Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

Fuzzy Risk Assessment Method for Airborne Network Security Based on AHP-TOPSIS

With the exponential increase in information security risks,ensuring the safety of aircraft heavily relies on the accurate performance of risk assessment.However,experts possess a limited understanding of fundamental security elements,such as assets,threats,and vulnerabilities,due to the confidentiality of airborne networks,resulting in cognitive uncertainty.Therefore,the Pythagorean fuzzy Analytic Hierarchy Process(AHP)Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)is proposed to address the expert cognitive uncertainty during information security risk assessment for airborne networks.First,Pythagorean fuzzy AHP is employed to construct an index system and quantify the pairwise comparison matrix for determining the index weights,which is used to solve the expert cognitive uncertainty in the process of evaluating the index system weight of airborne networks.Second,Pythagorean fuzzy the TOPSIS to an Ideal Solution is utilized to assess the risk prioritization of airborne networks using the Pythagorean fuzzy weighted distance measure,which is used to address the cognitive uncertainty in the evaluation process of various indicators in airborne network threat scenarios.Finally,a comparative analysis was conducted.The proposed method demonstrated the highest Kendall coordination coefficient of 0.952.This finding indicates superior consistency and confirms the efficacy of the method in addressing expert cognition during information security risk assessment for airborne networks.

Machine Learning Models for Heterogenous Network Security Anomaly Detection

The increasing amount and intricacy of network traffic in the modern digital era have worsened the difficulty of identifying abnormal behaviours that may indicate potential security breaches or operational interruptions. Conventional detection approaches face challenges in keeping up with the ever-changing strategies of cyber-attacks, resulting in heightened susceptibility and significant harm to network infrastructures. In order to tackle this urgent issue, this project focused on developing an effective anomaly detection system that utilizes Machine Learning technology. The suggested model utilizes contemporary machine learning algorithms and frameworks to autonomously detect deviations from typical network behaviour. It promptly identifies anomalous activities that may indicate security breaches or performance difficulties. The solution entails a multi-faceted approach encompassing data collection, preprocessing, feature engineering, model training, and evaluation. By utilizing machine learning methods, the model is trained on a wide range of datasets that include both regular and abnormal network traffic patterns. This training ensures that the model can adapt to numerous scenarios. The main priority is to ensure that the system is functional and efficient, with a particular emphasis on reducing false positives to avoid unwanted alerts. Additionally, efforts are directed on improving anomaly detection accuracy so that the model can consistently distinguish between potentially harmful and benign activity. This project aims to greatly strengthen network security by addressing emerging cyber threats and improving their resilience and reliability.

Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

The accuracy of historical situation values is required for traditional network security situation prediction(NSSP).There are discrepancies in the correlation and weighting of the various network security elements.To solve these problems,a combined prediction model based on the temporal convolution attention network(TCAN)and bi-directional gate recurrent unit(BiGRU)network is proposed,which is optimized by singular spectrum analysis(SSA)and improved quantum particle swarmoptimization algorithm(IQPSO).This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data.Furthermore,a prediction model of TCAN-BiGRU is established respectively for each subsequence.TCAN uses the TCN to extract features from the network security situation data and the improved channel attention mechanism(CAM)to extract important feature information from TCN.BiGRU learns the before-after status of situation data to extract more feature information from sequences for prediction.Besides,IQPSO is proposed to optimize the hyperparameters of BiGRU.Finally,the prediction results of the subsequence are superimposed to obtain the final predicted value.On the one hand,IQPSO compares with other optimization algorithms in the experiment,whose performance can find the optimum value of the benchmark function many times,showing that IQPSO performs better.On the other hand,the established prediction model compares with the traditional prediction methods through the simulation experiment,whose coefficient of determination is up to 0.999 on both sets,indicating that the combined prediction model established has higher prediction accuracy.

Research on the Construction of Computer Network Security System in Middle School Campus Network

In order to improve the security of high school campus networks,this paper introduces the goal,system composition,and function of the network security of high school campus networks,and puts forward a series of strategies,including the establishment of network security protection system,data backup and recovery mechanism,and strengthening network security management and training.Through these strategies,the safety and stable operation of the campus network can be ensured,the quality of education can be improved,and school’s development can be promoted.

A New Model for Network Security Situation Assessment of the Industrial Internet

To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.

System Architecture and Key Technologies of Network Security Situation Awareness System YHSAS

Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHSAS is developed for national backbone network,large network operators,large enterprises and other large-scale network.This paper describes its architecture and key technologies:Network Security Oriented Total Factor Information Collection and High-Dimensional Vector Space Analysis,Knowledge Representation and Management of Super Large-Scale Network Security,Multi-Level,Multi-Granularity and Multi-Dimensional Network Security Index Construction Method,Multi-Mode and Multi-Granularity Network Security Situation Prediction Technology,and so on.The performance tests show that YHSAS has high real-time performance and accuracy in security situation analysis and trend prediction.The system meets the demands of analysis and prediction for large-scale network security situation.

Reform of the Course System of Network Security Theory and Technology for Foreign Students in China

Theory and technology of network security is the core course of information security major,however,it still faces many challenges in the education of foreign graduate students studying in China.This paper analyzes the status quo and existing problems in the course of theory and technology of network security for foreign graduate students studying in China,the most fundamental of which is that the existing teaching materials are difficult to meet the needs of foreign graduate students.In view of the problem,this paper discusses how to improve the existing teaching materials to adapt to the teaching needs for foreign students and puts forward some new ideas and reform measures.

Research on computer network security measures

With the rapid development of the computer network technologies, in the application of the computer networks, the importance of the network security is becoming increasingly prominent, and computer network security issues have received more and more attention of the communities. In the face of the problems of the security hidden trouble in the computer network, it is essential to take the relevant measures to ensure the safety of the computer network. With the computer network security as the breakthrough point, this paper discusses the precautionary measures of the computer network security based on the analysis of the computer network security, in order to illustrate the importance of the computer network security, to provide references for ensuring the security of the computer network.

On an Interactive Network Security Measure

An interactive network security measure and a description of its function as well as its principle are presented. Based on the existing security loopholes and bugs in operating systems, this measure focuses on the restrictive condition of security and the establishment of configuration files. Under the control and administration of the secure management of configuration files, each system module brings much flexibility, adaptability and high-level security. The security detecting and managing software used in UNIX based on this measure has obtained good results, achieving the goal of automatically detecting and handling inner and outer system-violation and system abuse.

A Research on the Application of Virtual Network Technology in Computer Network Security

In the computer field,network security is a crucial integrant.It is necessary to pay attention on the application of virtual network technology,so as to raise the standard of computer network security to a new level[2].In view of this,this paper will analyze the application of virtual network technology in computer network security and propose some strategies for future reference.

Network security protection technology for a cloud energy storage network controller

As part of the ongoing information revolution,smart power grid technology has become a key focus area for research into power systems.Intelligent electrical appliances are now an important component of power systems,providing a smart power grid with increased control,stability,and safety.Based on the secure communication requirements of cloud energy storage systems,this paper presents the design and development of a node controller for a cloud energy storage network.The function division and system deployment processes were carried out to ensure the security of the communication network used for the cloud energy storage system.Safety protection measures were proposed according to the demands of the communication network,allowing the system to run safely and stably.Finally,the effectiveness of the system was verified through a client-side distributed energy storage demonstration project in Suzhou,China.The system was observed to operate safely and stably,demonstrating good peak-clipping and valley filling effects,and improving the system load characteristics.

Application Strategy of Data Encryption Technology in Computer Network Security

In the context of the information age,on the basis of the convenience of computer networks,security issues have gradually emerged.The data encryption technology is reasonably applied in the process of computer network security practice,which promotes the safe and reliable operation of the computer network to a certain extent.Based on this,our article regards data encryption technology as the main research object,focusing on its specific application in computer network security.

Application-Transparent Live Migration for Virtual Machine on Network Security Enhanced Hypervisor

As the number of Virtual Machines(VMs) consolidated on single physical server increases with the rapid advance of server hardware,virtual network turns complex and frangible.Modern Network Security Engines(NSE) are introduced to eradicate the intrusions occurring in the virtual network.In this paper,we point out the inadequacy of the present live migration implementation,which hinders itself from providing transparent VM relocation between hypervisors equipped with Network Security Engines(NSE-H).This occurs because the current implementation ignores VM-related Security Context(SC) required by NSEs embedded in NSE-H.We present the CoM,a comprehensive live migration framework,for NSE-H-based virtualization computing environment.We built a prototype system on Xen hypervisors to evaluate our framework,and conduct experiments under various realistic application environments.The results demonstrate that our solution successfully fixes the inadequacy of the present live migration implementation,and the performance overhead is negligible.

Assessing the Risk Situation of Network Security for Active Defense

The risk situation assessment and forecast technique of network security is a basic method of active defense techniques. In order to assess the risk of network security two methods were used to define the index of risk and forecast index in time series, they were analytical hierarchy process （AHP） and support vector regression （SVR）. The module framework applied the methods above was also discussed. Experiment results showed the forecast values were so close to actual values and so it proved the approach is correct.

Network Security Incidents Frequency Prediction Based on Improved Genetic Algorithm and LSSVM

Since the frequency of network security incidents is nonlinear,traditional prediction methods such as ARMA,Gray systems are difficult to deal with the problem.When the size of sample is small,methods based on artificial neural network may not reach a high degree of preciseness.Least Squares Support Vector Machines (LSSVM) is a kind of machine learning methods based on the statistics learning theory,it can be applied to solve small sample and non-linear problems very well.This paper applied LSSVM to predict the occur frequency of network security incidents.To improve the accuracy,it used an improved genetic algorithm to optimize the parameters of LSSVM.Verified by real data sets,the improved genetic algorithm (IGA) converges faster than the simple genetic algorithm (SGA),and has a higher efficiency in the optimization procedure.Specially,the optimized LSSVM model worked very well on the prediction of frequency of network security incidents.

A network security situation prediction model based on wavelet neural network with optimized parameters

The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network （WNN） with optimized parameters by the Improved Niche Genetic Algorithm （INGA）. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm （GA） so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network （GA-WNN）. Genetic Algorithm-Back Propagation Neural Network （GA-BPNN） and WNN.

Key Technologies of Wireless Heterogeneous Network Security

Convergence and collaboration of heterogeneous networks in the next generation public mobile networks will be a subject of universal significance. Convergence of heterogeneous networks, as an effective approach to improve the coverage and capacity of public mobile network, to enable communication services, to provide Internet access and to enable mobile computing from everywhere, has drawn widespread attention for its good prospects in application. Construction of security system for wireless heterogeneous networks and development of new security models, key security techniques and approaches are critical and mandatory in heterogeneous networks development. Key technology of wireless heterogeneous networks security covers security routing protocol, access authentication, intrusion detection system, cooperative communication between nodes, etc.

Study on Network Security Architecture for Power Systems

The wide application of network technology in power systems brings not only convenience and flexibility but also security threats. An architecture of network security for power system was proposed in this study,which protected data and facilities from being attacked by outside users by means of firewall, security monitor and control system. Firewall was basically the first line of defense for the intranet; the security monitoring system was a kind of IDS (Intrusion Detection System), while security control system provided authentication, authorization,data-encrypted transmission and security management. This architecture provides various security services, such as identification, authentication, authorization, data integrity and confidentiality.

Application of mobile scanning agent in the network security

To enhance the security of network systems, puts forward a kind of software agent is put forward, which has the induction ability of network frameworks and the ability of behavior independence. It is mobile scanning agent. More attentions is paid to expound how to design and realize mobile scanning agent. Besides, it is also explained the programs of mobile scanning agent system. In the end, it expects mobile scanning agent.