Security of electronic information networks: risks and solutions

+ Linear and dynamic models of the system of information security in social networks, taking into consideration the relationships between users, were studied and the resistance of the security system was analyzed. There is a practical interest in studying dependence of the behavior of the system of social network security on the parameters of users’ interaction. Dynamic systems of information security in social networks in the mathematical sense of this term were considered. A dynamic system refers to any object or process, for which the concept of state as a totality of certain magnitudes at a given time is unambiguously defined and the law that describes a change (evolution) of the initial state over time was assigned. The network of social interactions consists of a totality of social users and a totality of the relations between them. Individuals, social groups, organizations, cities, countries can act as users. Relations imply not only communication interactions between users but also relations of the exchange of various resources and activities, including conflict relations. As a result of research, it was found that the security systems of a social network are nonlinear. Theoretical study of the dynamic behavior of an actual object requires the creation of its mathematical model. The procedure for developing a model is to compile mathematical equations based on physical laws. These laws are stated in the language of differential equations. Phase portraits of the data security system in the MATLAB/Multisim program, which indicate the stability of a security system in the working range of parameters even at the maximum value of the impacts, were determined. Thus, the influence of users’ interaction parameters on the parameters of the system of social network security was explored. Such study is useful and important in terms of information security in the network, since the parameters of users’ interaction significantly affect, up to 100 %, the security indicator.

It is a multiple criteria problem to evaluate the security of information networks. However, one key issue which is not fully considered in extant studies is how to obtain the values of these multiple evaluation criteria, and in most evaluation models some redundant criteria values are calculated. Motivated by this, in the work we first use the pairwise comparison method to attain the values of each evaluation aspect of the security of information networks, and then develop a novel similarity-based M(1, 2, 3) approach to rate the security of alternative networks. Application study shows that the proposed approach could rate the overall security of alternative information networks and recognize the most vulnerable aspects of each information network, which provides good supports for making policies to enhance the security of alternative information networks.

Development and improvement has brought a very great impact to the network technology of the computer, besides the security of the network information has become a focus of the social safety questions.In this paper, through the analysis of security threats to computer network information and protective factors conducted, a strategy for a common computer network information security and protection strategy has been proposed, and useful lessons for our computer network and information security and protection reference have been provided.A series of questions such as security and dependability regarding the computer network system arise, which has been taken into consideration in proposing the strategy.This paper puts forward proposals with respects to the importance of the computer network system, existing problems of the online security of the computer, and precautionary measures.The detailed discussion will help the mass users to become conscious regarding network security and take precautionary measures while using the network.

Security related data in cyberspace is multi-source and heterogeneous, so it cannot be effectively used in network security analysis. To solve this problem, a network security ontology model for network node security analysis is proposed. The model reflects the security status of network nodes through three key sub domains: network assets, security information, defense strategy, and modeling concepts and relationships in a hierarchical and structured form. Ontology related language is used for query, and user-defined rules are used to enhance the reasoning ability of ontology, so as to assist network security analysis. Experiments and quality evaluation results show that the ontology model can integrate multi-source data, query and reason the network security status, and has higher quality evaluation than the same type of ontology.

This paper proposes a general network security framework based on artificial intelligence technology. Then the relationship between security objectives, security boundaries, security system elements, security services and security risk assessment are described. The modeling methods of security objectives, security boundaries and security system elements are given by using the power and universality of Unified Modeling language (UML). On the basis of building the network scenario, this paper sets up the association rules of network information security event detection and constructs the information security risk measurement model from the three perspectives of network database security vulnerability, data islanding risk and network security protection ability. Finally, a typical network example is given to analyze the application of the above model and algorithm in network security evaluation and optimal active defense. The analysis results show that the model and method presented in this paper are feasible and effective.

Now with the wide use of computer network and e-commerce, network security has become an important problem that it must be considered and resolved. More and more professions in the enterprises and individuals are subjected to the security problem in different degree. It is looked for the more reliable safety solutions. In the rapid development of the networks, emphasis on network information security is growing. Through analyzing the network information security factor, several common network information security policies are proposed, and the system of network information security is formed. By using virtual machine technology, network attacks and network protection subsystem are virtually out on a server, the configuration management of the vast majority of current network operations and network attack protection can be realized. The training environment of network attack and defense with function is more comprehensive, more superior performance and more realistic simulation experiments degree. Then, network security threats and defense strategies are summarized in this paper.

Nowadays, the application of the Internet is more and more extensive, but also exposed many problems. Network attacks are gradually penetrating into all kinds of network terminals, and the attack means are more and more sophisticated and covert. Network paralysis, data and user information leakage and other security problems are increasingly prominent. The increasingly dangerous network security situation brings new challenges to the traditional single point and single source security defense system. At the same time, the network crime has caused more and more losses to the personal and property safety of the state, enterprises and citizens. Network security has become the most concerned problem of citizens. In order to clarify the influence of different factors on computer network security, construct effective solutions, and create a more secure environment for the development and expansion of network information technology, this paper analyzes the factors affecting computer network information security based on genetic algorithm, and proposes a BP neural network algorithm based on genetic algorithm optimization, which is used in network security optimization. The experimental results show that the accuracy of BP neural network algorithm optimized by genetic algorithm is as high as 86.5%. The combination of genetic algorithm and neural network can effectively improve the reliability of the network.KeywordsGenetic algorithmComputerNetwork securityApplication research

With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion can integrate data from a macro perspective. In recent years, Machine Learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model.

Computer networks cover almost all areas of daily life. At the same time, computer network information security issues have become increasingly prominent. At present, China is paying more and more attention to and strengthening the management of computer network information security. In order to ensure the information security of computer networks, it is necessary to adopt effective protection strategies to reduce and eliminate various hidden dangers of network information security. The paper puts forward the application of grey theory to the evaluation of the effect of cyber-attacks, and studies the grey evaluation model and evaluation algorithm of the effect of cyber-attacks. The results show that this method can not only evaluate the attack effect of a single attack method, but also rank the effects of different attack methods.

Cloud computing not only provides convenience for users, but also brings certain information security threats to users. How to effectively protect the user’s information security has become one of the core issues of network security. This paper analyzes the problems and reasons of network security in cloud computing environment, and points out the main ways to improve network security. This paper analyzes the network security problems in the cloud computing environment, and discusses the information security technology in the network. This paper studies the definition, characteristics, classification and core technology of cloud computing security, analyzes the security risk of cloud computing network, and puts forward the cloud computing network security model and security level evaluation method from different angles. Level experiment results show that the network security model in cloud environment is feasible, computing can fully meet the development requirements of the current network information age, and effectively standardize the network security operation environment. The results show that: in 2016, the number of articles related to cloud computing is at least 2653, and in 2020, the number of articles related to cloud computing is at most 6438.

Contemporarily, two emerging techniques, blockchain, and machine learning are driving dramatic rapid growth in the field of network security. This paper describes a literature review of machine learning approaches for network security analytics and summarizes some applications of blockchain in the field of network security. We first illustrate three types of network security data, including network traffic, software binary, and security logs. Then we discuss the application of machine learning and deep learning approaches to analyze these data. We cover a broad array of attack types, including malware, spam, insider threats, network intrusions. We also summarize some applications and potential development direction of blockchain technology.

Modern computer networks have a complex infrastructure that requires constant monitoring to detect anomalous conditions that can cause malfunctions, which is unacceptable for large-scale distributed networks. An important problem in the intelligent processing of syslog data is the existence of multilabel datasets. Among the Russian language scientific publications, the problem under consideration in the context of information security of computer networks is not presented. The purpose of the research work is to increase the security of computer networks through the use of multi-label learning methods in solving the problem of classifying system log class labels. In this paper, a comparative analysis of single-label and multi-label classifiers in a computational experiment on the Mean accuracy metric was carried out. According to the results of the analysis, 80% of single-label classifiers were inferior in classification accuracy according to the Mean accuracy multi-label metric to their counterparts, which may indicate a strong influence of multi-label class labels on the models under consideration. The considered structure of experimental data in a tabular form is influenced by the multi-label problem much more strongly than it can be estimated by a standard frequency check, which actualizes further research in this direction.

PurposeThis paper aims to develop and test a theoretical framework that examines the capacity of electronic open networks and closed interpersonal networks in building social capital and creating new knowledge. Specifically, this article aims to extend understanding in the field of knowledge management by examining how social networks can accumulate social capital and build up potential and absorptive capacity for the creation of new knowledge.Design/methodology/approachThe authors reviewed the literature on open electronic social networks and closed interpersonal networks, social capital and absorptive capacity to examine how different types of networks accumulate different dimensions of social capital and develop different measures of absorptive capacity. A model was developed that hypothesizes that open networks can impact the structural and cognitive dimension of social capital but have less than a moderate effect on the relational dimension. The model is tested in the academic community using a sample of 22 research faculties from ten different research institutions within the MIS departments and five from Marketing.FindingsThe paper posits that electronic open networks have a significantly higher impact on the structural and cognitive dimension of social capital and a less than moderate impact on the relational dimension. Electronic open networks are, thus, best suited for acquiring and assimilating new knowledge, however the transformation and exploitation of knowledge require the cohesive ties of closed networks.Research limitations/implicationsThe combinative effect of electronic open networks and closed interpersonal networks is critical for the development of a potential and realized absorptive capacity and the creation of new knowledge. It is essential for researchers to examine the effect of different types of social networks on the process of knowledge creation and whether social capital accumulated in interpersonal networks can be leveraged in electronic open networks to enhance the process of knowledge creation.Practical implicationsBusinesses benefit from this line of research in knowing how well different types of social network are suited to the different phases of knowledge creation. Leveraging the capacity of open electronic networks and closed interpersonal networks can foster innovation.Originality/valueThere is no existing literature that has examined the relationship between different types of social networks, social capital, absorptive capacity, and knowledge creation. This paper provides a foundation for future studies that examine the combinative effect of closed interpersonal and open electronic networks.

Two Factor Authentication for Secured Login Using Array Password Engender by Petri net

The security of computer networks under the development of big data is facing serious challenges, and the real-time and effective protection requires the detection of network data to be effective. Therefore, the research starts from network anomaly detection, introduces Error Back Propagation (BP) and Tuna Swarm algorithm for performance improvement and dimensionality reduction of feature data from the target of anomaly detection accuracy and network traffic data processing, and optimizes BP network parameters with improved sparrow search algorithm to propose a detection and early warning system. The results show that the model has high convergence speed and the average recall and F-value in anomalous data detection reaches 29.568% and 61.742%, which is significantly better than other comparative algorithms, and the recognition accuracy of anomalous data types is much higher than 90%. The method can effectively provide reference value for network security protection management and information monitoring.