Fuzzy Comprehensive Evaluation Model of Computer Network Security and its Application

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.

The attack and defense model of network security based on game theory has developed into an effective network security model. It is a hot topic in the field of network and information security to use the idea of game theory to attack and defend the network security. This paper analyzes the general model of game theory in the field of security. This paper also analyzes the general model of network security attack and defense game. This paper also analyzes the game theory model of cryptographic protocol. This paper studies the existing network security methods based on game theory, reviews the development and evolution process and technical core of network security based on game theory, and compares the specificity of network security models based on game theory. Finally, the paper discusses the problems in the field of network security based on game theory, and tries to forecast its future development trend.

It is our great pleasure to welcome you at the 3rd International Conference on Security of Information and Networks -- SIN 2010, organized in technical cooperation with the ACM Special Interest Group on Security, Audit and Control (SIGSAC). The conference, similar to SIN 2009 and SIN 2007, covers practically all aspects of security of information and networks. The SIN 2009 Conference had such sections as "Security Assessment, Policies, Educational Programs", "Security and Integrity Management and Models", "Cryptography, Security and Networks", "Access Control and Security Assurance", and "Attacks, Intrusion Detection". Based on accepted submissions, the following sections are presented at SIN 2010: Security Assessment, Policies, Privacy, and Educational Programs (12 papers);Management and Models of Security and Integrity (9 papers);Cryptography, Security, and Networks (10 papers);Attacks and Intrusion Detection (7 papers). We received 73 submissions of which only a small portion was accepted based on at least two independent reviews. At times three, even four, reviews were secured before a decision could be made. Among all the submissions, 31 were accepted as full papers (42%), 6 as short (8%), and 1 as a position statement (1%). The authors of the submitted papers hail from China, France, Germany, India, Iran, Italy, Japan, North Cyprus, Philippines, Portugal, Russia, Turkey, Ukraine, and USA. The papers of "Security Assessment, Policies, Privacy, and Educational Programs" section consider such topics as security standards and regulations, privacy, anonymity, educational issues, and digital forensics. "Management and Models of Security and Integrity" section papers consider various security management policies, models and architectures. "Cryptography, Security, and Networks" section features papers on cryptographic algorithms and protocols, as well as networks security. And finally, "Attacks and Intrusion Detection" section combines the papers discussing the detection and prevention of intrusions. SIN 2010 Conference gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of security of information and networks. We hope that these proceedings will serve as a valuable reference for security researchers and developers.

Denial of Service (DOS) and (DDOS) Distributed Denial of Service attacks have become a major security threat to university campus network security since most of the students and teachers prepare online services such as enrolment, grading system, library etc. Therefore, the issue of network security has become a priority to university campus network management. Using online services in university network can be easily compromised. However, traditional security mechanisms approach such as Defense-In-Depth (DID) Model is outdated in today’s complex network and DID Model has been used as a primary cybersecurity defense model in the university campus network today. However, university administration should realize that Defense-In-Depth (DID) are playing an increasingly limited role in DOS/DDoS protection and this paper brings this fact to light. This paper presents that the Defense-In-Depth (DID) is not capable of defending complex and volatile DOS/DDOS attacks effectively. The test results were presented in this study in order to support our claim. The researchers established a Defense-In-Depth (DID) Network model at the Central Luzon State University and penetrated the Network System using DOS/DDOS attack to simulate the real network scenario. This paper also presents the new approach Defense-through-Deception network security model that improves the traditional passive protection by applying deception techniques to them that give insights into the limitations posed by the Defense-In-Depth (DID) Model. Furthermore, this model is designed to prevent an attacker who has already entered the network from doing damage.

High-tech projects have high risks involving wide range of links, high expense of investment, long period of development and production cycle, complicated operation system, etc. When evaluating the project's risk, a separate gray method or fuzzy theory model is not comprehensive, it should combine the gray method with the fuzzy evaluation model. From the fuzzy evaluation and gray evaluation method the gray clustering statistic could be obtained by use of grey cluster theory. It is necessary to construct fuzzy membership matrix of risk and establish a fuzzy and gray comprehensive evaluation method, and adequately adopt expert's evaluation information of the fuzziness and grayness. Finally, using concrete empirical analysis in aviation armament project risk assessment, the feasibility and the correctness of the model are verified.

Regional innovation capability is an important indicator of both regional innovative and long‐term development. The purpose of this study is to build an evaluation index system for regional innovation capability in order to identify regional differences and support innovation more effectively. After establishing a reasonable evaluation value for regional innovation capability, a combination of simulated annealing optimized projection pursuit (SA–PP) and N‐layer nested fuzzy comprehensive evaluation models is used to assess China's regional innovation capabilities. The results show that the SA–PP model effectively mitigates the risk of erroneous evaluation results caused by index weight uncertainty, resulting in a more reasonable, robust, and intelligent assessment of regional innovation capability. Furthermore, the nested fuzzy comprehensive evaluation model is capable of easily resolving the evaluation factor set's heterogeneity and multilayer problems. The most significant influences on China's regional innovation capability are knowledge acquisition and enterprise innovation. The comprehensive score of the proposed combinational evaluation model manifests that provinces with strong regional innovation capabilities are mainly concentrated in the southeast coastal regions. The research results allow for precise weight determination and object ranking.

The ADDIE model is highly compatible with teaching in terms of educational purposes, concepts, and processes and has a positive contribution to teaching. In this study, the ADDIE model is used to teach a comprehensive practice course for automobile majors in order to promote the systematic design and implementation of teaching. In this paper, the main problems to be solved are firstly extracted through the elemental analysis of the comprehensive practice course system of automobile majors, and the expected level of learning objectives to be achieved is pre-set. Then, a complete integration of educational media resources is carried out to test the effectiveness of the developed course instructional design. The fuzzy evaluation model is utilized to assess the quality of the course constructed by the ADDIE model. Specifically, the hierarchical analysis method is applied to calculate the index weights, and then the entropy weight method is used to correct the obtained index weights to obtain the final weights, determine the fuzzy operator, and make a comprehensive assessment of the teaching quality. It can be seen through the analysis that the method proposed in this paper makes the average final grade of the experimental class higher than that of the control class by 3.4524 points, and the final rating of teaching quality obtained by the method of this paper using the fuzzy comprehensive evaluation model is 3.7968 points, which is a good level. This indicates that the upgraded method of teaching practical courses is more effective than the original method, enhances the overall performance of students, and fulfills the requirements for teaching practice in automotive specialties.

With more and more control systems accessing computer networks, the increase in their associated vulnerabilities has led to a decreasing security evaluation of the networks. It is essential to secure computer networks from attacks. To this end, the study constructs a network of computer network security evaluation model based on an optimized circular algorithm. To avoid detecting the model’s parameters falling into the local optimum, the model is first optimized based on the Corsi grey wolf optimization (CGWO) algorithm for the hyperparameters of the Gaussian process (GP). To solve the problem of unbalanced data and the GP not having memory capability, the study proposes an optimized Gaussian Mixture Model-Recurrent neural networks (GMM-RNN) algorithm. Experimental results of attack type recognition accuracy showed that the research CGWO-GP algorithm can jump out of the local optimum, and its average value of accuracy reached 98.99%. The average value of the leakage rate was 0.42%, and the average value of the false alarm rate was 0.11%. The average detection accuracy of the GMM-RNN model for eight attack types was 95.899%. The optimal detection accuracy of this model performance was 96.3948%. The training time of the GMM-RNN model was 67.96 s, and the detection time of the test set was 6.45 s, which greatly optimized the real-time performance. The GMM-RNN model was more effective in predicting the security posture of computer networks, and the prediction value can reach 97.65%. The research model was significantly better than other algorithmic models in the performance and evaluation of computer network security and had certain research values.

The network has become an indispensable element in daily life and makes the connection between things closer. This paper presents a network security simulation model and discusses the basic framework and application of the model. This paper is based on the random forest network security situation awareness model. The model performs dimensionality reduction on the data through data feature analysis to highlight the characteristic attributes of data records and reduce invalid data, thereby reducing the resource overhead of the model and its dependence on network hardware device configuration. As the core of the model, the random forest algorithm enables the model to distinguish various network behaviors. Results show that the average detection rate of its network security model is 95%. Further reflects the high performance of this model.

Security policies have different components; firewall, active directory, and IDS are some examples of these components. Enforcement of network security policies to low level security mechanisms faces some essential difficulties. Consistency, verification, and maintenance are the major ones of these difficulties. One approach to overcome these difficulties is to automate the process of translation of high level security policy into low level security mechanisms. This paper introduces a framework of an automation process that translates a high level security policy into low level security mechanisms. The framework is described in terms of three phases; in the first phase all network assets are categorized according to their roles in the network security and relations between them are identified to constitute the network security model. This proposed model extends the organization based access control (OrBAC) model to include not only access control policy but also some other administrative security policies like auditing policy. Besides, it enables matching of each rule of the high level security policy with the corresponding ones of the low level security policy. Through the second phase, the high level security policy is mapped into the network security model. The second phase could be considered as a translation of the high level security policy into an intermediate model level. Finally, the intermediate model level is translated automatically into low level security mechanism. The paper illustrates the applicability of proposed approach through an application example.

The increasing interconnectivity in our infrastructure poses a significant security challenge, with external threats having the potential to penetrate and propagate throughout the network. Bayesian attack graphs have proven to be effective in capturing the propagation of attacks in complex interconnected networks. However, most existing security approaches fail to systematically account for the limitation of resources and uncertainty arising from the complexity of attacks and possible undetected compromises. To address these challenges, this paper proposes a partially observable Markov decision process (POMDP) model for network security under uncertainty. The POMDP model accounts for uncertainty in monitoring and defense processes, as well as the probabilistic attack propagation. This paper develops two security policies based on the optimal stationary defense policy for the underlying POMDP state process (i.e., a network with known compromises): the estimation‐based policy that performs the defense actions corresponding to the optimal minimum mean square error state estimation and the distribution‐based policy that utilizes the posterior distribution of network compromises to make defense decisions. Optimal monitoring policies are designed to specifically support each of the defense policies, allowing dynamic allocation of monitoring resources to capture network vulnerabilities/compromises. The performance of the proposed policies is examined in terms of robustness, accuracy, and uncertainty using various numerical experiments.

With the rapid development of computer technology and big data technology, the security of computer network has become a key problem that restricts its development. The improved new technology also causes huge network security problems. The continuous development of big data will improve the design level of computer network security system. However, as a media, the computer's requirements for computer network security will also increase. Therefore, it is necessary to continuously strengthen the security of computer network system, improve users' usage and improve the network security status. It is a very effective means to improve the network security through legal restrictions, so it has become the first task. With the continuous improvement of information and data transmission speed, information and data will accumulate in the process of information technology utilization, thus causing security problems. The computer network security technology in the big data environment seriously affects the development of information technology and the development of the whole society.

Network virus propagation and security situation awareness based on Hidden Markov Model

The cockpit of an aircraft is the main place where the pilot controls the aircraft on a mission. An excellent cockpit environment not only ensures the pilot's basic survival needs but also improves the comfort level and alleviates fatigue when performing missions. On the basis of domestic and international airworthiness standards, a top-down refinement method is deployed to determine the initial goal, and the environmental criteria are fully discussed and balanced in a dynamic process to build a comprehensive evaluation system for environmental factors in the aircraft cockpit. Based on the fuzzy comprehensive evaluation theory, an evaluation model for environmental factors is constructed by combining analytic hierarchical analysis (AHP) and particle swarm optimization (PSO). Then the feasibility of the evaluation model is verified by an illustrative example. The results suggest that the light environment gains the highest score among the 4 environmental criteria followed by the thermal environment, while both sound environment and microenvironment have relatively low scores. As for the 27 environmental sub-criteria, temperature, illumination, lighting clarity, light-color coordination, noise duration and pressure score the highest. The evaluation findings can provide important environmental control criteria for the subsequent environmental control system in the cockpit of the aircraft.

With the growth and advancement of quantum computing, conventional cryptographic methods are becoming increasingly vulnerable, particularly in the core aspects of cybersecurity, including identity, asset, and network security. In detail, the following paper will provide a systematic analysis of post-quantum cryptography (PQC) in these areas. It discusses the theoretical foundations and operational potential of PQC algorithms, including those based on lattices, which aim to withstand quantum attacks, enhance authentication systems, protect digital assets, and maintain secure network communications. Considering the standards efforts, such as the work by NIST and real-life projects (current implementations of NewHope by Google and pilot projects by AWS), as well as the implementation difficulties that the domain faces, the article provides an overview of strategies related to migration. It explains both the potential and the challenges that face the introduction of PQC into current security infrastructures. It focuses on hybrid cryptography, side-channel robustness, performance trade-offs, and regulatory issues. Ultimately, the findings of this study suggest adopting PQC as a proactive rather than a defensive approach, providing an opportunity to redesign the structure of contemporary cybersecurity to achieve greater security in the face of emerging threats in the future.