Biblio

Wireless Sensor networks can be composed of smart buildings, smart homes, smart grids, and smart mobility, and they can even interconnect all these fields into a large-scale smart city network. Software-Defined Networking is an ideal technology to realize Internet-of-Things (IoT) Network and WSN network requirements and to efficiently enhance the security of these networks. Software defines Networking (SDN) is used to support IoT and WSN related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. This work is a study of different security mechanisms available in SDN for IoT and WSN network secure communication. This work also formulates the problems when existing methods are implemented with different networks parameters.

With the advent of the Internet era, all walks of life in our country have undergone earth-shaking changes, especially the drone and geographic information industries, which have developed rapidly under the impetus of the Internet of Things era. However, with the continuous development of science and technology, the network structure has become more and more complex, and the types of network attacks have varied. UAV information security and geographic information data have appeared security risks on the network. These hidden dangers have contributed to the progress of the drone and geographic information industry. And development has caused a great negative impact. In this regard, this article will conduct research on the network security of UAV systems and geographic information data, which can effectively assess the network security risks of UAV systems, and propose several solutions to potential safety hazards to reduce UAV networks. Security risks and losses provide a reference for UAV system data security.

Artificial technology developed in recent years. It is an intelligent system that can perform tasks without human intervention. AI can be used for various purposes, such as speech recognition, face recognition, etc. AI can be used for good or bad purposes, depending on how it is implemented. The discuss the application of AI in data security technology and its advantages over traditional security methods. We will focus on the good use of AI by analyzing the impact of AI on the development of big data security technology. AI can be used to enhance security technology by using machine learning algorithms, which can analyze large amounts of data and identify patterns that cannot be detected automatically by humans. The computer big data security technology platform based on artificial intelligence in this paper is the process of creating a system that can identify and prevent malicious programs. The system must be able to detect all types of threats, including viruses, worms, Trojans and spyware. It should also be able to monitor network activity and respond quickly in the event of an attack.

The recent 5G networks aim to provide higher speed, lower latency, and greater capacity; therefore, compared to the previous mobile networks, more advanced and intelligent network security is essential for 5G networks. To detect unknown and evolving 5G network intrusions, this paper presents an artificial intelligence (AI)-based network threat detection system to perform data labeling, data filtering, data preprocessing, and data learning for 5G network flow and security event data. The performance evaluations are first conducted on two well-known datasets-NSL-KDD and CICIDS 2017; then, the practical testing of proposed system is performed in 5G industrial IoT environments. To demonstrate detection against network threats in real 5G environments, this study utilizes the 5G model factory, which is downscaled to a real smart factory that comprises a number of 5G industrial IoT-based devices.

The introduction of the study primarily emphasises the significance of utilising block chain technologies with the possibility of privacy and security benefits from the 5G Network. One may state that the study’s primary focus is on all the advantages of adopting block chain technology to safeguard everyone’s access to crucial data by utilizing intelligent contracts to enhance the 5G network security model on information security operations.Our literature evaluation for the study focuses primarily on the advantages advantages of utilizing block chain technology advance data security and privacy, as well as their development and growth. The whole study paper has covered both the benefits and drawbacks of employing the block chain technology. The literature study part of this research article has, on the contrary hand, also studied several approaches and tactics for using the blockchain technology facilities. To fully understand the circumstances in this specific case, a poll was undertaken. It was possible for the researchers to get some real-world data in this specific situation by conducting a survey with 51 randomly selected participants.

As an important branch of computer forensics, network forensics technology, whether abroad or at home, is in its infancy. It mainly focuses on the research on the framework of some forensics systems or some local problems, and has not formed a systematic theory, method and system. In order to improve the network forensics sys-tem, have a relatively stable and correct model for refer-ence, ensure the authenticity and credibility of network fo-rensics from the forensics steps, provide professional and non professional personnel with a standard to measure the availability of computer network crime investigation, guide the current network forensics process, and promote the gradual maturity of network forensics theories and methods, This paper presents a fuzzy decision tree reason-ing method for network forensics analysis.

The experimental results demonstrated that, With the development of cloud computing, more and more people use cloud computing to do all kinds of things. However, for cloud computing, the most important thing is to ensure the stability of user data and improve security at the same time. From an analysis of the experimental results, it can be found that Cloud computing makes extensive use of technical means such as computing virtualization, storage system virtualization and network system virtualization, abstracts the underlying physical facilities into external unified interfaces, maps several virtual networks with different topologies to the underlying infrastructure, and provides differentiated services for external users. By comparing and analyzing the experimental results, it is clear that virtualization technology will be the main way to solve cloud computing security. Virtualization technology introduces a virtual layer between software and hardware, provides an independent running environment for applications, shields the dynamics, distribution and differences of hardware platforms, supports the sharing and reuse of hardware resources, provides each user with an independent and isolated computer environment, and facilitates the efficient and dynamic management and maintenance of software and hardware resources of the whole system. Applying virtualization technology to cloud security reduces the hardware cost and management cost of "cloud security" enterprises to a certain extent, and improves the security of "cloud security" technology to a certain extent. This paper will outline the basic cloud computing security methods, and focus on the analysis of virtualization cloud security technology

With the development of software defined network and network function virtualization, network operators can flexibly deploy service function chains (SFC) to provide network security services more than before according to the network security requirements of business systems. At present, most research on verifying the correctness of SFC is based on whether the logical sequence between service functions (SF) in SFC is correct before deployment, and there is less research on verifying the correctness after SFC deployment. Therefore, this paper proposes a method of using Colored Petri Net (CPN) to establish a verification model offline and verify whether each SF deployment in SFC is correct after online deployment. After the SFC deployment is completed, the information is obtained online and input into the established model for verification. The experimental results show that the SFC correctness verification method proposed in this paper can effectively verify whether each SF in the deployed SFC is deployed correctly. In this process, the correctness of SF model is verified by using SF model in the model library, and the model reuse technology is preliminarily discussed.

From an information-theoretic standpoint, the intrusion detection process can be examined. Given the IDS output(alarm data), we should have less uncertainty regarding the input (event data). We propose the Capability of Intrusion Detection (CID) measure, which is simply the ratio of mutual information between IDS input and output, and the input of entropy. CID has the desirable properties of (1) naturally accounting for all important aspects of detection capability, such as true positive rate, false positive rate, positive predictive value, negative predictive value, and base rate, (2) objectively providing an intrinsic measure of intrusion detection capability, and (3) being sensitive to IDS operation parameters. When finetuning an IDS, we believe that CID is the best performance metric to use. In terms of the IDS’ inherent ability to classify input data, the so obtained operation point is the best that it can achieve.

Based on the characteristics of the new power system with many points, wide range and unattended, this paper studies the specific Cyberspace security risks faced by the disease control side, the station side and the site side, and proposes a new power system Cyberspace security assurance system of “integration of collection, network, side, end, industry and people”. The site side security access measures, the site side civil air defense technology integration measures, the whole business endogenous security mechanism, the whole domain communication security mechanism, the integrated monitoring and early warning and emergency response mechanism are specifically adopted to form a comprehensive integrated security mechanism for the new power system, form a sustainable protection model, effectively improve the security capability, while taking into account the cost and operational complexity of specific implementation links, Provide comprehensive guarantee capability for the safe operation of the new power system.

This article provides an overview of the security of VANET, which is a vehicle network. When reviewing this topic, publications of various researchers were considered. The article provides information security requirements for VANET, an overview of security research, an overview of existing attacks, methods for detecting attacks and appropriate countermeasures against such threats.

Machine tool is known as the mother of industry. CNC machine tool is the embodiment of modern automatic control productivity. In the context of the rapid development of the industrial Internet, a large number of equipment and systems are interconnected through the industrial Internet, realizing the flexible adaptation from the supply side to the demand side. As the a typical core system of industrial Internet, CNC system is facing the threat of industrial virus and network attack. The problem of information security is becoming more and more prominent. This paper analyzes the security risks of the existing CNC system from the aspects of terminal security, data security and network security. By comprehensively using the technologies of data encryption, identity authentication, digital signature, access control, secure communication and key management, this paper puts forward a targeted security protection and management scheme, which effectively strengthens the overall security protection ability.

With the introduction of the national “carbon peaking and carbon neutrality” strategic goals and the accelerated construction of the new generation of power systems, cloud applications built on advanced IT technologies play an increasingly important role in meeting the needs of digital power business. In view of the characteristics of the current power industrial control system operation support cloud platform with wide coverage, large amount of log data, and low analysis intelligence, this paper proposes a cloud platform network security behavior audit method based on FP-Growth association rule algorithm, aiming at the uniqueness of the operating data of the cloud platform that directly interacts with the isolated system environment of power industrial control system. By using the association rule algorithm to associate and classify user behaviors, our scheme formulates abnormal behavior judgment standards, establishes an automated audit strategy knowledge base, and improves the security audit efficiency of power industrial control system operation support cloud platform. The intelligent level of log data analysis enables effective discovery, traceability and management of internal personnel operational risks.

The industrial Internet platform has been applied to various fields of industrial production, effectively improving the data flow of all elements in the production process, improving production efficiency, reducing production costs, and ensuring the market competitiveness of enterprises. The premise of the effective application of the industrial Internet platform is the interconnection of industrial equipment. In the industrial Internet platform, industrial robot is a very common industrial control device. These industrial robots are connected to the control network of the industrial Internet platform, which will have obvious advantages in production efficiency and equipment maintenance, but at the same time will cause more serious network security problems. The industrial robot system based on the industrial Internet platform not only increases the possibility of industrial robots being attacked, but also aggravates the loss and harm caused by industrial robots being attacked. At the same time, this paper illustrates the effects and scenarios of industrial robot attacks based on industrial interconnection platforms from four different scenarios of industrial robots being attacked. Availability and integrity are related to the security of the environment.

The research try to implements an intelligent network operation management system for enterprise networks. First, based on Flask-state software architecture, the system adapt to Phytium CPU and Galaxy Kylin operating system successfully. Second, using the Isolation Forest algorithm, the system implements the anomaly detection of host data such as CPU usage. Third, using the Holt-winters seasonal prediction model, the system can predict time series data such as network I/O. The results show that the system can realizes anomaly detection and time series data prediction more precisely and intelligently.

Benchmark datasets are heavily depended upon by the research community to validate theoretical findings and track progression in the state-of-the-art. NIDS dataset creation presents numerous challenges on account of the volume, heterogeneity, and complexity of network traffic, making the process labor intensive, and thus, prone to error. This paper provides a critical review of CIC-IDS-2017 and CIC-CSE-IDS-2018, datasets which have seen extensive usage in the NIDS literature, and are currently considered primary benchmarking datasets for NIDS. We report a large number of previously undocumented errors throughout the dataset creation lifecycle, including in attack orchestration, feature generation, documentation, and labeling. The errors destabilize the results and challenge the findings of numerous publications that have relied on it as a benchmark. We demonstrate the implications of these errors through several experiments. We provide comprehensive documentation to summarize the discovery of these issues, as well as a fully-recreated dataset, with labeling logic that has been reverse-engineered, corrected, and made publicly available for the first time. We demonstrate the implications of dataset errors through a series of experiments. The findings serve to remind the research community of common pitfalls with dataset creation processes, and of the need to be vigilant when adopting new datasets. Lastly, we strongly recommend the release of labeling logic for any dataset released, to ensure full transparency.

The success of the web and the consequent rise in data sharing have made network security a challenge. Attackers from all around the world target PC installations. When an attack is successful, an electronic device's security is jeopardised. The intrusion implicitly includes any sort of behaviours that purport to think twice about the respectability, secrecy, or accessibility of an asset. Information is shielded from unauthorised clients' scrutiny by the integrity of a certain foundation. Accessibility refers to the framework that gives users of the framework true access to information. The word "classification" implies that data within a given frame is shielded from unauthorised access and public display. Consequently, a PC network is considered to be fully completed if the primary objectives of these three standards have been satisfactorily met. To assist in achieving these objectives, Intrusion Detection Systems have been developed with the fundamental purpose of scanning incoming traffic on computer networks for malicious intrusions.

Network attacks are always a nightmare for the network administrators as it eats away a huge wavelength and disturbs the normal working of many critical services in the network. Network behavior based profiling and detection is considered to be an accepted method; but the modeling data and method is always a big concern. The network event-based profiling is getting acceptance as they are sequential in nature and the sequence depicts the behavior of the system. This sequential network events can be analyzed using different techniques to create a profile for anomaly detection. In this paper we examine the possibility of two techniques for sequential event analysis using Modified GSP and IPAM algorithm. We evaluate the performance of these algorithms on the CSE-CIC-IDS 2018 data set to benchmark the performance. This experiment is different from other anomaly-based detection which evaluates the features of the dataset to detect the abnormalities. The performance of the algorithms on the dataset is then confirmed by the pattern evolving from the analysis and the indications it provides for early detection of network attacks.

With the rapid popularity of the network, the development of information encryption technology has a significant role and significance in securing network security. The security of information has become an issue of concern to the whole society, and the study of cryptography has been increasingly concerned, and the hash function is the core of modern cryptography, the most common hash algorithms are MD5 series of algorithms, SHA series of algorithms. MD5 is a popular and excellent typical Hash encryption technology today, which is used for password management, electronic signature, spam screening. In this paper, we focus on the improved MD5 algorithm with more efficiency, focusing on the internal structure of MD5, and finally making it more efficient in retrieval.

Cyberspace is vulnerable to continuous malicious attacks. Traceability of network attacks is an effective defense means to curb and counter network attacks. In this paper, the evolutionary game model is used to analyze the network attack and defense behavior. On the basis of the quantification of attack and defense benefits, the replication dynamic learning mechanism is used to describe the change process of the selection probability of attack and defense strategies, and finally the evolutionary stability strategies and their solution curves of both sides are obtained. On this basis, the attack behavior is analyzed, and the probability curve of attack strategy and the optimal attack strategy are obtained, so as to realize the effective traceability of attack behavior.

At present, the research on the network security problem of underwater wireless sensors is still few, and since the underwater environment is exposed, passive security defense technology is not enough to deal with unknown security threats. Aiming at this problem, this paper proposes an offensive and defensive game model from the finite rationality of the network attack and defense sides, combined with evolutionary game theory. The replicated dynamic equation is introduced to analyze the evolution trend of strategies under different circumstances, and the selection algorithm of optimal strategy is designed, which verifies the effectiveness of this model through simulation and provides guidance for active defense technology.

Secrete message protection has become a focal point of the network security domain due to the problems of violating the network use policies and unauthorized access of the public network. These problems have led to data protection techniques such as cryptography, and steganography. Cryptography consists of encrypting secrete message to a ciphertext format and steganography consists of concealing the secrete message in codes that make up a digital file, such as an image, audio, and video. Steganography, which is different from cryptography, ensures hiding a secret message for secure transmission over the public network. This paper presents a steganographic approach using digital images for data hiding that aims to providing higher performance by combining fuzzy logic type I to pre-process the cover image and difference expansion techniques. The previous methods have used the original cover image to embed the secrete message. This paper provides a new method that first identifies the edges of a cover image and then proceeds with a difference expansion to embed the secrete message. The experimental results of this work identified an improvement of 10% of the existing method based on increased payload capacity and the visibility of the stego image.

This paper examines how the extent of the network has expanded from the traditional computer Internet to the field of edge computing based on mobile communication technology with the in-depth development of the mobile Internet and the Internet of Things. In particular, the introduction of 5G has enabled massive edge computing nodes to build a high-performance, energy-efficient and low-latency mobile edge computing architecture. Traditional network security technologies and methods are not fully applicable in this environment. The focus of this paper is on security protection for edge networks. Using virtualized networks builds a dynamic heterogeneous redundancy security model (i.e., DHR). It first designs and evaluates the DHR security model, then constructs the required virtualized heterogeneous entity set, and finally constructs a DHR-based active defense scheme. Compared with existing network security solutions, the security protection technology of the edge network studied this time has a better protective effect against the unknown security threats facing the edge network.

To prevent all sorts of attacks, the technology of security service function chains (SFC) is proposed in recent years, it becomes an attractive research highlights. Dynamic orchestration algorithm can create SFC according to the resource usage of network security functions. The current research on creating SFC focuses on a single domain. However in reality the large and complex networks are divided into security domains according to different security levels and managed separately. Therefore, we propose a cross-security domain dynamic orchestration algorithm to create SFC for network security functions based on ant colony algorithm(ACO) and consider load balancing, shortest path and minimum delay as optimization objectives. We establish a network security architecture based on the proposed algorithm, which is suitable for the industrial vertical scenarios, solves the deployment problem of the dynamic orchestration algorithm. Simulation results verify that our algorithm achieves the goal of creating SFC across security domains and demonstrate its performance in creating service function chains to resolve abnormal traffic flows.

We present an online framework for learning and updating security policies in dynamic IT environments. It includes three components: a digital twin of the target system, which continuously collects data and evaluates learned policies; a system identification process, which periodically estimates system models based on the collected data; and a policy learning process that is based on reinforcement learning. To evaluate our framework, we apply it to an intrusion prevention use case that involves a dynamic IT infrastructure. Our results demonstrate that the framework automatically adapts security policies to changes in the IT infrastructure and that it outperforms a state-of-the-art method.