Current Network Environment Research Articles

Open set identification of malicious encrypted traffic based on multi-feature fusion

In the current network environment, an increasing amount of malicious traffic is transmitted through encrypted channels, carrying control commands and data. With the continuous development of communication protocols and applications, new types of malicious encrypted traffic are emerging, posing significant challenges for network management (e.g., traffic engineering). Therefore, accurately identifying malicious traffic in complex open network spaces has become a hot research topic in network security. In this study, we draw inspiration from channel theory in image science and innovatively convert traffic data into Red-Green-Blue (RGB) image format to achieve the fusion of multiple features. Inspired by image recognition technologies, we have designed a multi-granularity network model that integrates both global and local features, serving as our core network architecture. At the top of the model, we have equipped each known category with a unique autoencoder, using its generated manifold to replace traditional prototypes for model construction. Classification is accomplished through a scoring mechanism that evaluates category membership and by setting thresholds to achieve open set recognition of unknown categories. Relying on our self-created dataset,Malicious and Encrypted Traffic 2024 (MNET2024), we conduct a series of extensive experiments. The results demonstrate that our proposed method exhibits outstanding performance in both closed-set and open-set recognition tasks.

Research On the Interrelationship Between Various Types of Fake News and Cyber Violence

In the swiftly advancing landscape of technology, the era of the Internet is quietly fading away, assuming an increasingly pivotal role in people's lives. As individuals flock to the virtual realm, actively participating in shaping the network environment, complexities arise. The network environment becomes a dynamic space marked by the constant impact of false news and network violence, transforming the Internet from a center of information exchange into a breeding ground for malevolence. This prompts a critical examination of the nexus between fake news and cyber violence, raising questions about how different types of false information influence online violence. This study introduces various fake news typologies and elucidates the concepts of fake news and cyber violence, analyzing the role of false information in the emergence of cyber violence. A concise review of relevant literature underscores the correlation between cyber violence and fake news. The article aims to stimulate contemplation on enhancing the current network environment and reducing the propagation of false information and cyber violence. Additionally, it proposes insights to foster a healthier online ecosystem and advocates for collective efforts to mitigate the impact of misinformation and cyber violence on society.

The Prosperous Short Video Environment: Hidden Threats to Young People's Mental Health

Abundant online media fill people's lives, especially the prosperity of short videos, which provide a convenient way of entertainment for young people's busy lives. However, many studies have found that while it brings people fun, it also brings many psychological problems. This study aims to address the psychological problems that occur in young people and propose ways to reduce the impact of the problems. By reading existing literature, combined with network data and the current network environment, this article finds out the psychological problems that young people will have after using short video platforms, such as blurring the boundaries between reality and virtual behavior, one-sided videos increasing psychological anxiety. After analyzing the problems, this paper found that the main causes of these problems came from social pressure and the platform's regulatory capabilities. Hence, young people can reshape their understanding of the short video virtual environment and improve their ability to resist the psychological problems caused by exploring new interests.

Analysis of a new firewall constructed on Pfsense with Snort to defend against common internet intrusions

This paper aims to design a new firewall based on the detection of common forms of network attacks in the current Internet environment using Snort in combination with the Pfsense network security platform. Firstly, the functions of Snort and Pfsense are introduced, and the shortcomings of traditional firewalls in the current network environment are analyzed. Then analyze the common port scanning attacks, DOS attacks and algorithm complexity attacks, and by means of reviewing the literature, demonstrate that it adopts the network proxy, CNN-BiLSTM intrusion detection model, IBM algorithm, VLDC algorithm and other means to specifically detect common network attacks, so as to achieve the function of specifically defending against network attacks, and to improve the operation efficiency and detection accuracy of the firewall. accuracy of the firewall, etc.

An insider user authentication method based on improved temporal convolutional network

With the rapid development of information technology, information system security and insider threat detection have become important topics for organizational management. In the current network environment, user behavioral bio-data presents the characteristics of nonlinearity and temporal sequence. Most of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature extraction. They do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data, and also do not adequately reflect the personalized usage characteristics of users, leading to bottlenecks in the performance of the authentication algorithm. In order to solve the above problems, this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism (ECA-TCN) to extract user mouse dynamics features and constructs an one-class Support Vector Machine (OCSVM) for each user for authentication. Experimental results show that compared with four existing deep learning algorithms, the method retains more adequate key information and improves the classification performance of the neural network. In the final authentication, the Area Under the Curve (AUC) can reach 96%.

A method for analyzing Stackelberg attack–defense game model in 5G by tCPSO

The development of 5G brings convenience to people, but it also brings threats. It is particularly important to analyze attack–defense confrontation from a macro perspective. Network security of graph is an important research problem in current network environment. A Stackelberg game model based on 5G network graph is established to analyze the attack–defense confrontation in this paper. In order to solve the Nash equilibrium, a compact particle swarm optimization based on t-Location-Scale distribution (tCPSO) is proposed. The tCPSO uses less memory space and has better optimization performance. The experiments prove that tCPSO has better optimization performance than other heuristic algorithms on CEC2013. The clustering experiments further show that tCPSO has better optimization ability than other heuristic algorithms. The optimal defense strategies under different attack–defense resources are analyzed in this paper. It is concluded that increasing the successful defense rate is more effective for the defender to obtain greater profit than reducing the successful attack rate. Using intelligent computing to solve Nash equilibrium is a new idea for researchers to analyze attack–defense confrontation.

Multipath TCP Meets Reinforcement Learning: A Novel Energy-Efficient Scheduling Approach in Heterogeneous Wireless Networks

Multipath TCP (MPTCP) has been standardized by the IETF as an extension of conventional TCP and it allows the system to utilize multiple paths simultaneously, which can aggregate bandwidth to improve network throughput. However, MPTCP needs to open multiple interfaces at the same time, which makes MPTCP consume more energy to maintain multiple interface connections. Thus, how to manage subflows with the MPTCP's scheduling system to determine which paths should be used for data transmission is of critical importance to reduce energy consumption and ensure network throughput. Due to the path heterogeneity and random packet losses in wireless networks, existing scheduling systems, and selecting paths based on the path's delay or energy cost, may suffer from performance degradation. In this article, we propose a reinforcement learning-based multipath scheduler called MPTCP-RL to determine the optimal path set for different flows. MPTCP-RL adopts deep reinforcement learning as well as MPTCP transmission model to manage path usage among multiple connections to make sure that the sender can adaptively select the optimal path set for a certain application according to the current network environment. MPTCP-RL is an asynchronous reinforcement learning framework, which separates the processes of offline training and online decision to ensure that the learning process will not introduce extra delay and overhead on the decision making process in MPTCP path management. The extensive experimental results show that MPTCP-RL can improve the aggregate throughput and reduce energy consumption significantly compared to the state-of-the-art mechanisms in a variety of network scenarios.

An SSA-LC-DAE Method for Extracting Network Security Elements

In the current network environment, the original network data present the characteristics of multiple features and high dimensions. Furthermore, in the existing element extraction methods based on deep neural networks, the loss of feature information layer by layer increases as the data dimensions decrease, which greatly affects the retention of network data information elements and brings a huge challenge to effective network security protection. This paper refers to a residual neural network to improve the deep autoencoder (DAE) and then utilizes them to propose a novel element extraction method named the layer-by-layer loss compensation deep autoencoder (LC-DAE) based on the sparrow search algorithm (SSA). In the proposed method, a loss compensation module is added to each encoding layer of the DAE. Specifically, this module first restores the data by using the decoding layer corresponding to the encoding layer. Then, the loss value of the calculated characteristic information is compensated using the output of the corresponding encoding layer. Subsequently, the SSA is used to optimize the LC-DAE in the training process. Finally, the experimental results show that compared with the existing methods, this method retains more sufficient element information, and significantly improves the classification performance of data using neural networks.

Optimization of Ideological and Political Education Courses for College Students and Strategies for Mental Health Education.

Most college students are still in the growth stage in the complex network environment. Therefore, it is necessary to consider measures for ideological education and psychological training. Firstly, by citing literature, this work explains the current network environment college students face and the adverse effects on their psychology. Secondly, the relationship between the ideological education work of college students and the network environment is expounded. Finally, the sampling survey method is adopted to analyze the use of the internet, information contact behavior, and the psychological status of college students at the present stage. The results showed that: (1) those with a college degree or above accounted for 47.1%. The data show that college students are at the forefront of internet use. They bear more social responsibilities than other groups. (2) About 90% of people have different degrees of depression and anxiety. The data show that psychological training and ideological and political education (IPE) for college students is necessary. This paper aims to sum up the obstacles existing in IPE and psychological training through the influence of network environment and bad information on IPE of college students. Based on the survey results, this paper can find a solution to create a good IPE environment for college students.

Blockchain-based dynamic trust access control game mechanism

The blockchain-based access control mechanism (BACM) is gradually becoming an essential paradigm for solving dynamic and trusted access control problems in the open network environment. However, since the current open network environment has such features as dynamic variability and the uncertainty of user identity, most of the existing BACM cannot solve the access control problems in the current open network environment in a dynamic, flexible, proactive, efficient, and fine-grained approach. In this paper, we propose a novel BACM scheme to address such problems. Specifically, we first design a new, proactive, and fine-grained access control model, by utilizing the dynamicity and fine-grain of the attribute-based access control model, flexibility shown by the trust evaluation mechanism in evaluating the trust level of users, and proactivity shown by the game evaluation mechanism in curbing malicious users who suddenly launch malicious access requests. Second, based on the above access control model, we propose a dynamic, flexible, and proactive BACM for the current open network environment, exploiting the trustworthiness and transparency that the smart contract and the transaction mechanism in blockchain technology show during program execution. Further, a double sliding storage window is built, guaranteeing accurate data acquisition by BACM while efficiently allowing it to acquire time-sensitive data during the permission management process. Meanwhile, a pre-authorization concept is introduced to improve the efficiency and flexibility of BACM in processing access control problems. Security analysis demonstrates that our proposed BACM scheme satisfies the simple security issue and the simple availability issue. Experiments on a real user trust record dataset demonstrate the high effectiveness of the proposed BACM scheme in evaluating and deciding on access requests and the superiorities over most existing schemes in dynamicity, fine granularity, flexibility, and proactivity.

Multi-hop transmission method of 5G millimeter wave relay based on network coding

With the rapid development of the Internet technology, millimeter wave (mmWave) will be used as a supplement to 5G low frequency bands to meet the extremely high system capacity requirements of 5G in hot spots. Although 5G mmWave communication can adapt to the needs of 5G network and carry a large amount of transmitted data, transmission stability has become one of the key technical issues of 5G network mmWave communication due to problems such as strong attenuation and poor penetration of mmWave. In order to improve the efficiency of the mmWave multi-hop transmission, we propose a 5G mmWave multi-hop transmission method based on network coding, which can adapt to the current wireless network environment, improve spectrum efficiency and increase network throughput. Based on MATLAB simulation experiments, it is verified that the proposed method can greatly improve the transmission efficiency and reduce the signal loss under the premise of ensuring the accurate signal transmission.

Computer Network Information Security Monitoring System Based on Big Data Era

With the rapid advancement of society and the level of programming and the rapid development of computer technology, networking and information are playing an increasingly important role in the social life of the masses. Creating and developing a network information security monitoring system have become one of the most important ways to keep a computer running smoothly. Traditional information security systems cannot adapt to the ever-changing network environment. If only relying on it to maintain network security, it will be far from effective monitoring and defense. Based on the theory of network information security, this study analyzes the characteristics of network information and the information security monitoring technology at the current stage. Based on the background of big data era, a new type of computer network information security monitoring system is proposed. This system is compared with the traditional network information security monitoring system, and the performance and stability of the system are investigated, respectively. The experimental data show that the network information security monitoring system designed in this study can achieve more than 99% detection rate of external attacks in a network environment with a background traffic of 10M. Its false alarm rate is lower than 4%, and the false alarm rate is lower than 7%. The qualitative mean reaches 93.02%, indicating its good monitoring accuracy and stability. By popularizing it in the current network environment, it can effectively identify and defend information attacks and maintain the development of network information security.

Learning Tailored Adaptive Bitrate Algorithms to Heterogeneous Network Conditions: A Domain-Specific Priors and Meta-Reinforcement Learning Approach

Internet adaptive video streaming is a typical form of video delivery that leverages adaptive bitrate (ABR) algorithms to provide video services with high quality of experience (QoE) for various users in diverse and unique network conditions. Such heterogeneous network environments, which can be viewed as exogenous input processes, often lead to the unstable performance of ABR algorithms. Unfortunately, learning-based ABR algorithm which generated by state-of-the-art reinforcement learning (RL) technologies achieves <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">good average performance</i> but fails to perform well in all kinds of network conditions. In this work, considering the video playback process as the Input-driven Markov Decision Process (IMDP), we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR (Adaptation of ABR), a novel meta-RL ABR approach. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR is mainly composed of an online stage and an offline stage. It leverages meta-RL to learn an initial meta-policy with various network conditions at the offline stage and makes decisions in personalized network conditions at the online stage. At the same time, we continually optimize the meta-policy to the tailor-made ABR policy for varying the current network environment within few shots. Moreover, in order to improve the learning efficiency, we fully utilize domain knowledge for implementing a virtual player to replay the previously experienced network. Using trace-driven experiments on various scenarios including different vehicles, users, network types, and heterogeneous user-preferences, we show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR outperforming recent ABR approaches with rapidly adapting to the personalized QoE metrics and specific network conditions. Testbed experimental results also illustrate the superiority of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR in adapting to the unseen environments.

Blockchain Private File Storage-Sharing Method Based on IPFS.

Under the current national network environment, anyone can participate in publishing. As an important information resource, knowledge files reflect the workload of publishers. Moreover, high-quality knowledge files can promote the progress of society. However, pirated inferior files have the opposite effect. At present, most organizations use centralized servers to centrally manage the knowledge files released by users. In addition, it is necessary to introduce an untrusted third party to examine and encrypt the contents of files, which leads to an opaque process of file storage transactions, tampering with intellectual copyright, and the inability to have consistent systems of file management among institutions due to the lack of uniform standards for the same intellectual files. The purpose of this paper is to ensure the safe storage of knowledge files on the one hand and to realize efficient sharing of copyrighted files on the other hand. Therefore, this paper combines NDN (Named Data Network) technology with a distributed blockchain and an Interplanetary File System (IPFS) and proposes a blockchain knowledge file storage and sharing method based on an NDN. The method uses the NDN itself for the file content signature and encryption, thereby separating the file security and transmission process. At the same time, the method uses a flexible NDN reverse path forwarding and routing strategy, combining an IPFS private storage network to improve the safety of the encrypted data storage security. Finally, the method takes advantage of all participating nodes consensus and shares files in the synchronized blockchain to ensure traceability. This paper introduces the structure and principles of the method and describes the process of file upload and transfer. Finally, the performance of the method is compared and evaluated, and the advantages and disadvantages of the method and the future research direction are summarized.

Adaptive Model of Discrete Real-Time Linear Dynamic Logic for the Optimization of Big Data Game Education Mode

With the advent of the era of big data and mobile Internet, the traditional teaching model of colleges and universities has been hit unprecedentedly. In the current big data and artificial intelligence network environment, new teaching concepts, characteristics, and model applications should be analyzed, and an education model based on artificial intelligence adaptive control and negative feedback adjustment mechanisms under the big data environment should be explored. The study of this model can provide practical experience and reference value for the application, popularization, and innovation of teaching information technology in the future. Combining the characteristics of big data games, this paper proposes a college student education model based on discrete dynamic model big data game. The scores of college students represent comprehensive information such as their qualifications to provide big data university education, the credit of historical records, and the degree of reliable data transmission. Based on the general model of the adaptive learning system, we have perfected the functions of each module in the overall structure of the domain-based special subject system and designed the overall workflow of the system. Computerized evaluation is introduced into the learner module, and an adaptive evaluation system structure based on cognitive diagnosis theory is designed to achieve a personalized topic selection model for different learners and enrich learners’ experience through more detailed and specialized evaluation results. The model lays the foundation for adaptive course path recommendation. This paper proposes a discrete real-time linear dynamic logic that can express discrete real-time characteristics and all regular characteristics. It can easily describe the behavior of some complex real-time systems. Experiments show that the discrete dynamic model big data game efficiency is improved by 35.71%, which can better optimize the education model of college students. In addition, it can be concluded that the research on the effectiveness of college student education in the era of big data not only determines the safety of the ideological field of colleges and universities but also affects the thinking orientation of college students.

Computer Network Intrusion Anomaly Detection with Recurrent Neural Network

Network intrusion anomaly detection technique has been widely employed in computer network environments as a highly effective security prevention method. As network technology and network applications have advanced at a rapid pace, so too has network data traffic, resulting in an increase in virus and attack kinds. In the face of large-scale traffic and characteristic information, traditional intrusion detection will have problems such as low detection accuracy, high false negatives, and reliance on dimensionality reduction algorithms. Therefore, it is particularly important to establish a fast and efficient network intrusion anomaly detection method to deal with the current complex network environment. This work designs a computer network intrusion detection model with a recurrent neural network in order to explore a new intrusion detection method. The main purpose of this article include the following: (1) design a network security emergency response system architecture with the recurrent neural network model. This system consists of a management center module, a knowledge database module, a data acquisition module, a risk detection tool module, a risk analysis and processing module, a data protection module, and a remote connection auxiliary module. The modules cooperate with each other to complete system functions. (2) Aiming at the risk analysis and processing module, a network intrusion detection model combining bidirectional long short-term memory (BiLSTM) and deep neural network (DNN) is designed. In view of the lack of consideration of the before-and-after relevance of intrusion data features and the multifeature problem in existing models, the use of BiLSTM to extract the relevance between features and the use of DNN to extract deeper features are proposed. Aiming at the problem that the model lacks consideration of the importance of features, it is proposed to embed an attention mechanism into the network to increase consideration for the importance of features. (3) Massive experiments have verified the reliability and effectiveness of the method proposed in this work.

Dilemma and Solution of Copyright Justice System for Health Short Video under Network Big Data Environment Monitoring.

Short videos have quickly taken over the mobile screens of network users and established themselves as one of the most significant social platforms in the “Internet+era” as a new carrier of social culture. Short films have the qualities of high participation, strong engagement, high real-time, and quick propagation, which satisfy the needs of Internet users in interpersonal communication and reading or enjoyment in leisure time. This is against the backdrop of network big data environment monitoring. Based on this, the paper defines the fair use system of short video copyright, elaborates on its concept, examines the system's challenges and potential fixes in the context of networks, and develops a model for short video repeat detection using local key points. According to the experimental findings, this algorithm's short video repetition detection accuracy rate can reach 95.8%, and its recall rate can reach 94.5%. For the development of the fair use system of short video copyright in the current network environment, the video in some complex scenes can achieve good detection performance, which establishes a good data analysis foundation and provides a significant amount of assistance for the system to escape its predicament.

Innovation of Network Political and Ideological Education in Colleges Based on Service Concept in Network Environment.

With the ongoing advancement of information technology, we must adopt the proper mindset when addressing the opportunities and difficulties that political thought education in institutions of higher learning face in the current network environment, take full advantage of the network's benefits, and take proactive steps to address the innovation of political thought education in institutions of higher education under the network environment. Based on the field of political thought education itself, this article studies the new characteristics of political thought education communication in institutions of higher learning under the network environment, analyses the problems and causes in the process of political thought education innovation in institutions of higher learning under the network environment, and explores a number of measures to improve the innovation level of political thought education under the network environment. This article describes the significance, traits, and internal connections between network media and the innovative development of political thought education in higher education institutions and proposes a cutting-edge course for the advancement of political thought education in higher education in the network era. In parallel, a system for early detection and evaluation of political thought education in higher education institutions is being built. The accuracy of this algorithm, according to experiments, can reach a high level of 95.31%, which is impressive. This study has served as a guide and a source of encouragement for institutions of higher learning to use network carriers to deliver political thought education.

A Security-Enhanced Image Communication Scheme Using Cellular Neural Network.

In the current network and big data environment, the secure transmission of digital images is facing huge challenges. The use of some methodologies in artificial intelligence to enhance its security is extremely cutting-edge and also a development trend. To this end, this paper proposes a security-enhanced image communication scheme based on cellular neural network (CNN) under cryptanalysis. First, the complex characteristics of CNN are used to create pseudorandom sequences for image encryption. Then, a plain image is sequentially confused, permuted and diffused to get the cipher image by these CNN-based sequences. Based on cryptanalysis theory, a security-enhanced algorithm structure and relevant steps are detailed. Theoretical analysis and experimental results both demonstrate its safety performance. Moreover, the structure of image cipher can effectively resist various common attacks in cryptography. Therefore, the image communication scheme based on CNN proposed in this paper is a competitive security technology method.

A spatial domain‐based color image blind watermarking scheme integrating multilevel discrete Hartley transform

In this paper, a spatial domain-based blind watermarking scheme of color images integrating multilevel discrete Hartley transform (DHT) is proposed to protect color digital images' copyright under the current 5G network environment. On the basis of the unique spatial computing property of the maximum energy coefficient of DHT and the energy aggregation principle of multilevel image transform, the color watermark image scrambled by Affine transform is directly hidden into color carrier image by quantitative techniques in the spatial domain, rather than the complex DHT domain. The specific performance of the scheme in various aspects is as follows: (1) the average value of the peak signal-to-noise ratio is higher than 40 dB; (2) the average value of structural similarity index metric remains above 0.9700; (3) the average value of normalized cross-correlation under no attack is 1.0000; (4) the average value of normalized cross-correlation. under attack is above 0.9600; (5) the total execution time is 1.1856 s; (6) the maximum embedding capacity is 0.0625 bpp; and (7) the entire key space is greater than 2432. Multiple simulation results demonstrate that the proposed scheme is not merely imperceptible and robust, but also secure and efficient, which can protect the digital copyright with effect.