Network Security Data Research Articles

Research on University Network Data Anomaly Detection and Security Protection Algorithm Based on edge computing

Research on University Network Data Anomaly Detection and Security Protection Algorithm Based on edge computing

Analysis of Big Data Network Security Defense Mechanism Application of Artificial Intelligence

In the context of the close integration of computer network technology and daily life, big data network security faces many challenges. With its powerful information processing ability, this AI can effectively identify and handle potential threats in the network, thereby improving network security. This paper discusses the application of artificial intelligence (AI) in enhancing big data network security defense, especially in the threat detection link. This paper proposes a detection algorithm that combines support vector machine (SVM) and K nearest neighbor (KNN), namely SVM-KNN algorithm, for network attack detection and defense. When the hazard data is 60 under the test sample, the detection accuracy of KNN, SVM, and SVM-KNN is 86.88%, 89.51%, and 96.75% respectively; when the hazard data is 60 under the experimental sample, the detection accuracy of KNN, SVM, and SVM-KNN is 82.70%, 83.88%, and 89.26% respectively.response.

A Vehicle Abnormal Behavior Detection Model in Single Intelligent Vehicle Scenarios

Connected and automated vehicles (CAVs) play a vital role in transforming human mobility, tackling road congestion and road safety. However, CAVs rely heavily on the security, accuracy, and stability of sensor readings and network data. When there are anomalies in the data, it is necessary to detect them in a timely manner and handle them. However, under single intelligent vehicle scenarios, existing detection methods often struggle to identify unknown types of anomalies and are difficult to deploy on computationally limited vehicle terminals. To address the aforementioned issues, this paper proposes a vehicle anomaly data detection method based on deep learning. First, we modify the discriminator based on the GAN network, so that the network can assign different weights to different sensors, thus improving the generalization performance of the model. Afterwards, we assign weights to each parameter of the model during the training process, and then prune the model according to the weights to improve its computational speed. We verify the reliability of our method on the Safe Pilot Model Deployment (SPMD) data set. It is shown that the proposed model has good detection performance for various anomaly data, especially when facing data that were not encountered during the training process, and the proposed model effectively reduces the computational time of the detection process.

Toward next-generation networks: A blockchain-based approach for core network architecture and roaming identity verification

With the evolution of next-generation communication networks, ensuring robust Core Network (CN) architecture and data security has become paramount. This paper addresses critical vulnerabilities in the architecture of CN and data security by proposing a novel framework based on blockchain technology that is specifically designed for communication networks. Traditional centralized network architectures are vulnerable to Distributed Denial of Service (DDoS) attacks, particularly in roaming scenarios where there is also a risk of private data leakage, which imposes significant operational demands. To address these issues, we introduce the Blockchain-Enhanced Core Network Architecture (BECNA) and the Secure Decentralized Identity Authentication Scheme (SDIDAS). The BECNA utilizes blockchain technology to decentralize data storage, enhancing network security, stability, and reliability by mitigating Single Points of Failure (SPoF). The SDIDAS utilizes Decentralized Identity (DID) technology to secure user identity data and streamline authentication in roaming scenarios, significantly reducing the risk of data breaches during cross-network transmissions. Our framework employs Ethereum, free5GC, Wireshark, and UERANSIM tools to create a robust, tamper-evident system model. A comprehensive security analysis confirms substantial improvements in user privacy and network security. Simulation results indicate that our approach enhances communication CNs security and reliability, while also ensuring data security.

Systematic survey on data security in wireless body area networks in IoT healthcare system.

In the Internet of Things (IoT) healthcare sector, the wireless body area network (WBAN) is being used to optimize medical results by tracking and treating patients as they go about their daily lives. Health insurance has also been one of the cybercriminal's main goals. The Systematic Review of IoT Healthcare systems particularly wireless body area networks is significant, to reach the benefits and challenges faced by existing methods in the domain. This study provides a systematic survey of WBAN data protection. Various types of devices are used in medical science to detect and diagnose diseases. The network is an integral part of medical science in today's era. In medical sciences, sensors take data from a problematic place like cancerous cells. This research discussed a lot of techniques in the literature review. Most of them are not able to fulfill the requirements. If an unauthorized person reaches the data that can be a severe issue, like the diagnosed disease was blood cancer, and after unauthorized access manipulation can change even the diagnosed issue in the database. A doctor can prescribe the medication based on provided data that has been manipulated by unauthorized persons. Several existing schemes are explored in the literature to determine how the protection of sharing patients' healthcare data can be improved. The systematic literature review (SLR) of multiple security schemes for WBAN is presented in this survey paper.

Federated Bayesian optimization XGBoost model for cyberattack detection in internet of medical things

BackgroundHospitals and medical facilities are increasingly concerned about network security and patient data privacy as the Internet of Medical Things (IoMT) infrastructures continue to develop. Researchers have studied customized network security frameworks and cyberattack detection tools driven by Artificial Intelligence (AI) to counter different types of attacks, such as spoofing, data alteration, and botnet attacks. However, carrying out routine IoMT services and tasks during an under-attack scenario is challenging. Machine Learning has been extensively suggested for detecting cyberattacks in IoMT and IoT infrastructures. However, the conventional centralized approach in ML cannot effectively detect newly emerging attacks without compromising patient data privacy and network flow data confidentiality. AimThis study discusses a Federated Bayesian Optimization XGBoost framework that employs multimodal sensory signals from patient vital signs and network flow data to detect attack patterns and malicious network traffic in IoMT infrastructure while ensuring data privacy and detecting previously unknown attacks. MethodologyThe proposed model employs a Federated Bayesian Optimisation XGBoost approach, which allows us to search the parameter space quickly and find an optimal solution from each local server while aggregating the model parameters from each local server to the centralised server. The XGBoost algorithm generates a new tree by taking into account the previously estimated value for the tree's input data and then optimizing the prediction gain. This study used a dataset with 44 attributes and 16 318 instances. During the preprocessing phase, 10 features were dropped, and the remaining 34 features were used to evaluate the network flows and biometric data (patient vital signs). ResultsThe performance evaluation reveals that the proposed model predicts data alteration, malware, and spoofing attacks in patients' vital signs and network flow data with a prediction accuracy of 0.96. The results obtained from the experiment demonstrate that both the centralized and federated models are synchronized, with the latter occasionally being slightly reduced. ConclusionThe findings indicate that the suggested model can be incorporated into the IoMT domain to detect malicious patterns while maintaining data privacy and confidentiality efficiently.

Edge Computing and its Role in IoT: Analyze how Edge Computing is Transforming IoT by Processing Data at the Edge of the Network, Reducing Latency and Enhancing Data Security

An age of technological revolution has begun as a result of the Internet of Things (IoT) explosive growth, in which billions of gadgets gather and exchange data over several networks. The conventional cloud- centric approach of data processing faces substantial hurdles as Internet of Things (IoT) devices become more sophisticated and data-intensive. These challenges include high latency, bandwidth limits, and privacy concerns. By decentralizing data processing and moving it closer to the data source—the network's edge—edge computing emerges as a crucial solution. This study examines how edge computing is revolutionizing the Internet of Things by examining three of its main benefits: lower latency, better data security, and increased bandwidth economy. Edge computing expedites response times, strengthens data security, and maximizes network use by processing data locally. The fundamentals of edge computing, implementation difficulties, and prospective advancements that might further integrate edge computing with sophisticated IoT systems are all covered in the talk. In addition to changing IoT environments, this investigation seeks to demonstrate how edge computing is essential to real- time, safe, and effective data processing in an increasingly interconnected world.

Application of network data security based on wireless sensor technology in the development of digital sharing economy

Nowadays, when the existing algorithms are fused in the feature function network feature, they do not consider the different effects of different features on the saliency detection task. In order to solve this problem, this paper proposes a saliency feature fusion network based on multi-focus mechanism to complete the saliency detection task. While recognizing the important role of the network, it must also be realized that because most network users do not understand the details of network composition and network system analysis, the security of computer network data is relatively low. Unscrupulous hackers use their network technology and software to invade unauthorized networks and hosts without authorization. Therefore, network data security is very important. The competition and monopoly of the digital sharing economy platform has always been a hot topic of increasing attention from domestic and foreign economists. The platform economy is based on an efficient data collection and transmission system, a digital platform supported by continuously developing computing power and powerful data processing algorithms, which greatly promotes the development of social productivity. The core trend of the development of China's digital economy is the ecologicalization of the digital sharing economy. Small industrial platforms rely on massive user resources collected by a large platform to quickly connect the upper and lower channels of the industry to transform and form a new industry ecosystem.

A Knowledge Graph Completion Algorithm Based on the Fusion of Neighborhood Features and vBiLSTM Encoding for Network Security

Knowledge graphs in the field of network security can integrate diverse, heterogeneous, and fragmented network security data, further explore the relationships between data, and provide support for deep analysis. Currently, there is sparse security information in the field of network security knowledge graphs. The limited information provided by traditional text encoding models leads to insufficient reasoning ability, greatly restricting the development of this field. Starting from text encoding, this paper first addresses the issue of the inadequate capabilities of traditional models using a deep learning model for assistance. It designs a vBiLSTM model based on a word2vec and BiLSTM combination to process network security texts. By utilizing word vector models to retain semantic information in entities and extract key features to input processed data into BiLSTM networks for extracting higher-level features that better capture and express their deeper meanings, this design significantly enhances understanding and expression capabilities toward complex semantics in long sentences before inputting final feature vectors into the KGC-N model. The KGC-N model uses feature vectors combined with graph structure information to fuse forward and reverse domain features and then utilizes a Transformer decoder to decode predictions and complete missing information within the network security knowledge map. Compared with other models using evaluation metrics such as MR, MRR demonstrates that employing our proposed method effectively improves performance on completion tasks and increases comprehension abilities toward complex relations, thereby enhancing accuracy and efficiency when completing knowledge graphs.

IoT based weather monitoring system for temperature, humidity, rainfall and seismic detection in real time

This paper presents the design and implementation of an IoT-based weather monitoring system enhanced with seismic detection capabilities, employing Node MCU microcontroller and the Blynk mobile application platform. The system incorporates various sensors including a rain sensor, Light light-dependent resistor (LDR), a DHT11 sensor for temperature and humidity measurement, and an accelerometer (MPU6050) for earthquake detection. The hardware components are interconnected with the Node MCU microcontroller, facilitating real-time data acquisition from the sensors. Programming of the Node MCU is conducted using the Arduino IDE, enabling data transmission to the Blynk app via Wi-Fi connectivity. The Blynk app serves as the user interface, allowing seamless visualization and monitoring of weather parameters and seismic activity on smartphones. Key functionalities of the system include periodic data collection from sensors, transmission of sensor data to the Blynk server, and updating of graphical representations in the Blynk app interface. The accelerometer sensor is utilized to detect changes in acceleration, indicative of seismic disturbances. Threshold values are defined to trigger alerts within the Blynk app in the event of potential seismic activity. The implementation undergoes rigorous testing to ensure accuracy, reliability, and responsiveness under diverse environmental conditions. Considerations such as network stability, power efficiency, and data security are addressed to enhance system performance and longevity. The system demonstrates promising capabilities for real-time weather monitoring and seismic event detection, offering valuable insights for disaster preparedness and environmental analysis. IoT-based weather monitoring system integrated with seismic detection provides an effective solution for comprehensive environmental monitoring, leveraging the power of IoT technology and mobile applications for enhanced data accessibility and analysis. Further advancements and optimizations can be explored to extend the functionality and scalability of the system for broader applications in environmental science and disaster management.

A framework for automating environmental vulnerability analysis of network services

The primary objective of this paper is to introduce a comprehensive framework designed to automate the assessment of environmental vulnerability status of communication protocols and networked services, within operational contexts. The proposed algorithm leverages the Common Vulnerability Scoring System version 3 (CVSS 3) metrics in conjunction with network security data. The initial step involves the establishment of a network security ontology, which serves to model the environmental attributes associated with the current security posture of communication protocol channels available within an infrastructure. The process commences with the identification and enumeration of all active communication services through a combination of diverse information gathering tools. Subsequently, active network services undergo assessment using a blend of passive scanning and active security analysis tools, which produce the environmental security score. This score can be integrated into vulnerability scoring systems such as CVSS, facilitating the fine-tuning of base CVSS scores, as well as vulnerability mitigation in real-world environments. To validate the proposed framework, we conducted testing across various networks and communication protocols within a controlled environment, thereby offering tangible illustrations for widely-utilized communication protocols.

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

<div>Cloud computing (CC) is a rapidly developing IT approach with intrusion detection system being a crucial tool for safeguarding virtual networks and machines from potential threats, thereby mitigating security concerns in the cloud environment. The intrusion detection system (IDS) system demands significant improvements, primarily based on optimizing performance and bolstering security measures. This research aims to implement an IDS in cloud computing utilizing deep learning (DL) method. The DL model is a promising technique and is widely used to detect intrusions. The implemented hierarchical long short-term memory (HLSTM) method’s performance is evaluated for feature selection through variance threshold-based regression (VTR) on two IDS network datasets: Bot-IoT and network security lab-knowledge discovery and data mining (NSL-KDD). This paper concludes the use of an intrusion detection network resulting in high security and performance. Moreover, the implemented method on the NSL-KDD and Bot-IoT datasets obtains respective accuracies of 99.50% and 0.995. It is compared with the existing methods namely, ensemble ID model for CC utilizing DL, LeNet, fuzzy deep neural network with a Honey Bader algorithm for privacy-preserving ID, and improved metaheuristics with a fuzzy logic-based IDS for cloud security, and beluga whale-tasmanian devil optimization based on deep convolutional neural network (CNN) with TL, chronological slap swarm algorithm-based deep belief network (DBN), and dragonfly improved invasive weed optimization-based Shepard CNN.</div>

Analysis of multimedia network data security based on routing algorithm

In today’s society, the application of multimedia data is increasing, and the accompanying data security is also an important issue. This paper will analyze the data security of multimedia network based on routing algorithm. The advantages of wireless local area networks (WLAN) and ad hoc networks are combined in wireless mesh networks (WMNs), which have steadily grown to be a reliable method of wireless broadband access. The application of multi RF and multi-channel technology improves the performance of WMN. However, the limitation that includes the number of wireless network interface cards and channels in the network, results in the great challenges ofrouting and channel allocation in multi RF and multi-channel WMN. Based on AODV algorithm, this paper proposes a new cross layer routing algorithm which is JRCA-AODV algorithm. The simulation results demonstrate that the JRCA-AODValgorithm can successfully increase multiRF multi-channel WMN throughput while lowering packet loss rate and network delay. Take the packet rate of 8 Mbps as an example, the network throughput based on JRCA-AODV algorithm is 1.14 times that of MRMC-AODV algorithm, the packet loss rate based on JRCA-AODV algorithm is 14.06% lower than that of MRMC-AODV algorithm, and the network time delay based on JRCA-AODV algorithm is 14.27% lower than that of MRMC-AODV algorithm. At the same time, JRCA-AODV algorithm can effectively reduce the probability of data loss caused by the competitive and shared access mechanism of the channel, improve the success rate of data transmission, thus reduce the probability of data eavesdropping and tampering in the process of repeated transmission, and improve the security of multimedia network data.This will provide a new direction for promoting the idea of multimedia data security.

Privacy by design. User-held data model as a basis for Privacy and Personal Data protection in social networks

The article reveals the concept and features of privacy by design approach. An analysis of the legislation of the European Union, which regulates the obligation to comply with mentioned concept, namely the provisions of the General Data Protection Regulation, was carried out. Within the framework of the concept of privacy by design, the concepts and pecularities of using user-held data model are studied, which aims to change the legal regulation and technical support of information security as well as the security of personal data in social networks.
 In order to achieve the goal of the research, the need to introduce the specified model of data protection was analyzed – namely practical problems of personal data protection within the peculiarities of the market of social network service providers. The need of the interpretation of personal data as an object of property right was outlined, as well as the interpretation of the purpose of the meta data itself as personal information about a person that can be used to recognize or track a person separately or in combination with other information, which is directly stated in the OMB Act No. A-130, however, is neither interpreted similarly in the Data Protection Regulation nor in the legislation of Ukraine. The main advantages of implementing a user-held data model are presented, namely the advantages for data owners, businesses, the state, and cloud service providers for providing and maintaining the model.
 Legislation in the field of personal data protection in the EU, in particular in the field of social networks, is more complex at the supranational and national level. It is provided with a separate regulatory and organizational mechanisms, which are different from the one that exists today in Ukraine. The mechanism for the protection of personal data in the European Union provides the creation of independent public authority that is responsible for the implementation and compliance with the requirements of the General Data Protection Regulation. The article highlights the need to create a single separate body in the field of personal data protection in Ukraine, which will be able to perform the function of monitoring and ensuring compliance with the requirements of the relevant legislation, in particular, compliance with privacy by design.

Improving network data security interaction methods under wireless communication

AbstractWith the development of information technology, network data security issues have received widespread attention. Under traditional wired communication networks, it not only has high installation and maintenance costs, requires a lot of manpower and resources, but also has low security performance, which is not significant in improving network data security. How to improve network data security, prevent data from being maliciously leaked, stolen, and effectively transmitted in the network, is a major challenge facing today's society. Wireless communication technology has brought new ideas to improve network data security. This article analyzed the role of wireless communication technology in improving network data security and selected 30 enterprises as the research objects. This article studied the role of traditional wired communication technology and wireless communication technology in network data security protection and compared several indicators based on virus detection rate, defense rate, data transmission speed, and customer satisfaction. The experimental results showed that the average detection rate of Trojan viruses based on wireless communication technology was 80.8%, and the average defense rate against viruses using antivirus software was 58%. In the case of virus attacks, the average transmission speed was 2.4 s, and the average satisfaction of users with wireless communication technology in virus defense was 6.9. It indicates that wireless communication technology has significantly improved virus detection rate, defense rate, data transmission speed, and customer satisfaction in the mode of improving network data security. This technology has significant significance and value for users.

A smart decentralized identifiable distributed ledger technology‐based blockchain (DIDLT‐BC) model for cloud‐IoT security

AbstractThe most important and difficult challenge the digital society has recently faced is ensuring data privacy and security in cloud‐based Internet of Things (IoT) technologies. As a result, many researchers believe that the blockchain's Distributed Ledger Technology (DLT) is a good choice for various clever applications. Nevertheless, it encountered constraints and difficulties with elevated computing expenses, temporal demands, operational intricacy, and diminished security. Therefore, the proposed work aims to develop a Decentralized Identifiable Distributed Ledger Technology‐Blockchain (DIDLT‐BC) framework that is intelligent and effective, requiring the least amount of computing complexity to ensure cloud IoT system safety. In this case, the Rabin algorithm produces the digital signature needed to start the transaction. The public and private keys are then created to verify the transactions. The block is then built using the DIDLT model, which includes the block header information, hash code, timestamp, nonce message, and transaction list. The primary purpose of the Blockchain Consent Algorithm (BCA) is to find solutions for numerous unreliable nodes with varying hash values. The novel contribution of this work is to incorporate the operations of Rabin digital data signature generation, DIDLT‐based blockchain construction, and BCA algorithms for ensuring overall data security in IoT networks. With proper digital signature generation, key generation, blockchain construction and validation operations, secured data storage and retrieval are enabled in the cloud‐IoT systems. By using this integrated DIDLT‐BCA model, the security performance of the proposed system is greatly improved with 98% security, less execution time of up to 150 ms, and reduced mining time of up to 0.98 s.

The application of data encryption technology in computer network system

Network security as the focus of the application of computer technology, in the construction and development of modern society, in the face of the rapid development of Internet technology, worldwide network communication and network trade, how to effectively protect the security of network data and information, to solve the security problem of network system, is the main issue discussed by the academic circle. As an important basis for ensuring computer information security, data encryption technology plays an important role in effectively improving network security construction. Therefore, on the basis of understanding the concept of data encryption technology, according to the operation requirements of computer network system in the new era, this paper mainly explores the application of data encryption technology in computer network system to clarify the main direction of rapid development of network technology in the future.

Analysis of Competitive Differences in the Bilateral Platforms of the Digital Economy Using Artificial Intelligence and Network Data Security

Analysis of Competitive Differences in the Bilateral Platforms of the Digital Economy Using Artificial Intelligence and Network Data Security

Analysis of competitive differences in the bilateral platforms of the digital economy using artificial intelligence and network data security

Analysis of competitive differences in the bilateral platforms of the digital economy using artificial intelligence and network data security

Investigation on the Application of Computer System Reliability and Data Security in GPN Networks

Investigation on the Application of Computer System Reliability and Data Security in GPN Networks