Anomaly detection combining one-class SVMs and particle swarm optimization algorithms

Jiang, Tian; Gu, Hong

doi:10.1007/s11071-009-9650-5

Cited by 42 publications

(22 citation statements)

References 27 publications

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“…Currently, RSM is effective to continuously improve the accuracy and efficiency in structural reliability by reducing the number of expensive finite element analysis [21][22][23][24][25]. The typical surrogate models include polynomial response surface method [15,16,21,22] and support vector machine (SVM) [25][26][27][28][29][30][31][32]. The polynomial response surface method fits a low-order polynomial to a set of experimental data by least squares regression analysis [15,16,31,32].…”

Section: Introductionmentioning

confidence: 99%

“…The SVM which is regarded as an intelligent statistical learning method with implicit performance function has been applied to the reliability analysis and optimal design of complex structure instead of the polynomial response surface method, because SVM has the advantages of smaller training samples, higher computational accuracy and efficiency. Just because of these advantages, the SVM is also applied to anomaly detection for signals [30,31] and nonlinear predictive control of controller [32]. To further improve the computing speed for SVM model, the least squares SVM (LSSVM) was developed and proved to be an effective method for structural steady reliability analysis [26].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

Fei

Choy

et al. 2016

Nonlinear Dyn

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To develop the high performance and high reliability of turbomachinery just like an aeroengine, distributed collaborative time-varying least squares support vector machine (LSSVM) (called as DC-T-LSSVM) method was proposed for the dynamic probabilistic analysis of high-pressure turbine bladetip radial running clearance (BTRRC). For structural transient probabilistic analysis, time-varying LSSVM (called as T-LSSVM) method was developed by improving LSSVM, and the mathematical model of the T-LSSVM was established. The mathematical model of DC-T-LSSVM was built based on T-LSSVM and distributed collaborative strategy. Through the dynamic probabilistic analysis of BTRRC with respect to the nonlinearity of material property and the dynamics of thermal load and centrifugal force load, the probabilistic distributions and features of different influential parameters on BTRRC, such as rotational speed, the temperature of gas, expansion coefficients, the surface coefficients of heat transfer and the deformations of disk, blade and casing, are obtained. The deformations of turbine disk, blade and casing, the rotational speed and the temperature of gas significantly influence BTRRC. Turbine disk and blade perform the positive effects on the BTRRC, while turbine casing has the negative impact. The comparison of four methods (Monte Carlo method, T-LSSVM, DCERSM and DC-T-LSSVM) reveals that the DC-T-LSSVM reshapes the possibility of the probabilistic analysis of complex turbomachinery and improves the computational efficiency while preserving the accuracy. The efforts offer a useful insight for rapidly designing and optimizing the BTRRC dynamically from a probabilistic perspective.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

Fei

Choy

et al. 2016

Nonlinear Dyn

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“…A novel intrusion detection framework based on particle swarm optimization (PSO) was proposed by Jiang Tian and Gu [70] which had combined the idea of unsupervised learning method and the supervised strategy. Instead of calculating the accuracy, ROC analysis was utilized to evaluate the detection performance.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

Ganapathy

Kanagasabai

Muthurajkumar

et al. 2013

J Wireless Com Network

169

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Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper.Keywords: Survey; Intrusion detection system; Neural networks; Fuzzy systems; Swarm intelligence; Particle swarm intelligence Review Intrusion detection systemsRecently, Internet has become a part and parcel of daily life. The current internet-based information processing systems are prone to different kinds of threats which lead to various types of damages resulting in significant losses. Therefore, the importance of information security is evolving quickly. The most basic goal of information security is to develop defensive information systems which are secure from unauthorized access, use, disclosure, disruption, modification, or destruction. Moreover, information security minimizes the risks related to the three main security goals namely confidentiality, integrity, and availability.Various systems have been designed in the past to identify and block the Internet-based attacks. The most important systems among them are intrusion detection systems (IDS) since they resist external attacks effectively. Moreover, IDSs provide a wall of defense which overcomes the attack of computer systems on the Internet. IDS could be used to detect different types of attacks on network communications and computer system usage where the traditional firewall cannot perform well. Intrusion detection is based on an assumption that the behavior of intruders differ from a legal user [1]. Generally, IDSs are broadly classified into two categories namely anomaly and misuse detection systems based on their detection approaches [2,3]. Anomaly intrusion detection determines whether deviation from the established normal usage patterns can be flagged as intrusions. On the other hand, misuse detection systems detect the violations of permissions effectively. Intrusion detection systems can be built by u...

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“…Support vector regression (SVR) is later exploited to cope with the regression estimation and function approximation problems. SVR has shown breakthroughs and excellent performance in many areas [17][18][19][20][21]. Since the quality of SVR models depends on a proper setting of SVR parameters, the key issue for applying SVR is how to set its parameters.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer

Xia

Chen

Tang

et al. 2015

Neural Comput & Applic

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As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems to be solved, such as the modeling problem. This paper adopts support vector regression (SVR) to construct the prediction model of blast furnace silicon content. To ensure a good generalization performance for the given datasets, it is important to select proper parameters for SVR. In view of this problem, a new particle swarm optimizer called DMS-PSO-CLS is presented to optimize the parameters of SVR. In DMS-PSO-CLS, a new cooperative learning strategy is hybridized with DMS-PSO, which makes particle information be used more effectively for generating better-quality solutions. DMS-PSO-CLS takes merits of the DMS-PSO and the cooperative learning strategy so that both the convergence speed and the convergence precision can be improved. Experimental results show that DMS-PSO-CLS can find the optimal parameters of SVR with high speed and the SVR model optimized by DMS-PSO-CLS can achieve a good regression precision on the predictive problem of blast furnace.

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Anomaly detection combining one-class SVMs and particle swarm optimization algorithms

Cited by 42 publications

References 27 publications

Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer

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