Network Security Issues Research Articles

Research on Automatic Identification of Power Big Data Anomalies Based on Improved Decision Tree

With the advancement of smart grid technology, the issue of power system network security has become increasingly critical. To fully utilize the power grid’s vast data resources and enhance the efficiency of anomaly detection, this paper proposes an improved decision tree (DT)-based automatic identification approach for anomalies in electric power big data. The method employs six-dimensional features extracted from the dimensions of volatility, trend, and variability to characterize the time series of power data. These features are integrated into a hybrid DT-SVM-LSTM framework, combining the strengths of DTs, support vector machines, and long short-term memory networks. Experimental results demonstrate that the proposed method achieves an accuracy of 96.8%, a precision of 95.3%, a recall of 94.8%, and an F1-score of 95.0%, outperforming several state-of-the-art methods cited in the literature. Moreover, the approach exhibits strong robustness to noise, maintaining high detection accuracy even under low signal-to-noise ratio conditions. These findings highlight the effectiveness of the method in efficiently detecting anomalies and addressing noise interference.

Application of Deep Learning in Public Network Security Management

With the rapid advancement of information technology, network security issues have become increasingly prominent, posing significant challenges in the field of public administration. Deep learning, as a cutting-edge technology in artificial intelligence, is emerging as a critical tool for enhancing network security management due to its exceptional performance in big data processing and pattern recognition. This paper reviews the basic concepts of deep learning and its current applications in network security management, discusses the design and implementation of deep learning-based network security management systems, and analyzes their effectiveness and evaluation through specific application cases. The results show that deep learning excels in areas such as intrusion detection, malware detection, network traffic analysis, and anomaly detection, significantly enhancing network security defenses. However, the application of deep learning in network security still faces challenges such as data privacy, model robustness, and computational resources. The paper concludes by proposing future development directions, providing theoretical support and practical references for further improving public network security management.

Adversarial Machine Learning Challenges in Modern Network Security Systems

Hostile machine learning has network security issues that reduce prediction model accuracy. A full defence against these assaults entails establishing hostile scenarios, strengthening models via strategy training, and applying powerful defences. Small adjustments introduce antagonistic inputs into the research. These teach the model to recognize and withstand deception attempts. The proposed solution competed with Trust Shield, Secure Guard, Defend, and Adversary Block in rigorous performance testing. The recommended strategy has a 95.0% success rate for discovering assaults and a much lower 5.0% false positive rate. This is much superior to conventional approaches. Due to its modest accuracy loss and rapid response, it's effective at fighting assaults. This comprehensive overview demonstrates the wide-scale application of the strategy with minimal resources. Finally, this research emphasizes the need for robust and adaptable AI security. This will assist in creating secure and trustworthy AI solutions to protect sensitive data and ensure prediction model accuracy in an increasingly hostile future.

Payload State Prediction Based on Real-Time IoT Network Traffic Using Hierarchical Clustering with Iterative Optimization.

IoT (Internet of Things) networks are vulnerable to network viruses and botnets, while facing serious network security issues. The prediction of payload states in IoT networks can detect network attacks and achieve early warning and rapid response to prevent potential threats. Due to the instability and packet loss of communications between victim network nodes, the constructed protocol state machines of existing state prediction schemes are inaccurate. In this paper, we propose a network payload predictor called IoTGuard, which can predict the payload states in IoT networks based on real-time IoT network traffic. The steps of IoTGuard are briefly as follows: Firstly, the application-layer payloads between different nodes are extracted through a module of network payload separation. Secondly, the classification of payload state within network flows is obtained via a payload extraction module. Finally, the predictor of payload state in a network is trained on a payload set, and these payloads have state labels. Experimental results on the Mozi botnet dataset show that IoTGuard can predict the state of payloads in IoT networks more accurately while ensuring execution efficiency. IoTGuard achieves an accuracy of 86% in network payload prediction, which is 8% higher than the state-of-the-art method NetZob, and the training time is reduced by 52.8%.

Renyi entropy-driven network traffic anomaly detection with dynamic threshold

Network traffic anomaly detection is a critical issue in network security. Existing Abnormal traffic detection methods rely on statistical-based or anomaly-based approaches, and these detection methods all require a full understanding of traffic characteristics and attack patterns. Information entropy has been widely studied in abnormal traffic detection because it can describe the distribution characteristics of network traffic. However, this method makes it difficult to cope with the timing and variability of network traffic. To address these challenges, this paper proposes a network traffic anomaly detection method based on Renyi entropy. Simultaneously, we introduce a fixed time window and utilize an improved EWMA model within this window to dynamically set thresholds for anomaly detection. Experimental results show that the method proposed in this paper is superior to popular abnormal traffic detection methods in terms of effectiveness and efficiency, it is better adapted to the dynamic changes of network traffic and provides a more reliable solution for anomaly detection.

Malicious DNS detection by combining improved transformer and CNN

With the widespread application of the Internet, network security issues have become increasingly prominent. As an important infrastructure of the Internet, the domain name server has been attacked in various forms. Traditional methods for detecting malicious domain servers are usually based on rules or feature engineering, requiring a large amount of manual participation and rule library updates. These methods cannot adapt to the constantly changing threat environment. In response to these issues, this study first improves the Transformer by adjusting its attention head and encoding method. Then, the model is combined with convolutional neural networks. Finally, a block-based ensemble classifier is used for classification detection. The relevant outcomes showed that the average accuracy score of the proposed method was as high as 95.8 points, the average detection time score was 96.8 points, the average feature extraction ability score of the model was 96.3 points, and the overall performance score was 97.6 points. This method has significant advantages over traditional methods in terms of accuracy and detection time, providing a new tool for detecting malicious domain servers.

Hybrid Mutual Authentication for Vehicle-to-Infrastructure Communication Without the Coverage of Roadside Units

The security issues in Vehicle Ad Hoc Networks (VANETs) are prevalent within Intelligent Transportation Systems (ITS). To ensure the security of vehicle-to-infrastructure (V2I) communication, extensive research on V2I authentication has been conducted in recent years. However, these protocols often overlook the limitations of communication range, leading to failures in V2I communication. Consequently, addressing the challenge of secure V2I communication in areas not covered by distributed roadside units (RSUs) remains a significant task. To address these issues, the current study proposes an Anonymous Certificate-less Hybrid Mutual Authentication Protocol (ACHMAP) based on Vehicle-to-Vehicle-to-Infrastructure (V2V2I) communication. In the proposed protocol, a secure multi-hop link is established through vehicle-to-vehicle (V2V) mutual one-time token authentication. Subsequently, the out-of-coverage vehicle and relevant RSUs complete V2I mutual authentication using signcryption messages transmitted by vehicle nodes. In the security analysis, it is demonstrated that the entire V2V2I stage can resist various security attacks, such as replay attacks, impersonation attacks, and threats to user anonymity, while preserving confidentiality and integrity. We simulated the proposed protocol using Network Simulator 3 (NS-3) to confirm that the authentication mechanism has lower overhead and minimal authentication delay in V2V2I communication.

The Integrated Development of Wireless Networks and AI

This paper combines the current research status of wireless networks and artificial intelligence, delving into the state of wireless network research and its impacts, and highlights the importance and necessity of the integrated development of wireless networks and AI. The aim is to provide readers with an understanding of the current situation and challenges of wireless networks. With the increasing number of mobile communication users, spectrum resources are becoming increasingly scarce, network security issues are more prominent, and the complexity of network architecture and business processes has significantly increased, making it difficult for traditional operations and maintenance methods to meet these demands. Therefore, leveraging AI to build a new mode of communication network operation has become an inevitable choice. The intelligent evolution of wireless networks is an unstoppable trend. In the future, full intelligence will be achieved through digital infrastructure, knowledge introduction, and digital twins, driving the efficient development of wireless networks.

The Application of National Security Algorithm in Wireless Networks for Industrial Automation Process Automation (WIA-PA) Network Data Security Transmission

Industrial wireless communication technology is the key to promoting the development of factory intelligence and automation. However, wireless network data security has hindered the development of industrial intelligence. Therefore, to solve the security issues of industrial wireless networks, a network data secure transmission technology based on the national security algorithm is proposed based on industrial wireless network standard protocols. Firstly, to address the vulnerability of network node identity authentication to attacks, the national security algorithm is used to construct a node authentication model. Secondly, a data security transmission model is constructed based on the improved national security algorithm, which can securely transmit industrial wireless networks by encrypting network application layer data. In network node identity authentication, when the step size was 50, the identity authentication average accuracy of the research mode reached over 98.95%, which was better than other models. When the step size was 50, the average accuracy of identity authentication in the research model was over 98.95%, showing the best performance among similar models. In the attack test of illegal client C, when the interaction history data was within 500, the node identity authentication was higher than 99.56%. However, when the interaction history data exceeded 500, the overall performance of the research model was still the best. In the comparison of node code storage and content usage overhead, when the code quantity was 15000, the storage overhead of the research model was 87356 bytes, with more node code storage and lower memory usage. The overall performance of the research model is better. From this, the research method performs the best in both identity recognition accuracy and security in network data security. The research content will provide technical support for the security management and data transmission of industrial wireless technology.

Intelligent Analysis and Prediction of Computer Network Security Logs Based on Deep Learning

Since the beginning of the 21st century, the development of computer networks has been advancing rapidly, and the world has gradually entered a new era of digital connectivity. While enjoying the convenience brought by digitization, people are also facing increasingly serious threats from network security (NS) issues. Due to the significant shortcomings in accuracy and efficiency of traditional Long Short-Term Memory (LSTM) neural networks (NN), different scholars have conducted research on computer NS situation prediction methods to address the aforementioned issues of traditional LSTM based NS situation prediction algorithms. Although these algorithms can improve the accuracy of NS situation prediction to a certain extent, there are still some limitations, such as low computational efficiency, low accuracy, and high model complexity. To address these issues, new methods and techniques have been proposed, such as using NN and machine learning techniques to improve the accuracy and efficiency of prediction models. This article referred to the Bidirectional Gated Recurrent Unit (BiGRU) improved by Gated Recurrent Unit (GRU), and introduced a multi model NS situation prediction algorithm with attention mechanism. In addition, the improved Particle Swarm Optimization (PSO) algorithm can be utilized to optimize hyperparameters and improve the training efficiency of the GRU NN. The experimental results on the UNSW-NB15 dataset show that the algorithm had an average absolute error of 0.0843 in terms of NS prediction accuracy. The RMSE was 0.0932, which was lower than traditional prediction algorithms LSTM and GRU, and significantly improved prediction accuracy.

Application of Deep Learning in Public Network Security Management

With the rapid advancement of information technology, network security issues have become increasingly prominent, posing significant challenges in the field of public administration. Deep learning, as a cutting-edge technology in artificial intelligence, is emerging as a critical tool for enhancing network security management due to its exceptional performance in big data processing and pattern recognition. This paper reviews the basic concepts of deep learning and its current applications in network security management, discusses the design and implementation of deep learning-based network security management systems, and analyzes their effectiveness and evaluation through specific application cases. The results show that deep learning excels in areas such as intrusion detection, malware detection, network traffic analysis, and anomaly detection, significantly enhancing network security defenses. However, the application of deep learning in network security still faces challenges such as data privacy, model robustness, and computational resources. The paper concludes by proposing future development directions, providing theoretical support and practical references for further improving public network security management.

Research on Network Security Issues and Protection Strategies in Smart Cities

With the rapid development of information technology and the advancement of smart city construction, urban management and services increasingly rely on networked intelligent systems. However, this highly interconnected network environment has also brought numerous network security challenges to smart cities. Smart cities involve a large number of Internet of Things (IoT) devices, big data platforms, cloud computing technologies, and smart infrastructure, making network security problems more complex and severe. This paper analyzes the composition and development trends of smart cities, explores the main security risks currently faced, and focuses on the security risks in key areas such as the Internet of Things, cloud computing, big data, and intelligent transportation systems. Furthermore, this paper proposes a multi-layered protection system and strategies for smart city network security, aiming to improve the network security assurance level of smart cities. The research seeks to provide practical protection measures to help smart cities cope with the growing security threats and ensure their sustainable development.

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.

Network Data Intrusion Detection and Data Feature Extraction of Electromechanical Facilities from Machine Learning

With the rapid development of Internet technology, network security issues have become more complex and changeable. Various intrusion methods threaten the information network environment in the Electromechanical Facility (EF) system. This paper focuses on EF to study the relevant detection methods and data feature extraction in complex network intrusions. Firstly, four common machine learning algorithms are used to calculate the data set. The advantages and disadvantages of each algorithm are analyzed after tuning and comparison. Secondly, a network intrusion detection algorithm is proposed based on Recursive Feature Elimination (RFE) principal component analysis. It uses RFE to reduce the number of features and improve the elimination judgment index to align with the detection requirements of information network datasets. Finally, a fault diagnosis method is proposed based on empirical pattern decomposition and support vector machine under Renyi entropy complexity measurement. This method trains and identifies the Renyi entropy of several basic pattern components obtained by decomposing empirical patterns as feature vectors. The results show that the RFE method judged by random forest removes irrelevant features, and the evaluation index is improved to align with the network dataset’s detection requirements. It reduces the data dimension, reduces the operation time, and improves the accuracy of a few attack types. The comprehensive final detection effect is better than other algorithms. Additionally, the embedded operating system construction method based on the protection mechanism realizes the separate storage of the operating system and key data. Also, it can prevent the network system from being maliciously invaded, ensuring the stability of the instrument operation under harsh working conditions.

Secrecy Performance Review for IoT-based Cognitive Farming System

Abstract: Smart farm management has complex challenges; therefore, there is a need to improve the overall adaptability, profitability, and environmental soundness of the farming systems. Agricultural production can be enhanced with the help of IoT-based cognitive tools. The rapid increase in IoT-based smart systems has raised critical concerns about efficiently utilizing the wireless spectrum. To drastically bring the improvement in the utilization of the wireless spectrum, cognitive radio (CR) has emerged as an effective solution. The inherent openness and broadcast nature of the wireless medium have given rise to many security issues in cooperative networks. Informationtheoretic security or physical layer security (PLS) is evolved as an efficacious wireless prototype for reliable transmissions to avert the eavesdropper from expropriating transmittal data on the wireless connections. Security is the key factor in these networks as they are exposed and active in behavior, thus, are susceptible to fraudulent activities. We investigated the performance of the CR system in terms of secrecy and also examined the system in terms of diversity receipt and selection of relay. The secrecy outcome of channel state information at the sender was also studied. The detrimental effects of fading in cooperative networks were also extensively discussed. Cooperative diversity was examined by deploying MRC and SC strategies at receptors. It has been deduced that CSI knowledge at the sender also has an eloquent concussion on the secrecy of the cognitive system.

Survey of federated learning in intrusion detection

Intrusion detection methods are crucial means to mitigate network security issues. However, the challenges posed by large-scale complex network environments include local information islands, regional privacy leaks, communication burdens, difficulties in handling heterogeneous data, and storage resource bottlenecks. Federated learning has the potential to address these challenges by leveraging widely distributed and heterogeneous data, achieving load balancing of storage and computing resources across multiple nodes, and reducing the risks of privacy leaks and bandwidth resource demands. This paper reviews the process of constructing federated learning based intrusion detection system from the perspective of intrusion detection. Specifically, it outlines six main aspects: application scenario analysis, federated learning methods, privacy and security protection, selection of classification models, data sources and client data distribution, and evaluation metrics, establishing them as key research content. Subsequently, six research topics are extracted based on these aspects. These topics include expanding application scenarios, enhancing aggregation algorithm, enhancing security, enhancing classification models, personalizing model and utilizing unlabeled data. Furthermore, the paper delves into research content related to each of these topics through in-depth investigation and analysis. Finally, the paper discusses the current challenges faced by research, and suggests promising directions for future exploration.

A Multi-Scale Temporal Feature Extraction Approach for Network Traffic Anomaly Detection

With the rapid advancement of network technology and the expanding scale of the Internet, network traffic data has grown exponentially, leading to increasingly prominent issues in network security. Many researchers are developing new network traffic anomaly detection models, but these models are difficult to effectively capture the multi-scale temporal characteristics of network traffic data and learn the correlation and importance among various feature dimensions. To this end, we propose a multi-scale temporal feature network (MSTFN-AM) based on an attention mechanism. MSTFN-AM integrates original data with temporal information through the temporal position encoding module and extracts multi-scale temporal features through the temporal feature extraction module. The temporal self-attention module can effectively identify dependencies and correlations between different time points. Ultimately, experiments conducted on two public datasets demonstrate that the MSTFN-AM model outperforms all baseline models in prediction performance.

Data Security Issues in Intelligent Cloud Computing Systems

This project takes the intelligent cloud computing system as the main research object. It mainly uses the literature analysis method to explore the data security problems in the intelligent cloud computing system. By investigating the development background of intelligent cloud computing, the author summarizes the definition of cloud computing and the characteristics of cloud computing. The author also makes a specific analysis of the advantages of cloud computing, and concludes that cloud computing is possible through safe and reliable data, low demand for clients, shared cloud data and unlimited possibilities. These four points are the main reasons major enterprises choose cloud computing to replace traditional computing resources. In the topic, the author studied the data security problems in cloud computing from three aspects: the problems exposed in the development process of cloud computing, the effective measures to protect data security in cloud computing and predicting the future development trend of improving data security. The authors believe that future research should focus on a balance between network security issues in cloud computing and building a secure and reliable data system.

Research on Multi-Layer Defense against DDoS Attacks in Intelligent Distribution Networks

With the continuous development of new power systems, the intelligence of distribution networks has been increasingly enhanced. However, network security issues, especially distributed denial-of-service (DDoS) attacks, pose a significant threat to the safe operation of distribution networks. This paper proposes a novel DDoS attack defense mechanism based on software-defined network (SDN) architecture, combining Rényi entropy and multi-level convolutional neural networks, and performs fine-grained analysis and screening of traffic data according to the amount of calculation to improve the accuracy of attack detection and response speed. Experimental verification shows that the proposed method excels in various metrics such as accuracy, precision, recall, and F1-score. It demonstrates significant advantages in dealing with different intensities of DDoS attacks, effectively enhancing the network security of user-side devices in power distribution networks.

End-to-End Learning-Based Study on the Mamba-ECANet Model for Data Security Intrusion Detection

With the rapid development of information technology, network security issues have become increasingly prominent. In particular, data security intrusions pose serious threats to the data privacy and system security of enterprises and individuals. Traditional intrusion detection systems often exhibit low detection accuracy and high false alarm rates when faced with complex and dynamic network environments and diverse attack methods. Therefore, this paper proposes a data security intrusion detection system based on deep learning, which integrates the Mamba model and ECANet model and employs an end-to-end learning approach for training and optimization. First, the Mamba model is introduced for preliminary data feature extraction, whose efficient feature representation capabilities provide a solid foundation for the subsequent detection process. Then, by integrating the ECANet model, feature selection is further optimized through the attention mechanism, enhancing the model’s focus on important features. Finally, an end-to-end learning approach is adopted to train and optimize the entire system, ensuring excellent performance and robustness in practical applications. Experimental results show that the proposed intrusion detection system demonstrates higher detection accuracy on multiple test datasets, improving by approximately 5% compared to traditional methods, providing a new and effective solution for data security.