Earticle

The bat algorithm is a recently proposed meta-heuristic algorithm. Usually in solving the problem of optimization, the position of virtual bats is updated by flying speed, which decreases efficiency of the algorithm and accuracy of the solution. This paper has improved the location update strategy and individual selection strategy of bat algorithm, then puts forward an improved bat algorithm. The algorithm is used to solve three typical reliability-redundancy allocation problems, and the simulation experiment results show that the presented algorithm greatly enhances the computation efficiency, convergence speed and precision of the optimal solution in addressing the problem of reliability redundancy optimization. When compared with the best results reported in the previous literatures, the algorithm achieves a better or equally good optimal solution. It is demonstrated that the proposed algorithm is effective in solving reliability-redundancy allocation problems.

Portable devices are today used in all areas of life thanks to their ease of use as well as their applications with unique features. The increase in the number of users, however, also leads to an increase in security threats. This study examines the threats to mobile operating systems. Addressing the four mobile operating systems (Android, Apple OS (iOS), Symbian and Java ME) with the highest number of users, the study provides statistical information about the features of the corresponding operating systems and their areas of use. In the study, the most important threats faced by the mobile operating systems (Malware, Vulnerabilities, Attacks) and the risks posed by these threats were analyzed in chronological order and the future-oriented security perspective was suggested..

In order to solve such problems as poor robustness and low detection accuracy in the present copying & pasting forgery detection algorithm, an image copying & moving forgery detection algorithm based on non-sampling wavelet transform (undecimated wavelet transform, UWT) coupling with Zernike moments is proposed in this article. Firstly, the non-sampling wavelet transform is adopted to decompose the input image into approximation coefficient LL and detail coefficient HH to obtain the similarity and the diversity of the image blocks; secondly, the overlapped blocks are divided to calculate the sub-block interval of the image; thirdly, the sub-blocks are ordered and classified according to LL similarity and HH diversity; fourthly, Zernike moments are introduced to construct the distance matrix for image feature matching and image forgery detection. The experiment shows: the algorithm proposed thereby has good translation invariance and amplitude rotation invariance, good post-processing resistance performance, high detection accuracy, low false negative rate and low false positive rate.

When the weight of each attribute is determined in the multiple attribute decision making problems, calculated by the method of subjective values or objective values solely will cause the problem that weight coefficient is not reasonable. So the paper puts forward the weightingmethod which is based on maximizing deviations and normalized constraint condition. The method integrates the subjective and objective weighting information. On the one hand, the deviation between each weight vector which is determined by the various weighting method makes the maximum of its total deviation. On the other hand, the various evaluated object integrated value makes the maximum of its total evaluated value. Thus we establish a double objective optimization model. What’s more, we deduce the weight calculation formula by solving the model. Finally we have an experimental analysis. It proves that the combination weighting methodcan reflect the relative importance of each indicators and the information that index itself contains. In other words, it can reflect the subjective and objective decisions which make the weighting results more reasonable.

In this paper the concept of cryptography and steganography are combined to perform a powerful encryption. Here we propose a novel approach where a duel encryption methodology has been implemented. In the first level of encryption a pattern matching algorithm has been employed to encrypt the text message in terms of their positional value. In second level, the conventional LSB method has been used to embed the positional value in the cover file. Such a duel encryption method will ensure data security in an efficient manner. Finally the performance of the proposed method is evaluated in terms of means square error (MSE) and signal to noise ratio (SNR). A comparison has been carried out with conventional LSB method. The experimental results and the comparisons demonstrated that our algorithm is highly efficient in terms of encryption and the capacity size of the text.

Aiming to the demand and characteristics of vehicle scheduling optimization problem in emergency rescue, this paper establishes a multi-objective optimization model, which takes minimizing variable bidirectional distance, path risk and cost as the optimization target. To avoid the prematurely falling into local optimization of ant colony system(ACS)algorithm, and to improve the algorithm adaptability, computational efficiency and the quality of optimal solution, this paper proposed and realized a chaos-based improved ant colony system algorithm, which can overall updates the chaos disturbance to pheromone. Simulation results show that the algorithm is feasible, which can well meet the demand of vehicle scheduling optimization in emergency rescue.

For the purpose of effectively utilizing & managing ICT resources and reducing costs, various companies are using cloud service as their core technological strategy. In spite of such effort, cloud service security, information protection and performance are being considered as areas that need to be considered when implementing cloud service. In Korea, cloud service certification system is being used to ensure user reliability for cloud service and high-quality cloud services are being certified by considering their quality, security and ongoing service capability. Since current certification system is being utilized focusing on management system, however, it lacks the consideration on technical aspects. Accordingly, this study aims to examine information assurance systems such as ISO 20071 and ISMS to propose a framework for enhancing the cloud service certification system of Korea by focusing on security. The findings of this study are expected to help in increasing the use of cloud service by improving user reliability through the increased utilization of cloud service certification system of Korea.

The grid security affects directly the development of the grid and the practical application of grid system software. The access control is one of important contents of grid security research. The traditional access control models have ignored the subject security, and can not be solved with the dynamic grid, and the characteristics of the coexistence of multiple security strategy etc problems. Regarding expanded the concepts under the common network environment subject, the object, safe service, granularity control, the concept of subject/ object decomposition and the organization classifications are defined. Using the RBAC model and BLP the model basic principle, some of the new access control security strategy based on the grid environment has been defined in the paper. After discussion shows that the new rules emphasis on the subject security, and adapt to the dynamic nature of grid environment and characteristics of the coexistence of multiple security strategy. The new rules are compatibility with existing network access control model, they are secure and are also an expansion under the common network environment access control strategy, and this has certain positive significance to the grid security research.

The project of ant colony algorithm optimization neural network combining blind equalization algorithm is proposed. The better initial weights of neural networks are provided because of the randomness, ergodicity and positive feedback of the ant colony algorithm. And then, a combination of optimal weights are found through BP algorithm, which is fast local search speed. Thus blind equalization performance is improved. Computer simulation show that, the novel blind equalization algorithm speeds up the convergence rate, reduces the remaining steady-state error and bit error rate, which is compared with the Neural Network Blind Equalization Algorithm(NNBE) and Genetic Algorithm optimization Neural Network Blind Equalization Algorithm(GA-NNBE) .

In this paper we applies typical four audio watermarking technologies to protect ownership or copyright of mix music which is combined two or three pre-existing song. In experimental results, applied watermarking technologies cannot be applicable to mix music and three technologies fail to extract watermarks from mix music. This results is from mix music that does not contain full watermark information. Therefore applicable watermarking technology should development for mix music. The common problem of failed technologies cannot extract the second watermark from underlying mix music.

RFID technology has increasingly used in the various fields of our lives, and in the entire life cycle of the tag, there may be several ownership changes. Therefore, it is very important to protect the security and privacy of customer’s information during transferring. Combining with the physical characteristics of the anti-clone tag and the low-cost requirement, we proposed a lightweight ownership transfer protocol using Physically Unclonable Function (PUF) to achieve a safe ownership transfer. Through using formal analysis, it shows that this protocol can also resist reply attack, desynchronization attack, tampering attack, to protect the old and the new owner’s private information.

In this paper, a multi-sensor information fusion steady-state Kalman estimator for discrete time stochastic linear systems with system errors and sensor errors is presented. Gevers-Wouters(G-W) algorithm is used in this paper. Steady-state Kalman estimator is presented in this paper avoids the complex Diophantine equation, and it is based on the ARMA model to compute the steady-state Kalman estimators gain, further the Lyapunov equation is used to estimate the variance matrix and covariance matrix of estimation error. So this algorithm can obviously reduce the computational burden. In order to improve the estimation accuracy, the multi-sensor information fusion method is adopted. The fusion method includes weighted measurement fusion, weighted by scalars and the covariance intersection fusion. Under the linear minimum variance optimal information fusion criterion, the calculation formula of optimal weighting coefficients have be given in order to realize scalars weighted. To avoid the calculation of cross-covariance matrices, another distributed fusion filter is also presented by using the covariance intersection fusion algorithm, which can reduce the computational burden. And the relationship between the accuracy and the computation complexities among the three fusion algorithm are analyzed. A simulation example of the target tracking controllable system with two sensors shows its effectiveness and correctness.

The Maritime Cloud is the term used to describe the concept of an infrastructure that support authorized, seamless information transfer, as requested by the IMO e-navigation strategy, and as derived from testbeds focused on e-navigation. In this paper, we proposed Bucket ID Transformation that is a new encryption mechanism and the scheme can range search without order-preserving. Bucket ID Transformation is performed by recursive HMAC as many as a value of Bucket ID.

With the development of the transportation industry, the trend of overload is obviously, design load of highway bridges needs to be improved. Based on the field investigation and data analysis, this study modifies the design standers of highway. The regularities on characteristics of models, vehicle weight and the distribution of axle load and spacing were studied. Using the Matlab programming language and the Monte Carlo numerical simulation method to simulate a random traffic model, then loaded on simple bridges and continuous bridges with different span in order to calculate bending effect. The data were compared with the first level in highway load code and proposed the load increase parameters: when the span is less than 20 m, then use 1.2 times the standard specification in bridge design; in other situation, use 1.3 times the standard factor. Analyzing the characteristic of the vehicle load, can not only give technical parameters for the quantitative analysis of overloading traffic effects on the fatigue damage of bridge, but also provide a reference for bridge design units and the operation management department. So it has great realistic significance and research value.

Authentication and key agreement protocol becomes an important security issue for multi-server architecture. Combining biometrics with password enhances the level of security. Recently, Baruah et al. analyzed that Mishra et al.’s protocol has several drawbacks and proposed an improved biometric based multi-server authentication scheme. They claimed that their scheme satisfies all the required security attributes for a secure authentication. In this paper, we indicate that their scheme is not secure against key reveal attack, replay attack, and smart card forgery attack. Any registered user can retrieve the session key or launch the replay attack by eavesdropping on the communication channel. In addition, registered user can forge smart card when colluding with registered server.

During the last two decades the world is being transformed towards a virtual agglomerate of large interconnected cities. Moreover, an integration of digital infrastructures with the physical city infrastructure and facilities is taking place. This integration includes data generating sensors and various internet-connected devices which are dispersed in a city having as a target to offer a variety of services in order to ease citizens’ daily lives. In this, so called, smart city digital ecosystem, companies provide eBusiness models that are based on Collective Intelligence based decision making applications focusing on discovering citizens’ needs by collecting data related to their activities. However, the lack of privacy preserving mechanisms in such applications that eliminate the risk of correlation between citizens’ activities and their identities discourages citizens from providing their data to commerce systems. In this paper, we first discuss a new privacy preserving authentication technology, the Privacy-ABCs, and its successful deployment in a course evaluation pilot in higher education as well as a social networking application for pupils in a school environment. Based on the wide acceptance of this technology by the pilot users, we propose the deployment of a Privacy-ABCs based authentication system in a generic Collective Intelligent eBusiness model in a Smart City. The incorporation of this authentication technology in Collective Intelligence based eServices, has the potential of establishing new market opportunities since citizens’ anonymity will increase their incentives to provide valuable data related to their consuming activities and buying preferences as they move about in the city.

Traditional supply chain management is concerned about benefit maximization, although it can also involve raw materials recycling and energy conservation. According to the evaluation index system of green supply chain performance, the green degree will be composed in three layers; the first layer includes environmental impact degree, resource utilization rate, environmental benefit and reputation. In this study, we invited experts group to put forward the relative importance of the indicators, and then use the analytic hierarchy process to determine the weight of the indicators. Results show that the maximum weight is economic benefit, and the weight of the other four angles is more balanced and degree of attention was significantly lower than economic benefit. Green supply chain collaboration is the core competitiveness of enterprise development, green supply chain collaboration to better meet the user's personalized needs, but also to better enhances the competitiveness of the whole supply chain

Teaching quality evaluation usually involves multiple influence factors with grey and uncertain information. This brings about limitations to effective evaluation of teaching quality in colleges and universities. Thus, this paper draws merits from previous research and proposes a teaching quality evaluation system and model for colleges and universities. First, based on real situations, grey categories of evaluation state for teaching quality are established. The definite weighted functions of grey category of evaluation state are confirmed. Specific steps of the teaching quality evaluation model based on grey clustering analysis are accounted for. Finally, a case study is introduced to verify the model. This model enlightens a new way to evaluate teaching quality in colleges and universities.

Presently, there is a growth in the transmission of image and video data. Security becomes a main issue. Very strong image cryptographic techniques are a solution to this problem. There is a use of a randomly generated public key and based on that there is an application of DNA algorithm. In the proposed method DNA algorithm based substitution is used for spatial domain bit permutation. Here the chaotic logistic map is used for generating a pseudorandom bit sequence. We have generated 48bit length sequences for every pixel. After the substitution operation, a final layer of security is imposed to make this process more fault tolerant. The For checking the strength of the work a series of tests are performed and various parameters are checked like Correlation Coefficient Analysis, analysis of NPCR and UACI values etc.

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a promising cryptographic tool to implement access control for secure cloud storage systems. However, most existing access control schemes based on CP-ABE for the multi-authority cloud storage systems rely on a fully trusted global certificate authority. It is just an ideal assumption while there never exists a fully trusted global certificate authority in reality. In this paper, we construct a system with multiple certificate authorities (CA). The parameters of those CAs could be verified when an authority suspects the messages received from the correlative certificate authority. Besides, we construct a verifiable secret sharing (VSS) scheme to realize the decentralization of the certificate authority in our scheme. The scheme adopts the Pedersen commitment in combination with the properties of bilinear-pairs on elliptic curve and bilinear Diffie-Hellman problem. The analysis shows that our scheme is highly efficient, authentic and provably secure under the security model. Our scheme simultaneously supports efficient attribute revocation.

In recent years，machine learning method has been applied to the extensive research on traffic classification. In these methods, SVM (Support vector machine) is a supervised learning which can improve generalization ability of learning machine effectively. However, the penalty parameter C and kernel function parameter  are generally given by test experience during training of SVM. How to determine the optimal parameters of SVM is a problem to be solved. We proposed a method to deriving the optimal parameters of SVM based on GA (Genetic algorithm).This method does not need to traverse all the parameter points. The method extracts a certain number population from random solutions, and ultimately produces SVM optimal parameters according to the specific rules of operation. Through the method, we derived the optimal parameters combination C and  of SVM. The accuracy of network traffic classification is improved greatly.

This thesis proposes a method to detect sophisticated electronic financial frauds using SVDD. The financial industry detects electronic financial frauds using FDS, but its false positive rate is high enough to require additional authentications. It causes customers inconveniences and does not detect those sophisticated financial frauds. In order to resolve the aforementioned issues, this study proposes a method to detect such potential frauds by profiling and vectorizing user activities and device information by SVDD.

The development of information technology has changed the traditional teaching methods and means, especially in physical education. Twenty-first century will be the era of multimedia, multimedia technology will change the teaching mode, content and methods, ultimately lead to the fundamental reform of whole educational theory and education system. In this paper, author evaluate the effect of multimedia teaching by using questionnaire survey, result shows that more than 55% students in physical education were satisfied with the multimedia teaching, however, multimedia teaching has low performance in teaching method and interaction. Therefore, the university should strengthen the professional development of physical education teachers in information technology teaching, set up corresponding information teaching service center, providing digital resources, network curriculum development and instructional design, comprehensively improve the teaching ability of multimedia teaching.

Field experiment was used in this research to study influences brought by salt water with different degrees of mineralization to distribution characteristics of soil available nitrogen and nitrate nitrogen in red jujube root zones when the salt water drip irrigation was carried out to jujubes. It is shown in the results that the distribution content of soil available nitrogen in jujube tree root zones tended to gradually decrease from top to bottom in the soil profile at different growth stages. The soil available nitrogen contents at different growth stages exceeded initial values. The mineralization degrees of salt water which influenced the available nitrogen were successively 3 g/L＞4 g/L＞2 g/L＞CK, wherein the salt water with mineralization degree of 3g/L brought largest influences to transformation and utilization of available nitrogen in jujube tree root zones; with regard to different growth stages, the available nitrogen content was high in the soil layer with depth of 0-20 cm in the jujube root zone, and the content was small in the soil layer with depth o 30-50 cm; during drip irrigation with the mineralization degree of over 3g/L, the nitrate nitrogen content in soil layers of the jujube tree root zone exceeded the content under fresh water irrigation treatment as a whole.

Time variant key is one of the great research challenges in the field of cryptography for achieving the perfect security. Automatic variable Key (AVK) has been well researched and established as an important technique to realize time variant key in achieving towards perfect security. In AVK, proposed by Bhunia, the key is made to vary from data to data or session to session that is essentially required for achieving perfect secrecy. The variable key is generated in each time a data is sent and or a session is made. The time variant key (in AVK) is found to reduce the frequency attack as seen in earlier research, while such key was in applied in AES algorithm. In the present experimental research the authors study the application of AVK & Computing & Shifting AVK (CSAVK) in RSA to examine the reducing effect of frequency attack.

In this paper, we review and analyze some well-known cryptographic protocols for encryption of data that establish a secure communication for clouds. Many protocols are there, which provides various security goals for cloud computing. We have briefed on Secure Shell Protocol, Internet Protocol Security (IPSec), Kerberos, Wired Equivalent Privacy (WEP) and WiFi Protected Access(WPA) and discuss how these have been applied in cloud for secure communication. Further, we present the commands used by these protocols, the various methods used to encrypt data (tunnels and channels for instance) and their advantages for clouds.

With the rapid development of the Internet of things based smart hardware technology and computer engineering, combination of computer techniques with general expanded training exercise is the irreversible trend. In this paper, we propose the security enhanced smart hardware assisted regression analysis and the health monitoring technique based expanded training exercise effect evaluation model. Our research is composed of the following parts. (1) We review state-of-the-art smart hardware device organization and security enhancement countermeasures to serve as the foundation for the further modelling. (2) We propose novel mathematical regression analysis paradigm and optimized prediction model based on the neural network theory and convex optimization to assisting the later evaluation work. (3) We propose the new health monitoring device with the enhanced security to real-time monitor the movement and physical data. (4) We compare the proposed algorithm with the other popular adopted approaches and conduct comparison experiment. The experimental result proves that our model gets the higher prediction precision which enhance the traditional precision from 87% to 96.5% and the robustness is strengthened as well. In the future, we will combine more mathematical model to modify the current model to achieve the better performance.

Developing computer technologies and a network of persistently growing size put massive hosts and transmission devices in a vast network at increasingly higher risks. Log information of various devices can facilitate the detection of intrusion and attacks. Log information from a single data source is, however, with limitations. The analysis results cannot precisely reflect the current network situation if log information in a single data source is analyzed without correlation to analysis of log information from different data sources. To better demonstrate network situation, this paper proposes an improved event scenario correlation method for multi-source log analysis via researching on numerous existing data fusion methods and event correlation methods as well as integration of conventional event scenario correlation (ESC) method with fuzzy reasoning. Experimental results prove that the proposed method significantly reduces the False Positive rate (FP rate) and False Negative rate (FN rate) of security logs.