Network Security Evaluation Research Articles

Q-Rung Orthopair Fuzzy Approach for Multiple-Attribute Group Decision-Making and Application to Commercial Computer Network Security Evaluation

Nowadays, with the increasing use of computer network technology in various fields, coupled with the openness of the Internet, computer networks are facing various information security issues, which will have a very important impact on their applications. Both enterprises and individuals may cause significant economic losses due to these problems, so certain measures need to be taken to protect the information security of computer networks. The commercial computer network security (CCNS) evaluation is MAGDM. In this research, based on projection measure (PROJM) and bidirectional projection measure (BPROJM), some forms of projection measure with q-rung orthopair fuzzy sets (q-ROFSs) are depicted. Then, two weighed projection approaches are depicted to put forward the MAGDM. Lastly, numerical examples for CCNS evaluation and different comparative analysis is employed to verify the developed approaches.

Synergistic Evaluation of Computer Network Security Using Attack Graphs and Security Event Processing

This study presents an intricate evaluation framework for enhancing computer network security through converging attack graph analysis and security event processing (SEP). The researchers construct a comprehensive methodology integrating these techniques to systematically identify vulnerabilities, visualize potential attack paths, and implement real-time threat monitoring. The findings demonstrate the efficacy of this integrated approach in fortifying network defenses and minimizing exposure to cyber threats.

PDHL-MARCOS method based on cumulative prospect theory for MAGDM and its application in network security evaluation

The increasingly large and complex network brings convenience to our life and production, but it also brings potential dangers. Any minor vulnerability may lead to failure of application software configuration, software maintenance, user management and system management. Eventually, our network is interrupted, and even life and production data are leaked. Therefore, establishing a scientific and effective network security evaluation model is an effective way to supervise, manage and improve network security; It is also an effective measure to reduce the loss of life and production. Therefore, we first selected six factors closely related to network security to form a complete network security evaluation index system. Then, the utility function in the traditional measurement of alternatives and ranking according to compromise solution (MARCOS) method is improved by using the value function in cumulative prospect theory (CPT), and a PDHL-MARCOS evaluation model that can reflect the decision-makers’ (DMs’) attitudes towards gains and losses is obtained in the probabilistic double hierarchy linguistic term set (PDHLTS). In addition, the relative entropy (RE) method is also used to obtain expert weights. The combination weights of attributes are obtained using the correlation coefficient and the standard deviation (CCSD) method and the weight function in CPT.

Projection measure-driven optimization of q-rung orthopair fuzzy MAGDM for computer network security evaluation

The computer network environment is very complex, and there are many factors that need to be considered in the process of network security evaluation. At the same time, various factors have complex nonlinear relationships. Neural networks are mathematical models that simulate the behavioral characteristics of animal neural networks. They process information by adjusting the connection relationships of internal nodes, and have a wide range of applications in solving complex nonlinear relationship problems. The computer network security evaluation is multiple attribute group decision making (MAGDM) problems. In this paper, based on projection measure and bidirectional projection measure, we shall introduce four forms projection models with q-rung orthopair fuzzy sets (q-ROFSs). Furthermore, combine projection measure and bidirectional projection measure with q-ROFSs, we develop four forms of projection models with q-ROFSs. Based on developed weighted projection measure models, the multiple attribute group decision making (MAGDM) model is established and all computing steps are simply depicted. Finally, a numerical example for computer network security evaluation is given to illustrate this new model and some comparisons are also conducted to verify advantages of the new built methods.

Enhanced computer network security assessment through employing an integrated LogTODIM-TOPSIS technique under interval neutrosophic sets

In the context of the development of the new era, computer network technology has become an indispensable and important technological means in people’s daily work and life. Through network technology, information resources can be collected, integrated, processed, and applied, thereby improving information analysis and mining capabilities, constructing big data environments for various industries, providing convenient and fast intelligent information services, and promoting social transformation and development. However, in practical development, network security issues seriously affect information security and social stability, and computer viruses and hackers pose a huge threat to computer systems. The computer network security evaluation is the MAGDM problems. Recently, the Logarithmic TODIM (LogTODIM) and TOPSIS technique has been utilized to cope with MAGDM issues. The interval neutrosophic sets (INSs) are utilized as a technique for characterizing uncertain information during the computer network security evaluation. In this paper, the interval neutrosophic number Logarithmic TODIM-TOPSIS (INN-LogTODIM-TOPSIS) technique is conducted to solve the MAGDM under INSs. Finally, a numerical case study for computer network security evaluation is utilized to validate the proposed technique. The prime contributions of this paper are put forward: (1) The entropy technique based on score values and accuracy value are conducted to obtain weight information under INSs; (2) an integrated INN-LogTODIM-TOPSIS technique is conducted to put forward the MAGDM issue; (3) An illustrative example for computer network security evaluation has been accomplished to put forward the INN-LogTODIM-TOPSIS technique.

Research on neural networks in computer network security evaluation and prediction methods

Anomaly detection in networks to identify intrusions is a common and successful security measure used in many different types of network infrastructure. Network data traffic has increased due to the proliferation of viruses and other forms of cyber-attacks as network technology and applications have developed quickly. The limitations of classical intrusion detection, such as poor detection accuracy, high false negatives, and dependence on dimensionality reduction methods, become more apparent in the face of massive traffic volumes and characteristic information. That’s why IoT infrastructures often use Software-Defined Networking (SDN), allowing for better network adaptability and control. Hence, this paper’s convolutional neural network-based Security Evaluation Model (CNN-SEM) is proposed to secure the source SDN controller from traffic degradation and protect the source network from DDoS assaults. The proposed CNN-SEM system might defend against DDoS assaults once discovered by applying and testing a Convolutional Neural Network (CNN). The model can automatically extract the useful aspects of incursion samples, allowing for precise classification of such data. The detection and mitigation modules evaluate the proposed SDN security system’s performance, and the findings showed promise against next-generation DDoS assaults. The experimental results show the CNN-SEM achieves a high accuracy ratio of 96.6%, a detection ratio of 97.1%, precision ratio of 97.2%, a performance ratio of 95.1% and an enhanced security rate of 98.1% compared to other methods.

An Evaluation of Wireless Network Security with Penetration Testing Method at PT PLN UP2D S2JB

Advances in information and communication technology continue to grow over time. This causes significant changes in social, economic, and political conditions. One company that requires strong network security is PT PLN (Perusahaan Listrik Negara) Persero, which is a leading energy company in Indonesia. In this case, the need to evaluate network security at PT PLN is very important. This evaluation will help identify vulnerabilities and security gaps that exist in PT PLN's network infrastructure. This network security evaluation using the Penetration Testing Execution Standards (PTES) method can provide an overview of the vulnerabilities or weaknesses of the network system at PT PLN UP2D S2JB which has quite a lot of gaps to exploit. This is evidenced by the results of fifteen tests conducted, only two of which failed, namely in the type of attack The Rogue Access Point. The results of Penetration Testing are very necessary and important as feedback for system managers in fixing existing vulnerability gaps.

A new method of dynamic network security analysis based on dynamic uncertain causality graph

In the context of cloud computing, network attackers usually exhibit complex, dynamic, and diverse behavior characteristics. Existing research methods, such as Bayesian attack graphs, lack evidence correlation and real-time reflection of the network attack events, and high computational complexity for attack analysis. To solve these problems, this study proposes a Dynamic Uncertain Causal Attack Graph (DUCAG) model and a Causal Chain-based Risk Probability Calculation (CCRP) algorithm. The DUCAG model is constructed to represent the uncertain underlying causalities among network attack events, and the CCRP algorithm aims at dynamically updating the causality weights among different network attack events and attacker hypotheses based on alarm information and causal chain reasoning process. By causality simplification and causality reasoning methods, the CCRP efficiently predicts the attacker behaviors and potential attack likelihood under uncertain time-varying attack situations, and is robust to the incompleteness and redundancy in alarm information. Four experiments under different attack scenarios demonstrate that, the DUCAG model can effectively characterize and predict the complex and uncertain attack causalities, in a manner of high time efficiency. The proposed method has application significance on cloud computing platforms by dynamically evaluating network security status, predicting the future behaviors of attackers, and assisting in adjusting network defense strategies.

Measuring network security in the cloud : A roadmap for proactive defense

An exhaustive examination and evaluation of hierarchy network security measuring and optimal active defense in the context of cloud computing is provided in this paper. After collecting all the cloud platform’s security-related data obtained via evaluation, the pertinent security data is summarized and analyzed to create the cloud platform’s safety record index. This index serves as a benchmark for the cloud platform administrators to evaluate the security vulnerabilities associated with their cloud platform. It offers cloud platform administrators a standard against which to assess the security hazards of cloud platforms. We primarily investigate the building of the cloud platform, the building process of the security situational awareness system, and the estimation of the safety circumstance value and use via the cloud platform’s security circumstance understanding system, thereby significantly enhancing the cloud platform’s stability, security, and dependability. By implementing the approach, the limitations of conventional network security management are circumvented, as the latter is predicated solely on historical data and is incapable of perceiving real-time alterations in the security condition of the system. Concurrently, the anticipated outcomes are incorporated into the imprecise decision-making system’s input, enhancing the evaluation’s precision. The approach improves the efficiency and efficacy of predicting the network safety situation in real time, augments the algorithm’s rate of convergence and prediction precision, and prevents overfitting from occurring. Simulation experiments conducted using the internet network safety posture dataset demonstrate that this research approach exhibits superior learning efficiency and lower prediction error compared to conventional machine learning and other deep learning techniques.

Network Security Evaluation Based on Improved Genetic Algorithm and Weighted Error Backpropagation Algorithm

Network Security Evaluation Based on Improved Genetic Algorithm and Weighted Error Backpropagation Algorithm

Improving computer network security evaluation with grey relational analysis and probabilistic simplified neutrosophic sets

The popularity of computer network has provided great convenience for people’s work and life, but it has also brought corresponding information security problems. It is very important to do a good job in computer network security evaluation. Conventional computer network security evaluation can be realized with the help of firewalls, antivirus software, etc., while in the face of complex computer network applications, it is necessary to adopt a security evaluation method with good operability and wider application range. The computer network security evaluation is viewed as multiple attribute decision-making (MADM) issue. In this paper, an extended probabilistic simplified neutrosophic number grey relational analysis (PSNN-GRA) method is established for computer network security evaluation. The PSNN-GRA method integrated with Criteria Importance Though Intercrieria Correlation (CRITIC) method in probabilistic simplified neutrosophic sets (PSNSs) circumstance is applied to rank the optional alternatives and a numerical example for computer network security evaluation is used to proof the newly proposed method’s practicability along with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute.

A generalized integrated group decision-making framework for computer network security evaluation with Interval Neutrosophic information

The rapid development of global information technology, especially the emergence and widespread application of the Internet, has enabled information technology to quickly penetrate into various fields of the economy and society. Informatization and networking have become important features of today’s era. However, while people enjoy the tremendous progress brought by information technology to humanity, the openness and security vulnerabilities of computer networks have also made network information security issues increasingly prominent. The invasion of hackers, the continuous generation and spread of computer virus, and the rampant use of rogue software have all caused great economic losses to individuals, enterprises, and countries. The computer network security evaluation is a multiple-attribute group decision making (MAGDM). Then, the TODIM and TOPSIS method has been established to deal with MAGDM issues. The interval neutrosophic sets (INSs) are established as an effective tool for representing uncertain information during the computer network security evaluation. In this manuscript, the interval neutrosophic number TODIM-TOPSIS (INN-TODIM-TOPSIS) method is established to solve the MAGDM under INSs. Finally, a numerical example study for computer network security evaluation is established to validate the INN-TODIM-TOPSIS method. The main research contribution of this paper is established: (1) the INN-TODIM-TOPSIS method is put up for MAGDM with INSs; (2) the INN-TODIM-TOPSIS method is put up for computer network security evaluation and were compared with existing methods; (3) Through the detailed comparison, it is evident that INN-TODIM-TOPSIS method for computer network security evaluation proposed in this paper are effective.

A fuzzy group decision-making framework for computer network security evaluation with probabilistic linguistic information

The computer network security evaluation is a classical multiple attribute group decision making (MAGDM) problems. Recently, the Exponential TODIM TODIM (ExpTODIM) method has been used to cope with MAGDM issues. The probabilistic linguistic term sets (PLTSs) are used as a tool for characterizing uncertain information during the computer network security evaluation. In this manuscript, the probabilistic linguistic ExpTODIM (PL-ExpTODIM) is built to solve the MAGDM under PLTSs. In the end, a numerical case study for computer network security evaluation is given to validate the proposed method. The main contribution of this paper is constructed: (1) the Exponential TODIM TODIM (ExpTODIM) method is extended to the PLTSs; (2) the probabilistic linguistic ExpTODIM (PL-ExpTODIM) method is defined to solve the MAGDM under PLTSs; (3) In the end, a numerical case study for computer network security evaluation is given to validate the proposed method.

An Evaluation Model for Network Security Based on an Optimized Circular Algorithm

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.

A novel MADM technique based on projection methods under q-rung orthopair fuzzy setting and its application to computer network security evaluation

Computer network security evaluation is a basic work to determine the security performance of the network system and implement the network security management. It involves organizational management, network technology, personnel psychology, social environment and other factors. In recent years, with the rapid development of information technology in China, the problem of computer network security has become increasingly prominent. Although domestic and foreign scholars have sought effective methods of network security evaluation from different aspects and using different methods, many factors involved in network security are difficult to quantify, so far, there is no relatively mature quantitative evaluation method of network security. The computer network security evaluation is classical multiple attribute decision making (MADM) problems. In this article, based on projection measure, we shall introduce the projection models with q-rung orthopair fuzzy information. First of all, the definition of q-rung orthopair fuzzy sets (q-ROFSs) is introduced. In addition, to fuse overall q-rung orthopair fuzzy evaluation information, two aggregation operators including q-ROFWA and q-ROFWG operators is introduced. Furthermore, combine projection with q-ROFSs, we develop the projection models with q-rung orthopair fuzzy information. Based on developed weighted projection models, the multiple attribute decision making model is established and all computing steps are simply depicted. Finally, a numerical example for computer network security evaluation is given to illustrate this new model and some comparisons between the new proposed models and q-ROFWA and q-ROFWG operators are also conducted to illustrate advantages of the new built method.

Neural network based single index evaluation for SQL injection attack detection in health care data

In recent years, there are a lot of security risks in the network, and there is the combination intersection between the computer network security problems and security evaluation. The scale of computer network is very large with vast information, so there are many loopholes in the system network. At present, many have established a computer network security evaluation system to monitor the network security vulnerabilities, viruses, and defects in healthcare. However, many places simply analyzed the risk assessment of network security, but there is no assessment of network security situation. For the network security evaluation system, there is no complete evaluation system of network information security. Therefore, a network security evaluation system must be constructed to develop an effective and practical simulation model of computer network security evaluation. Through the simulation model, the effect of network security can be improved. Since the reform and opening up, the simulation of computer network security evaluation in our country is a new subject. It can directly study the network security evaluation, build a network security evaluation model, and study the network security in detail. In the computer network simulation system, it can analyze, study, design, and plan various stages, so as to play an important role.

IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning

With the development of IoT, IoT devices have proliferated. With the increasing demands of network management and security evaluation, automatic identification of IoT devices becomes necessary. However, existing works require a lot of manual effort and face the challenge of catastrophic forgetting. In this paper, we propose IoT-Portrait, an automatic IoT device identification framework based on a transformer network. IoT-Portrait automatically acquires information about IoT devices as labels and learns the traffic behavior characteristics of devices through a transformer neural network. Furthermore, for privacy protection and overhead reasons, it is not easy to save all past samples to retrain the classification model when new devices join the network. Therefore, we use a class incremental learning method to train the new model to preserve old classes’ features while learning new devices’ features. We implement a prototype of IoT-Portrait based on our lab environment and open-source database. Experimental results show that IoT-Portrait achieves a high identification rate of up to 99% and is well resistant to catastrophic forgetting with a negligible added cost both in memory and time. It indicates that IoT-Portrait can classify IoT devices effectively and continuously.

Analysis and Evaluation of Network and Application Security Based on Next Generation Firewall

Analysis and Evaluation of Network and Application Security Based on Next Generation Firewall

A Distributed Traffic Replay Framework for Network Emulation

Traffic replay can effectively improve the fidelity of network emulation and can support emerging network verification and network security evaluation. A distributed traffic replay framework for network emulation, named DTRF, is designed for large-scale and high-fidelity network traffic emulation on the cloud platform. A fast traffic replay strategy bypassing the kernel is proposed to improve the traffic replay performance in the single node by alleviating the problem of high overhead from the kernel protocol stack. To conveniently expand the emulation scale, an extendable method of workflow orchestration that can realize the flexible control and configuration of the execution process of emulation tasks is proposed. The experimental results show that, compared with that of the traditional method, the throughput of traffic replay is increased by 3.71 times on average, the number of concurrent streams is increased by 4.18 times on average, and the timestamp error of the replayed packets is reduced by 97.8% on average. The DTRF can expand the emulation scenario to a massive heterogeneous network.

Design of Computer Network Security Intrusion Prevention Strategy and Evaluation Algorithm Analysis Technology

With the continuous improvement of global informatization, computer networks have basically reached complete popularization. In today's society, it has become a major application and provides corresponding services for various industries. Therefore, it is necessary to effectively protect various information in the network. Therefore, this article provided a detailed discussion on the problems existing in computer network information security. These issues included their own reasons, hacker intrusion, and spam. Based on this, this article explored computer network information security protection strategies such as deploying data encryption, timely patching system vulnerabilities, and installing firewalls and antivirus software. The experimental results showed that the network output values of the clustering algorithm were: 0.888 for level 1, 0.725 for level 2, 0.678 for level 3, 0.461 for level 4, and 0.211 for level 5. These were all within the scope.