Network Security Data Research Articles

TBRm: A Time Representation Method for Industrial Knowledge Graph

With the development of the artificial intelligence industry, Knowledge Graph (KG), as a concise and intuitive data presentation form, has received extensive attention and research from both academia and industry in recent years. At the same time, developments in the Internet of Things (IoT) have empowered modern industries to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). Using knowledge graphs (KG) to process data from the Industrial Internet of Things (IIoT) is a research field worthy of attention, but most of the researched knowledge graph technologies are mainly concentrated in the field of static knowledge graphs, which are composed of triples. In fact, many graphs also contain some dynamic information, such as time changes at points and time changes at edges; such knowledge graphs are called Temporal Knowledge Graphs (TKGs). We consider the temporal knowledge graph based on the projection and change of space. In order to combine the temporal information, we propose a new representation of the temporal knowledge graph, namely TBRm, which increases the temporal dimension of the translational distance model and utilizes relational predicates in time add representation in time dimension. We evaluate the proposed method on knowledge graph completion tasks using four benchmark datasets. Experiments demonstrate the effectiveness of TBRm representation in the temporal dimension. At the same time, it is also practiced on a network security data set of the Industrial Internet of Things. The practical results prove that the TBRm method can achieve good performance in terms of the degree of harm to IIoT network security.

Evolution of Email Security Standards

The first work on email security began in 1984, which resulted in the Privacy Enhanced Mail (PEM) specifications. Two years later, work was done as part of the Secure Data Network System (SDNS) sponsored by the U.S. Government, resulting in the Message Security Protocol (MSP). PEM and MSP both influenced the development of the most popular Internet email security protocol, which is Secure MIME (S/MIME). This article provides insight into the early history of email security, describes ongoing work to improve email security, and then makes some predictions about the future.

Big data network security defense mode of deep learning algorithm

Abstract With the rapid development and progress of big data technology, people can already use big data to judge the transmission and distribution of network information and make better decisions in time, but it also faces major network threats such as Trojan horses and viruses. Traditional network security functions generally wait until the network power is turned on to a certain extent before starting, and it is difficult to ensure the security of big data networks. To protect the network security of big data and improve its ability to defend against attacks, this article introduces the deep learning algorithm into the research of big data network security defense mode. The test results show that the introduction of deep learning algorithms into the research of network security model can enhance the security defense capability of the network by 5.12%, proactively detect, and kill cyber attacks that can pose threats. At the same time, the security defense mode will evaluate the network security of big data and analyze potential network security risks in detail, which will prevent risks before they occur and effectively protect the network security in the context of big data.

SKDStream: a dynamic clustering algorithm on time-decaying data stream

Data stream is a type of data that continue to grow over time. For example, network security data stream will constantly be generated in the field of data security, and encrypted data stream will be generated in the privacy protection scenario. Clustering is a basic task in the analysis of data stream. In addition to the large amount of data and limited computer memory, there are the following challenges in time-decaying data stream clustering: (1) How to quickly process time-varying data stream and how to quickly save vaild data. (2) How to maintain and update clusters and track their evolution in real time. Based on the fact that the existing data stream algorithms do not provide a good strategy to the above problems, this paper proposes a dynamic clustering algorithm named SKDStream. The algorithm divides the entire data space into distinct minimal bound hypercubes, which are maintained and indexed by a newly defined structure, SKDTree, that aggregates and updates clusters in real time without requiring large primary storage. Clusters are composed of dense hypercubes. Experiments on synthetic datasets and real datasets show that the response time of the algorithm is similar to that of existing dataflow algorithms, but the quality of the generated clusters is relatively stable over time. Furthermore, the SKDStream algorithm is able to track the evolution of the number of clusters, centers, and density in real time, and compared to D-stream, SKDStream is efficient and effective in clustering.

Analysis of Safe Storage of Network Information Data and Financial Risks Under Blockchain Combined With Edge Computing

To discuss the control of financial risks (FRs) under blockchain (BC) and improve network information security (NIS) and data security, edge computing (EC) combined with BC is proposed to control the risks of the big data (BD) financial system. Firstly, the BC-based financial system is introduced, and the characteristics of BC such as decentralization, tamper-resistant, and smart contract are analyzed. Secondly, the development status of NIS and the characteristics of marginal computing are explained, and the control model of NIS is established. Then, EC is used to encrypt the identity authentication system to ensure data security, and the BC-based FR evaluation model is established. Finally, a questionnaire is designed regarding the NIS model, and the results are analyzed. A simulation experiment is conducted regarding the index evaluation of the BC-based FR evaluation model. The experimental results indicate that network personnel control, environment, and technology have positive effects on NIS, and the impact factors are 0.26, 0.24, and 0.33, respectively.

Artificial intelligence based optimization for mapping IP addresses to prevent cyber-based attacks

Dynamic capacity decides the bearing of mechanical turn of events, logical and sane dynamic capacity is a vital factor in the turn of events and development of an industry. With the quick advancement of man-made reasoning and PC, man-made consciousness has been generally utilized in game, the computer generated simulation and bionic robot research has become a significant piece of bionics research and has perceived a lot of consideration. As of now, the savvy job of man-made consciousness has not been concentrated inside and out. A large number of the current virtual characters have issues of moderate reaction and helpless dynamic. Hence, in this paper, the virtual soccer match has been taken for instance, and the dynamic capacity of the virtual person of the game was investigated, to settle on a superior discretion and dynamic capacity, with the goal that most of clients can truly feel the knowledge of virtual job. With the advancement of current science and innovation, PC network innovation has been applied to different fields of creation and life. Notwithstanding, a wide range of organization assaults arise in unendingly, and the security and insurance of PC network is vital. On this premise, this paper took wellbeing assurance from the PC on the organization data as the beginning from the sensible and specialized angles to frame a blend of various data security innovation of PC network safeguard inside and out model and presented and investigated for wellbeing innovation by utilizing the model. The paper discussed about the PC network data security innovation of various play in the data security assurance work. The outcomes show that the profundity assurance model can viably forestall network assaults.

Security Analysis of Social Network Topic Mining Using Big Data and Optimized Deep Convolutional Neural Network.

This research aims to conduct topic mining and data analysis of social network security using social network big data. At present, the main problem is that users' behavior on social networks may reveal their private data. The main contribution lies in the establishment of a network security topic detection model combining Convolutional Neural Network (CNN) and social network big data technology. Deep Convolution Neural Network (DCNN) is utilized to complete the analysis and search of social network security issues. The Long Short-Term Memory (LSTM) algorithm is used for the extraction of Weibo topic information in the memory wisdom. Experimental results show that the recognition accuracy of the constructed model can reach 96.17% after 120 iterations, which is at least 5.4% higher than other models. Additionally, the accuracy, recall, and F1 value of the intrusion detection model are 88.57%, 75.22%, and 72.05%, respectively. Compared with other algorithms, the model's accuracy, recall, and F1 value are at least 3.1% higher than other models. In addition, the training time and testing time of the improved DCNN network security detection model are stabilized at 65.86 s and 27.90 s, respectively. The prediction time of the improved DCNN network security detection model is significantly shortened compared with that of the models proposed by other scholars. The experimental conclusion is that the improved DCNN has the characteristics of lower delay under deep learning. The model shows good performance for network data security transmission.

МОДЕЛИ МАШИННОГО ОБУЧЕНИЯ И ГЛУБОКОГО ОБУЧЕНИЯ ДЛЯ ЭЛЕКТРОННОЙ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ В МОБИЛЬНЫХ СЕТЯХ

Recent advances in wireless communication technologies have led to the creation of a hugeamount of data that is transmitted everywhere. Most of this information is part of an extensive andpublicly accessible network that connects various stationary and mobile devices around the world.The capabilities of electronic devices are also increasing day by day, which leads to more datageneration and information exchange through networks. Similarly, with the increasing diversityand complexity of mobile network structures, the frequency of security breaches in it has increased.This hinders the introduction of intelligent mobile applications and services, as evidencedby the wide variety of platforms that provide data storage, data computing and application services to end users. In such scenarios, it becomes necessary to protect data and check their use inthe network and applications, as well as check their incorrect use in order to protect private information.According to this study, a security model based on artificial intelligence should ensurethe confidentiality, integrity and reliability of the system, its equipment and protocols that controlthe network, regardless of its creation, in order to manage such a complex network as a mobileone. The open difficulties that mobile networks still face, such as unauthorized network scanning,fraudulent links, etc., have been thoroughly studied in this article. This article also discusses severalML and DL technologies that can be used to create a secure environment, as well as manycybersecurity threats. It is necessary to address the need to develop new approaches to ensure ahigh level of electronic data security in mobile networks, since the possibilities for improving thesecurity of mobile networks are limitless.

(Retracted) Software development and design of network security system based on log data

With the rapid development of computer network technology and the fact that the network brings convenience to people’s lives and work, security issues cannot be ignored. Various countries and social organizations build a network security vulnerability database based on big data analysis technology, use network offensive and defensive platforms to train network security talents, and the target environment as a core component in the offensive and defensive platforms plays an important role in practice. We construct a large network security data set with a security knowledge system, analyze the current construction technology and vulnerability data characteristics of major vulnerability databases at home and abroad from multiple levels, and propose a vulnerability information correlation technology based on a combination of general vulnerability numbers and specific vulnerability numbers. Also, we comprehensively analyze each vulnerability database, assess the comprehensiveness and coverage of the vulnerability data information covered, and establish the source of the vulnerability data, data reference, security content automation protocol support, proof of concept, platform environment affected by the vulnerability exploitation code situation, and other fields of network security big data sets with security knowledge system. Here, we have completed the design and implementation of the user rights management module, the centralized monitoring module, and the service desk and configuration change management module. The system can accurately monitor the operation of the system in real time, find problems in time, warn of potential risks, and promote the operation and maintenance approach to intelligent. The transformation has improved the efficiency of fault diagnosis and greatly enhanced the safety of the system. Experimental research shows that the software system automatically screens and manually confirms the vulnerabilities that meet the target construction environment, and initially constructs a total of 43 effective target environments, which provides security researchers with the understanding of vulnerability principles, hazards, and targeted network security training. A stable and convenient experimental environment.

Enhancing cybersecurity by generating user-specific security policy through the formal modeling of user behavior

Organizations today are faced with the difficult challenge of balancing the embrace of new and emerging technology, and securing their systems and data that support critical business functions. Although there have been significant advances in security enforcement technology, attackers are still able to compromise organizations and access. The impacts of computer intrusions have become so untenable that many organizations are looking at a drastic rethinking of their approach to the security of internal networks. This approach is called Zero Trust and it seeks to remove all notion of a trusted internal network boundary. The benefits of Zero Trust include significantly increasing the work that attackers would need to perform to achieve their objectives. But Zero Trust will also increase the management complexity for internal security teams. These teams will need a way to collect data and enforce policy decisions based upon analysis. This process will need to be done for all organizational systems, and data, and it will need to be done in all access contexts. Our approach uses formal methods to model and examine end-users security-related behaviors. Researchers have found that the users’ security decisions correlate with factors including demographics, personality traits, decision-making styles, and risk-taking preferences. We describe these behaviors by using Finite-State Automata (FSA). This allows for the automated formulation of linear-time security properties based on Timed Computation Tree Logic (TCTL). The logic is then used to check the satisfaction of collected and observed security behaviors against policy. This formal behavioral analysis could be combined with other security and network data during the context analysis process that needs to occur for each Zero Trust access request. Other network or host security data could include address identifiers, tokens, event data, packet inspection, running process data, cyber threat intelligence, and much more. Our method allows organizations that embrace a Zero Trust philosophy to generate context specific security policies that can be automatically verified for correctness and completion.

Blockchain and AI for Data Security in Distributed Systems

Abstract: Patients with hepatitis, influenza, cancer, and other deadly diseases are treated in hospitals in developing countries. In the event that a particular disease is encountered, staff can either refer the patient to an expert in that area or, in the absence of a diagnosis, may recommend the patient to the general physician. It is common for the general doctor to identify and eliminate an improbable disease by going through trial and error. A patient goes through this very painful ordeal as the doctors take one disease medication at a time to determine the cause of the symptoms. By using a Data Vendor, developed countries have largely addressed this problem. In this paper, we review all past work related to the provision of data security in networks, particularly those with data brokers. By introducing block chains and artificial intelligence, we can accomplish this best. An analysis of past data integrity methodologies in networks is presented in this article alongside an evaluation of a newer approach.

A Network Attack Risk Control Framework for Large-Scale Network Topology Driven by Node Importance Assessment

In large-scale network scenarios, network security data are characterized by complex association and redundancy, forming network security big data, which makes network security attack and defense more complicated. In this paper, the authors propose a framework for network attack risk control in large-scale network topology, called NARC. Using NARC, a user can determine the influence level of different nodes on the diffusion of attack risk in complex network topology, thus giving optimal risk control decisions. Specifically, this paper designs a topology-oriented node importance assessment model, combined with node vulnerability correlation analysis, to construct a diffusion network of attack risks for identifying potential attack paths. Furthermore, the optimal risk control node selection method based on game theory is proposed to obtain the optimal set of defense nodes. The experimental results demonstrate the feasibility of the proposed NARC, which helps to ease the risk of network attacks

Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection

Network anomaly detection is a very relevant research area nowadays, especially due to its multiple applications in the field of network security. The boost of new models based on variational autoencoders and generative adversarial networks has motivated a reevaluation of traditional techniques for anomaly detection. It is, however, essential to be able to understand these new models from the perspective of the experience attained from years of evaluating network security data for anomaly detection. In this paper, we revisit anomaly detection techniques based on PCA from a probabilistic generative model point of view, and contribute a mathematical model that relates them. Specifically, we start with the probabilistic PCA model and explain its connection to the Multivariate Statistical Network Monitoring (MSNM) framework. MSNM was recently successfully proposed as a means of incorporating industrial process anomaly detection experience into the field of networking. We have evaluated the mathematical model using two different datasets. The first, a synthetic dataset created to better understand the analysis proposed, and the second, UGR'16, is a specifically designed real-traffic dataset for network security anomaly detection. We have drawn conclusions that we consider to be useful when applying generative models to network security detection.

RAPCHI: Robust authentication protocol for IoMT-based cloud-healthcare infrastructure.

With the fast growth of technologies like cloud computing, big data, the Internet of Things, artificial intelligence, and cyber-physical systems, the demand for data security and privacy in communication networks is growing by the day. Patient and doctor connect securely through the Internet utilizing the Internet of medical devices in cloud-healthcare infrastructure (CHI). In addition, the doctor offers to patients online treatment. Unfortunately, hackers are gaining access to data at an alarming pace. In 2019, 41.4 million times, healthcare systems were compromised by attackers. In this context, we provide a secure and lightweight authentication scheme (RAPCHI) for CHI employing Internet of medical Things (IoMT) during pandemic based on cryptographic primitives. The suggested framework is more secure than existing frameworks and is resistant to a wide range of security threats. The paper also explains the random oracle model (ROM) and uses two alternative approaches to validate the formal security analysis of RAPCHI. Further, the paper shows that RAPCHI is safe against man-in-the-middle and reply attacks using the simulation programme AVISPA. In addition, the paper compares RAPCHI to related frameworks and discovers that it is relatively light in terms of computation and communication. These findings demonstrate that the proposed paradigm is suitable for use in real-world scenarios.

Research on data mining method of network security situation awareness based on cloud computing

Abstract Due to the complexity and versatility of network security alarm data, a cloud-based network security data extraction method is proposed to address the inability to effectively understand the network security situation. The information properties of the situation are generated by creating a set of spatial characteristics classification of network security knowledge, which is then used to analyze and optimize the processing of hybrid network security situation information using cloud computing technology and co-filtering technology. Knowledge and information about the security situation of a hybrid network has been analyzed using cloud computing strategy. The simulation results show that a cyber security crash occurs in window 20, after which the protection index drops to window 500. The increase in the security index of 500 windows is consistent with the effectiveness of the concept of this document method, indicating that this document method can sense changes in the network security situation. Starting from the first attacked window, the defense index began to decrease. In order to simulate the added network defense, the network security events in the 295th time window were reduced in the original data, and the defense index increased significantly in the corresponding time period, which is consistent with the method perception results, which further verifies the effectiveness and reliability of this method on the network security event perception. This method provides high-precision knowledge of network security situations and improves the security and stability of cloud-based networks.

Network Coding-based Resilient Routing for Maintaining Data Security and Availability in Software-Defined Networks

Software-Defined Networking (SDN) improves network performance by flexible traffic control. Data security and data availability are two main concerns for designing a resilient routing algorithm in SDN. Existing algorithms such as the MPT algorithm consider joint data security and availability, but they cannot make a good trade-off. In this paper, we propose a Network Coding-based Resilient Routing algorithm named NCRR to jointly achieve data security and availability. NCRR is a heuristic algorithm that computes routing decisions in three scenarios based on the number of disjoint paths. Specifically, the scenario of three disjoint paths is enough to ensure joint security and availability for a flow when three or more disjoint paths can be used for forwarding this flow. However, due to topological diversity, we cannot always find three disjoint paths for each flow. Thus, the scenarios of two disjoint paths and one path are used to ensure joint security and availability if only two disjoint paths and one path can be used. To evaluate the performance of NCRR, simulations have been conducted using two real-world network topologies. Simulation results show that NCRR improves the joint data security and availability performance by approximately 6.23% on AttMpls topology and 21.34% on Cernet topology, compared with existing MPT.

A chaotic video steganography technique for carrying different types of secret messages

Increasing the security of data in insecure communication networks is the motivation of researchers to propose different types of methods. Steganography is one of the security techniques that is used in videos, images, sounds, and other digital data. This technique embeds important data or secret messages into cover media to prevent unauthorized access. This paper proposes a new method for video steganography to develop our security purposes. The proposed method uses integer wavelet transform to increase the security and quality of video frames. This paper also presents an improved three-dimensional Sine chaotic map and uses it in the processes of the proposed video steganography. Hiding different types of messages with various sizes is the main aim of the suggested algorithm. The simulation results show that the proposed method has good imperceptibility and performance after the embedding process. Furthermore, the results of extracted messages after applying some noises on the stego videos, and the regular/singular (RS) steganalysis results demonstrate the acceptable robustness against some noises. Also, comparison results show that the proposed method outperforms some similar approaches.

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks.

Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

Lightweight and High-Performance Data Protection for Edge Network Security

To strengthen the security of edge network data and reduce network latency, in this paper, we combine an advanced reduced instruction set computing machine (ARM) and a field programmable gate array (FPGA) to propose a lightweight ARM-FPGA computing architecture for edge network data security protection and acceleration. Firstly, the access control lists are set through FPGA to authorize and filter illegal data, thereby reducing the transmission and processing of invalid data on the network. Secondly, based on the principles of dynamization, diversification, and randomization, the initial random key is generated using a pseudo-random number generator and a scrambling factor, and the hash of the data packet value updates the key to ensure “one frame, one key.” Then, an ARM microprocessor is used to monitor the working status of the system in real-time. If an abnormality is found, different disturbance factors are activated using key management to change the system running status and restore the stability of the system. Finally, pipeline technology and critical path optimization on FPGA are used to parallelize the underlying encryption algorithm and data compression algorithm to achieve high-speed memory communication and meet the performance requirements of different networks. The experimental and analysis results show that the computing architecture designed in this paper has high network encryption performance and can effectively prevent data leakage. In addition, the effectiveness of the architecture in terms of network delay and network load is verified.

Application of Computer Data Mining Technology Based on AKN Algorithm in Denial of Service Attack Defense Detection

Denial of service attacks have become one of the most difficult network security problems because they are easy to implement, difficult to prevent, and difficult to track, and they have brought great harm to the network society. Denial of service (Dos) is a phenomenon in which a large number of useless data packets or obstructive content are maliciously transmitted to the target server, which makes the target server unable to provide users with normal services. Denial of service attack (Dos attack) is a very typical network attack method, and the main harm of Dos attack is to exhaust service resources, making the computer or network unable to provide normal services. And AKN (adaptive Kohonen network) is an adaptive neural network proposed in recent years, and an algorithm summarized by using the characteristics of the neural network is called the AKN algorithm. This algorithm can realize fast, low-consumption, and high-precision denial of service detection in complex networks. In the era of big data, network security is becoming more and more important, and in order to maintain the security of network data, this article studies the common forms and principles of Dos attacks, as well as the current corresponding defense detection methods. It also investigates several commonly used algorithms of computer data mining technology, such as clustering algorithms, classification algorithms, neural network algorithms, regression algorithms, website data mining, and association algorithms, and proposes a computer data mining model based on the AKN algorithm. In addition, the computer data mining technology based on the AKN algorithm is used to conduct defensive detection experiments under Dos attacks and compares with classic algorithms. Experimental results show that experiments based on the AKN algorithm have better defensive detection effects than classic algorithms, with a detection accuracy rate of more than 97% and a detection efficiency improvement of more than 20%.