Minqiang Li

A security risk analysis model for information systems: Causal relationships of risk factors and vulnerability propagation analysis

With the increasing organizational dependence on information systems, information systems security has become a very critical issue in enterprise risk management. In information systems, security risks are caused by various interrelated internal and external factors. A security vulnerability could also propagate and escalate through the causal chains of risk factors via multiple paths, leading to different system security risks. In order to identify the causal relationships among risk factors and analyze the complexity and uncertainty of vulnerability propagation, a security risk analysis model (SRAM) is proposed in this paper. In SRAM, a Bayesian network (BN) is developed to simultaneously define the risk factors and their causal relationships based on the knowledge from observed cases and domain experts. Then, the security vulnerability propagation analysis is performed to determine the propagation paths with the highest probability and the largest estimated risk value. SRAM enables organizations to establish proactive security risk management plans for information systems, which is validated via a case study.

An information systems security risk assessment model under uncertain environment

Given there is a great deal of uncertainty in the process of information systems security (ISS) risk assessment, the handling of uncertainty is of great significance for the effectiveness of risk assessment. In this paper, we propose an ISS risk assessment model based on the improved evidence theory. Firstly, we establish the ISS index system and quantify index weights, based on which the evidential diagram is constructed. To deal with the uncertain evidence found in the ISS risk assessment, this model provides a new way to define the basic belief assignment in fuzzy measure. Moreover, the model also provides a method of testing the evidential consistency, which can reduce the uncertainty derived from the conflicts of evidence. Finally, the model is further demonstrated and validated via a case study, in which sensitivity analysis is employed to validate the reliability of the proposed model.

A hybrid coevolutionary algorithm for designing fuzzy classifiers

Rule learning is one of the most common tasks in knowledge discovery. In this paper, we investigate the induction of fuzzy classification rules for data mining purposes, and propose a hybrid genetic algorithm for learning approximate fuzzy rules. A novel niching method is employed to promote coevolution within the population, which enables the algorithm to discover multiple rules by means of a coevolutionary scheme in a single run. In order to improve the quality of the learned rules, a local search method was devised to perform fine-tuning on the offspring generated by genetic operators in each generation. After the GA terminates, a fuzzy classifier is built by extracting a rule set from the final population. The proposed algorithm was tested on datasets from the UCI repository, and the experimental results verify its validity in learning rule sets and comparative advantage over conventional methods.

Improving multiclass pattern recognition with a co-evolutionary RBFNN

A new hybrid scheme of the radial basis function neural network (RBFNN) model and the co-operative co-evolutionary algorithm (Co-CEA) is presented for multiclass classification tasks. This combination of the conventional RBFNN training algorithm and the proposed Co-CEA enforces the strength of both methods. First, the decaying radius selection clustering (DRSC) method is used to obtain the initial hidden nodes of the RBFNN model, which are further partitioned into modules of hidden nodes by the K-means method. Then, subpopulations are initialized on modules, and the Co-CEA evolves all subpopulations to find the optimal RBFNN structural parameters. Matrix-form mixed encoding and special crossover and mutation operators are designed. Finally, the proposed algorithm is tested on 14 real-world classification problems from the UCI machine learning repository, and experimental results illustrate that the algorithm is able to produce RBFNN models that have better prediction accuracies and simpler structures than conventional algorithms of classification.

Estimation of Node Localization with a Real-Coded Genetic Algorithm in WSNs

Location knowledge of sensor nodes in a network is essential for many tasks such as routing, cooperative sensing, or service delivery in ad hoc, mobile, or sensor networks, and it is hard to get the precision solution by traditional node localization algorithm, while genetic algorithm is an effective methodology for solving combinatorial optimization problems, so, in this paper, a real-coded version of the commonly used genetic algorithm is described in order to evaluate the precision of node localization problem in wireless sensor networks, meanwhile, the corresponding fitness function and genetic operators are designed. The algorithms presented in this paper are validated on a combined Windows XP and MATLAB simulation on a sensor network with fixed number of nodes whose distance measurements are corrupted by Gaussian noise. The results show that the proposed scheme gives accurate location of nodes.

CDSWS: coverage-guaranteed distributed sleep/wake scheduling for wireless sensor networks

Minimizing the energy consumption of battery-powered sensors is an essential consideration in sensor network applications, and sleep/wake scheduling mechanism has been proved to an efficient approach to handling this issue. In this article, a coverage-guaranteed distributed sleep/wake scheduling scheme is presented with the purpose of prolonging network lifetime while guaranteeing network coverage. Our scheme divides sensor nodes into clusters based on sensing coverage metrics and allows more than one node in each cluster to keep active simultaneously via a dynamic node selection mechanism. Further, a dynamic refusal scheme is presented to overcome the deadlock problem during cluster merging process, which has not been specially investigated before. The simulation results illustrate that CDSWS outperforms some other existed algorithms in terms of coverage guarantee, algorithm efficiency and energy conservation.

A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure

A new feature redundancy measurement based on mutual information was proposed.A multi-objective evolutionary algorithm for feature selection was presented.Pareto optimality was used to evaluate candidate feature subsets and find compact feature subsets.Experiments showed that our algorithm could select compact and discriminative feature subsets. Feature selection is an important task in data mining and pattern recognition, especially for high-dimensional data. It aims to select a compact feature subset with the maximal discriminative capability. The discriminability of a feature subset requires that selected features have a high relevance to class labels, whereas the compactness demands a low redundancy within the selected feature subset. This paper defines a new feature redundancy measurement capable of accurately estimating mutual information between features with respect to the target class (MIFS-CR). Based on a relevance measure and this new redundancy measure, a multi-objective evolutionary algorithm with class-dependent redundancy for feature selection (MECY-FS) is presented. The MECY-FS algorithm employs the Pareto optimality to evaluate candidate feature subsets and finds compact feature subsets with both the maximal relevance and the minimal redundancy. Experiments on benchmark datasets are conducted to validate the effectiveness of the new redundancy measure, and the MECY-FS algorithm is verified to be able to generate compact feature subsets with a high predictive capability.

Distributed Resource Allocation in Cloud-Based Wireless Multimedia Social Networks

With the rapid penetration of mobile devices, more users prefer to watch multimedia live-streaming via their mobile terminals. Quality of service provision is normally a critical challenge in such multimedia sharing environments. In this article, we propose a new cloud-based WMSN to efficiently deal with multimedia sharing and distribution. We first motivate the use of cloud computing and social contexts in sharing live streaming. Then our WMSN architecture is presented with the description of the different components of the network. After that, we focus on distributed resource management and formulate the bandwidth allocation problem in a gametheoretical framework that is further implemented in a distributed manner. In addition, we note the potential selfish behavior of mobile users for resource competition and propose a cheat-proof mechanism to motivate mobile users to share bandwidth. Illustrative results demonstrate the best responses of different users in the game equilibrium as well as the effectiveness of the proposed cheating avoidance scheme.

Coevolutionary learning of neural network ensemble for complex classification tasks

Ensemble approaches to classification have attracted a great deal of interest recently. This paper presents a novel method for designing the neural network ensemble using coevolutionary algorithm. The bootstrap resampling procedure is employed to obtain different training subsets that are used to estimate different component networks of the ensemble. Then the cooperative coevolutionary algorithm is developed to optimize the ensemble model via the divide-and-cooperative mechanism. All component networks are coevolved in parallel in the scheme of interacting co-adapted subpopulations. The fitness of an individual from a particular subpopulation is assessed by associating it with the representatives from other subpopulations. In order to promote the cooperation of all component networks, the proposed method considers both the accuracy and the diversity among the component networks that are evaluated using the multi-objective Pareto optimality measure. A hybrid output-combination method is designed to determine the final ensemble output. Experimental results illustrate that the proposed method is able to obtain neural network ensemble models with better classification accuracy in comparison with currently popular ensemble algorithms.

Learning Subspace-Based RBFNN Using Coevolutionary Algorithm for Complex Classification Tasks

Many real-world classification problems are characterized by samples of a complex distribution in the input space. The classification accuracy is determined by intrinsic properties of all samples in subspaces of features. This paper proposes a novel algorithm for the construction of radial basis function neural network (RBFNN) classifier based on subspace learning. In this paper, feature subspaces are obtained for every hidden node of the RBFNN during the learning process. The connection weights between the input layer and the hidden layer are adjusted to produce various subspaces with dominative features for different hidden nodes. The network structure and dominative features are encoded in two subpopulations that are cooperatively coevolved using the coevolutionary algorithm to achieve a better global optimality for the estimated RBFNN. Experimental results illustrate that the proposed algorithm is able to obtain RBFNN models with both better classification accuracy and simpler network structure when compared with other learning algorithms. Thus, the proposed model provides a more flexible and efficient approach to complex classification tasks by employing the local characteristics of samples in subspaces.