suitable for organizations that require risk analysis to implement ISO/IEC 27001 standard.

Large-scale Nonparallel Support Vector Ordinal Regression Solver

Abstract Large-scale linear classification is widely used in many areas. Although SVM-based models for ordinal regression problem are proven to be powerful techniques, the performance with nonlinear kernels are often suffering from time consuming. Recently, linear SVC not only is shown to obtain competitive performance in most of the cases, but also it is considerably fast during the process of training and testing. However, few studies focused on linear SVM-based ordinal regression models. In this paper, we propose a new approach, called linear Nonparallel Support Vector Ordinal Regression (NPSVOR), which can deal with large-scale problems. An efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) is designed to solve the proposed model. Our experiments are performed on large document data sets to demonstrate the effectiveness of the proposed method.

Opinion leader detection: A methodological review

Abstract A social network as an essential communication platform facilitates the interactions of online users. Based on the interactions, users can influence or be affected by the opinions of others. The users being able to influence and shape the opinions of others are considered as opinion leaders. The problem of identifying opinion leaders is an important task due to its wide applications in reality, including product adoption for marketing and societal analytics. The problem has been attracting proliferating studies over the recent years. To overview and provide insights of the methodologies and enlighten the future study, we review the well-known techniques for opinion leader detection problems. These techniques are classified into descriptive approaches, statistical and stochastic methods, diffusion process based approaches, topological based methods, data mining and learning methods, and approaches based on hybrid content mining. The advantages and drawbacks of each method are systematically analyzed and compared, to provide deep understanding into the existing research challenges and the direction of future trends. The findings of this review would be useful for those researchers are interested in identifying opinion leaders and influencers in social networks and related fields.

Ramp loss one-class support vector machine; A robust and effective approach to anomaly detection problems

Abstract Anomaly detection defines as a problem of finding those data samples, which do not follow the patterns of the majority of data points. Among the variety of methods and algorithms proposed to deal with this problem, boundary based methods include One-class support vector machine (OC-SVM) is considered as an effective and outstanding one. Nevertheless, extremely sensitivity to the presence of outliers and noises in the training set is considered as an important drawback of this group of classifiers. In this paper, we address this problem by developing a robust and sparse methodology for anomaly detection by introducing Ramp loss function to the original One-class SVM, called “Ramp-OCSVM”. The main objective of this research is to taking the advantages of non-convexity properties of the Ramp loss function to make robust and sparse semi-supervised algorithm. Furthermore, the Concave–Convex Procedure (CCCP) is utilized to solve the obtained model that is a non-differentiable non-convex optimization problem. We do comprehensive experiments and parameters sensitivity analysis on two artificial data sets and some chosen data sets from UCI repository, to show the superiority of our model in terms of detection power and sparsity. Moreover, some evaluations are done with NSL-KDD and UNSW-NB15 data sets as well-known and recently published intrusion detection data sets, respectively. The obtained results reveal the outperforming of our model in terms of robustness to outliers and superiority in the detection of anomalies.