Security Evaluation Method Research Articles

An improved RBF neural network method for information security evaluation

Itis well-known that information security means the protection of information,and ensuring the availability, confidentiality and integrity of information. The purposeof this paper is to present an improved RBF neural network method forinformation evaluation. Ant colony optimization is a multi-agent approach fordifficult combinatorial optimization problems, which has been applied tovarious NP hard problems. Here, ant colony optimization algorithm is applied tooptimize the parameters of RBF neural network. In this paper, we employ “unauthorized access”, “unauthorized accessto a system resource”, “data leakage”, “denial of service”, “unauthorizedmodification data and software”, “system crash” as the features of informationsecurity evaluation. It is indicated that the information security evaluation error of the improvedRBF neural network is smaller than that of the RBF neural network. Thus, theimproved RBF neural network is very suitable for information security evaluation. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4806

A Quantitative Network Situation Assessment Method Based on Stochastic Model

This paper describes the basic models of network security state evaluation system and concentrates on researching the situation assessment method with stochastic model. In the paper, which makes use of the Hidden Semi-Markov Model (HsMM), tries to simulate the operation of network system. The alert statistics, deriving from network defense system, is used as data sources to realize the evaluation of network security situation. HsMM modifies the HMM model concerning the hypothesis of some state-duration time in relation to exponential distribution, which coincides the description of the network systems operation in the real world. The experimental results imply that HsMM is an ideal security evaluation method.

Network Security Situation Evaluation Strategy Based on Cloud Gravity Center Judgment

Aiming at the problems of subjectivity and complexity in network security situation assessment process, the Cloud model is introduced to the network security situation assessment, and a network security evaluation method based on Cloud Model is proposed. Firstly, the level of network security situation is judged by Cloud gravity center evaluation method. Secondly, Maximum boundary based Cloud Model (MCM) method is applied to obtain the final assessment results. Thirdly, future security situation is predicted by adopting BP neural network based on improved genetic algorithm according to the result of situation assessment. Finally, the effectiveness and feasibility of the proposed evaluation method and the prediction effect for GA-BPNN are verified by the simulation experiment. The results show that the proposed evaluation and prediction method is feasible, and the results of the assessment are more objective and accurate.

Development of an ecological security evaluation method based on the ecological footprint and application to a typical steppe region in China

The steppes of Inner Mongolia lie in a region which are sensitive to global climate change. The region forms an important ecological barrier against sandstorms and it is also strategically important for the development of China's energy and mineral resources. To describe the influence of resources exploitation on the ecological security of the typical Inner Mongolian steppe, we developed a consumption footprint pressure index (CFPI) and a production footprint pressure index (PFPI) based on the ecological footprint concept, and developed an ecological footprint contribution index (EFCI) to assess the pressures created by transferring resources and products from output areas to input areas. Using these indices, we developed a coupled ecological security assessment model to evaluate the ecological security level of the typical steppe. We used the model to calculate CFPI, PFPI, and EFCI for the steppe area for three counties and one urban region of Inner Mongolia from 2001 to 2010. We found that CFPI and PFPI increased throughout the study period in most regions. In addition, EFCI was generally positive, which indicated the ecological security of the typical steppe was affected primarily by the electricity and production output processes. Our results suggest that the ecological security of the study area has been at serious risk since 2005.

A Regulation-Based Security Evaluation Method for Data Link in Wireless Sensor Network

This article presents a novel approach to the analysis of wireless sensor networks (WSN) security, based on the regulations intended for wireless communication devices. Starting from the analysis and classification of attacks, countermeasures, and available protocols, we present the current state on secure communication stacks for embedded systems. The regulation analysis is based on civil EN 50150 and MIL STD-188-220, both applicable to WSN communications. Afterwards, starting from a list of known WSN attacks, we use a correspondence table to match WSN attacks with countermeasures required by regulations. This approach allows us to produce a precise security evaluation and classification methodology for WSN protocols. The results show that current protocols do not present a complete coverage of security issues. While this conclusion is already known for many WSN protocols, to the best of our knowledge this is the first time a complete methodology is proposed to base this assertion. Moreover, by using the proposed methodology, we are able to precisely identify the exposed threats for each WSN protocol under analysis.

Network security situational assessment based on link performance analysis

Concerning the fusion essence of network situational assessment and the defect of being unaware of the unknown attack in the existing Analytic Hierarchy Process( AHP), a network security evaluation method based on link security situation was proposed. With the help of the theory of network performance analysis, the network security situational assessment model based on link performance analysis was proposed. Firstly, the paper calculated every link's security situational value and showed them in a matrix; secondly, used every link's weight and security situational value to get the network security situational value which was shown in vector. The experimental results show that the proposed method can not only reflect the changes of both the partial and entire security situation but also apperceive unknown attack, which provides administrator with much convenience.

Geopolitical Energy Security Evaluation Method and Its Application Based on Politics of Scale

Abstract. This paper rebuilds geo-energy security evaluation model, and evaluates China's geo-oil energy security in Russia's pacific oil pipeline construction from 1995 to 2010 quantitatively. The results showed that: (1) from the time point of view, the geo-oil safety index of China in the Russia's Pacific oil pipeline construction rising; (2) from the geopolitical relationship point of view, the China- Japan energy competition index tended to decrease, but the competition of energy imports of the two countries from Russia intensified; (3) Russia's energy export strategy has long been biased towards Europe, but it has been turned to the trend, the energy export strategy index of Russia has dropped significantly; (4) to a certain extent, the proportion stability of China’s oil consumption and China-Russian friendly relations eased China's geo-oil energy security; (5) from the geographical structure point of view, the overall national strength of China increasing which driven by China's economic growth, will aggravates the North east Asia’s geooil competition.

MODELO DE AVALIAÇÃO DA MATURIDADE DA SEGURANÇA DA INFORMAÇÃO

Business processes are supported by information technologies, although many processes and information systems were not designed to be secure. The lack of a security evaluation method might expose organizations to several risky situations. This work presents an information security maturity management process which uses a measurement method and a set of controls which treat information security on a comprehensive way. The results indicate that the method is efficient for evaluating the current state of information security, to support information security management, risks identification, the improvement of business processes and internal control processes.

Researches on a Novel Railway Tunnel Security Evaluation System and Method

Researches on a Novel Railway Tunnel Security Evaluation System and Method

Static Security analysis in Real time using ANN

This paper presents a method for automatic contingency selection and static security evaluation of electrical power systems. The method employs multi-layer Perceptron neural networks whose inputs are power flows and injections, while the outputs identify potentially harmful contingencies. The performance of the method is evaluated for different operating conditions using the IEEE 24 bus test system. It is shown that the neural network classifiers perform very well the contingency selection task and enables a previous classification of system operating state with respect to static security..

Development of Energy Security Evaluation Method using Mathematical Programming and Analysis of Optimal Strategy for Fuel Stockpile Operation

Development of Energy Security Evaluation Method using Mathematical Programming and Analysis of Optimal Strategy for Fuel Stockpile Operation

Pattern analysis and classification for security evaluation in power networks

Security evaluation is a major concern in real time operation of electric power networks, exhibiting behavioral patterns under abnormal conditions. Security assessment and evaluation can be viewed as a pattern analysis task identifying abnormal patterns of the power system behavior under highly loaded conditions. Traditional method of security evaluation are highly time consuming and infeasible for direct on-line implementation. This paper presents application of pattern directed inference system for static and transient security evaluation and classification. A straightforward and quick procedure called Sequential Forward Selection method is used for feature selection process. The classifier model in the pattern directed inference system is designed using different pattern classifier algorithms, viz., conventional, neural network and machine learning classifiers. Support Vector Machine (SVM), one of the popular machine learning classifier, is recognized as a suitable pattern classifier for security evaluation problem. The generalization performance of SVM classifier is greatly influenced by the proper setting of its parameters. This paper also addresses different heuristic optimization techniques used in the selection of SVM parameters. The design, development and performance of different classifiers for power system security classification are presented in detail. Simulation work is performed on standard New England 39-bus benchmark system and the feasibility of implementation of the proposed SVM based classifier system for on-line security evaluation is also discussed.

A Review of Regional Ecological Security Evaluation

This paper introducts and compares the conceptof ecological security and other related concepts (including ecilogical risk, ecosystem health, ecosystem servers and ecofogical capacity), sums up the review to assess regional ecological security theory frames (including PSR, PSIR,DSR,DPSIR and DPSER) and evaluation methods (including entropy method, principal component analysis, analytic hierarchy process, grey correlation method, fuzzy comprehensive evaluation method, artificial neural networks, matter-element analysis, ecological footprint, landscape ecological security pattern method and digital ecological security model), compares the advantages and disadvantages of various methods, and discussed regional ecological security evaluation method development direction.

Environmental Security Evaluation Method for Oil Field

Oil field exploitations have a considerable impact on environmental security. The method of oil field environmental security evaluation method should be introduced into oil field exploitations for protection of environmental security. The frame model of pressure-state-response (P-S-R) was selected for oil field environmental security evaluation. Assessment levels were classified into five levels according to the value of synthetical index (SI), which was determined by the calculation of information entropies and value assignment of analytic hierarchy process. Finally, quantitative assessments of oil field environmental security situation were presented.

Design of pattern recognition system for static security assessment and classification

Static security analysis is an important study carried out in the control centers of electric utilities. Static security assessment (SSA) is the process of determining whether the current operational state is in a secure or emergency (insecure) state. Conventional method of security evaluation involves performing continuous load flow analysis, which is highly time consuming and infeasible for real-time applications. This led to the application of pattern recognition (PR) approach for static security analysis. This paper presents a more efficient design of a PR system suitable for on-line SSA. The feature selection stage in the PR system uses many algorithms to select the optimal feature set. This paper proposes the use of Support Vector Machine (SVM), a recently introduced machine learning tool, in the classifier design stage of PR system. The developed PR system is implemented in IEEE standard test systems for SSA and classification. The performance of SVM classifier is compared with the conventional K-nearest neighbor, method of least squares and neural network classifiers. Simulation results prove that the SVM-PR classifier outperforms other equivalent classifier algorithms, giving high classification accuracy and less misclassification rate. The feasibility of SVM-PR classifier for on-line security assessment process is also presented.

A Kind of Network Security Evaluation Method Based on Local Variable Weight

The basis to build effective network security protection is to reasonably evaluate on network security. Based on analysis on shortcomings of existing network security evaluation methods, the paper built network security evaluation index system according to CC specifications. The advantages of local variable weight comprehensive method were analyzed and the method of network security evaluation based on combination of AHP and local variable weight was presented. The evaluation example on some network elements indicates that the method evaluate on overall information security level, so that the evaluation result be objective and reasonable.

Particle swarm optimization based K-means clustering approach for security assessment in power systems

Security assessment is a major concern in planning and operation studies of a power system. Conventional method of security evaluation performed by simulation involves long computer time and generates voluminous results. This paper presents a K-means clustering approach for classifying power system states as secure/insecure under a given operating condition and contingency. This paper demonstrates how the traditional K-means clustering algorithm can be profitably modified to be used as a classifier algorithm. The proposed algorithm combines particle swarm optimization (PSO) with the traditional K-means algorithm to satisfy the requirements of a classifier. The proposed PSO based K-means clustering technique is implemented in IEEE 30 Bus, 57 Bus, 118 Bus and 300 Bus standard test systems for static security and transient security evaluation. The simulation results of the proposed algorithm are compared with unsupervised K-means clustering, which uses different methods for cluster center initialization.

A Security Evaluation Method Based on Threat Classification for Web Service

Web service is a distributed computing model constructed on the basis of open standard technology with the characteristics of loose coupling, language neutrality, platform-independence, etc., how to efficiently evaluate the security of Web service is a challenging research topic. Current researches concern more about the testing of Web service and rarely about the issue of service security evaluation. On the basis of analyzing the current Web services in terms of security threats, a Web service security evaluation method based on threat classification is proposed, which can process security evaluation to Web service from different angles of view, such as spoofing, tampering, repudiation, message disclosure, denial of service and elevation of privilege, and can provide a referential evaluation index of Web service security for the users through the threat modeling and evaluating the degree of security. Finally, a case study on SOA application is discussed in detail, experimental results show that the proposed model works efficiently, it can provide valuable reference to check out security vulnerabilities of Web service and help to optimize the system’s security design.

Static Security Evaluation in Power Systems usingMulti-Class SVM with Different Parameter SelectionMethods

Security evaluation is a major concern in real time operation of electric power systems. Traditional method of security evaluation performed by continuous load flow analysis involves long computer time and generates voluminous results. This paper presents a practical and feasible Support Vector Machine Based Pattern Classification (SVMBPC) approach for static security evaluation in power systems. The proposed approach classifies the security status of any given operating condition in one of the four classes - Secure, Critically Secure, Insecure and Highly Insecure based on the computation of a numeric value called security index. The feature selection stage uses a simple and straightforward forward sequential method to select the best feature set from a large set of variables. The static security classifier is designed by a multi-class SVM with different parameter tuning methods. The proposed approach is implemented in New England 39 bus and IEEE 118 bus systems and the results are validated.

Supervised fuzzy C-means clustering technique for security assessment and classification in power systems

Security assessment is an important concern in planning and operation studies of an electric power system. Conventional method of security evaluation is performed by simulation consisting of load flow program and transient stability analysis, consuming long computer time and generating voluminous results. This paper presents a practical Pattern Recognition (PR) approach for security assessment in power systems. The problem of security assessment is focused in two modes, viz., static and transient. Static security pertains to the study of violation of system components when subjected to contingencies like line/generator outages. Transient Security study deals with system dynamic behavior when subjected to severe perturbations like three phase faults. A Supervised Fuzzy C-Means (SFCM) algorithm is proposed in the classification phase of PR system for security assessment. The proposed algorithm is tested on 39 Bus New England and IEEE 57 Bus systems. The classification results of the proposed SFCM classifier is compared with the Method of Least Squares (MLS) and Multilayer Perceptron (MLP) classifiers. The results prove that the former gives high classification accuracy and less misclassification rate compared to the latter, enhancing the feasibility and applicability of SFCM algorithm for on-line security evaluation. Keywords: Security Assessment, Static Security, Transient Security, Pattern Recognition, Fuzzy C-Means.