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This document is a summary of a special issue of the journal Sensors, titled "Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022–2023." The special issue features selected papers from the 10th and 11th International Conferences on Green and Human Information Technology (ICGHITs), which were held in Korea and Thailand. The conferences focused on the theme of "Emerging Artificial Intelligent (AI)+X technology" and "Hyper Automation + Human AI" respectively. The selected papers cover various topics such as network security, routing protocols, signal detection, and clustering mechanisms, all incorporating AI-based methods. The issue also includes papers on topics like secure authentication, distance estimation in RFID systems, energy optimization in smart homes, blockchain technology, and radar signal detection. The authors emphasize the importance of both technology and humanity in advancing green and information technologies. [Extracted from the article]

This document is a summary of a journal article titled "Featured Papers on Network Security and Privacy." The article discusses the importance of security-by-design in networks and the need for security to be considered throughout the entire lifecycle of a network. It distinguishes between security and privacy in networks and highlights the Zero Trust approach as a means of increasing network privacy protection. The article also provides an overview of several published articles on network security and privacy, including topics such as cryptographic methods, artificial intelligence (AI) techniques, homoglyph replacement detection, privacy preservation in blockchain technology, trust models, and click fraud detection. The authors emphasize the role of AI and machine learning (ML) in improving network security and protecting network assets. They also discuss the challenges of protecting end devices and propose ML/AI algorithms for mitigating availability threats. Overall, the article highlights the importance of incorporating security and privacy measures in network design and the potential of ML/AI in enhancing network security. [Extracted from the article]

The Internet of Things (IoT), projected to exceed 30 billion active device connections globally by 2025, presents an expansive attack surface. The frequent collection and dissemination of confidential data on these devices exposes them to significant security risks, including user information theft and denial-of-service attacks. This paper introduces a smart, network-based Intrusion Detection System (IDS) designed to protect IoT networks from distributed denial-of-service attacks. Our methodology involves generating synthetic images from flow-level traffic data of the Bot-IoT and the LATAM-DDoS-IoT datasets and conducting experiments within both supervised and self-supervised learning paradigms. Self-supervised learning is identified in the state of the art as a promising solution to replace the need for massive amounts of manually labeled data, as well as providing robust generalization. Our results showcase that self-supervised learning surpassed supervised learning in terms of classification performance for certain tests. Specifically, it exceeded the F1 score of supervised learning for attack detection by 4.83% and by 14.61% in accuracy for the multiclass task of protocol classification. Drawing from extensive ablation studies presented in our research, we recommend an optimal training framework for upcoming contrastive learning experiments that emphasize visual representations in the cybersecurity realm. This training approach has enabled us to highlight the broader applicability of self-supervised learning, which, in some instances, outperformed supervised learning transferability by over 5% in precision and nearly 1% in F1 score. [ABSTRACT FROM AUTHOR]

This paper focuses on a very pertinent issue in the field of cybersecurity, particularly concerning the increased use of public Wi-Fi and the proliferation of IoT devices. The scenario, where an attacker captures user details through a malicious access point set up through a NodeMCU, is a practical illustration of a Man-in-the-Middle (MitM) attack. These attacks are especially concerning in the context of smart homes and smart cities, where security breaches can lead to severe privacy invasions and disruptions of essential services. The NodeMCU, being a low-cost, open-source IoT platform, is accessible for creating such malicious access points. It can be programmed to mimic legitimate Wi-Fi hotspots to intercept the data of unsuspecting users. This type of attack can enable unauthorized access to sensitive information such as login credentials, personal data, and even control over connected IoT devices. This approach is user-friendly and can significantly reduce the risk of users unknowingly connecting to malicious websites. The contribution of this paper includes a demonstration and analysis of a common network and IoT attack and a novel method for detecting fraudulent URLs, particularly those used in phishing attacks. By addressing these aspects, the paper makes a significant contribution to the field of cybersecurity and helps foster a more secure and resilient network and IoT ecosystem. [ABSTRACT FROM AUTHOR]

This article discusses the importance of information, network, and communication security in today's high-tech society. It highlights the increasing reliance on computer communication networks and the potential risks associated with insecure networks. The article also provides a summary of three papers accepted in a special issue on "Information, Network, and Communications Security." These papers focus on topics such as blockchain, Internet of Things (IoT), and wireless communication networks. Overall, the article emphasizes the importance of reliable security strategies in reducing economic losses and enhancing social stability and national defense security. [Extracted from the article]

With the development of 5G, the number of IoT (Internet of Things) devices connected to the Internet will grow explosively. However, due to the vulnerability of the devices, attackers can launch attacks on the vulnerable IoT devices, causing great impact on the security of the network environment. Fine-grained identification of IoT devices can help network administrators set up appropriate security policies based on the functionality and heterogeneity of the devices, while enabling timely updates and upgrades for devices with security vulnerabilities or the isolation of these dangerous devices. However, most of the existing IoT device identification methods rely on a priori knowledge or expert experience in selecting features, which cannot weigh the identification performance and labor cost. In this paper, we design a fine-grained identification method for IoT devices based on causal inference, which automatically extracts key features in the protocol fields of device communication from the perspective of causality and then classifies key features using a Stacking integrated learning method to achieve high-precision and fine-grained device identification. Through experimental verification, the proposed method achieves 96.3% and 97.7% device model identification accuracy under HTTP/TCP and SSH/TCP protocol clusters. [ABSTRACT FROM AUTHOR]

The intersection of Cybersecurity and AI has garnered increasing attention in recent years due to the growing importance of securing digital assets in an interconnected world. This bibliometric analysis aims to provide valuable insights into the research trends and developments within this interdisciplinary domain. Using data extracted from the Scopus database, a total of 501 papers were selected and analyzed to uncover key patterns and themes. The methodology involved conducting a comprehensive literature search using specific keywords related to Cybersecurity and AI applications. The initial search yielded 736 papers, which were subsequently filtered to include research articles, conference papers, editorial papers, and review papers, resulting in the final dataset of 501 papers. The analysis of publication trends revealed a remarkable surge in research output since 2015, indicating the escalating interest in this field. Collaboration patterns among researchers and institutions were analyzed through co-authorship networks, highlighting a well-connected research community that fosters knowledge exchange. Keyword analysis exposed common areas of application, such as network security, deep learning, and the Internet of Things, underscoring the importance of AI technologies in enhancing Cybersecurity measures. Furthermore, examination of the most cited documents showcased influential contributions that have shaped the trajectory of Cybersecurity and AI research. The study emphasizes the significance of Cybersecurity and AI applications research, considering the ever-increasing reliance on technology in various aspects of modern life. By integrating AI technologies, Cybersecurity measures can be fortified with automated threat detection, adaptive defense mechanisms, and proactive risk mitigation, thereby bolstering overall cybersecurity resilience. The findings of this bibliometric analysis have several implications for researchers and policymakers. Researchers can leverage the identified trends and gaps to explore new research directions and potential collaborations. Policymakers can utilize these insights to make informed decisions regarding resource allocation for research initiatives aimed at addressing emerging Cybersecurity challenges. This bibliometric analysis provides a comprehensive overview of the evolving landscape of Cybersecurity and AI applications research. It underscores the growing importance of this interdisciplinary field and its potential to reshape the future of cybersecurity. As technology continues to advance, the integration of AI in Cybersecurity will play a pivotal role in safeguarding digital assets and ensuring the secure functioning of critical systems in an increasingly interconnected world. [ABSTRACT FROM AUTHOR]

Network risk assessment should include the impact of the relationship between vulnerabilities, in order to conduct a more in-depth and comprehensive assessment of vulnerabilities and network-related risks. However, the impact of extracting the relationship between vulnerabilities mainly relies on manual processes, which are subjective and inefficient. To address these issues, this paper proposes a dual-layer knowledge representation model that combines various attributes and structural information of entities. This article first constructs a vulnerability knowledge graph and proposes a two-layer knowledge representation learning model based on it. Secondly, in order to more accurately assess the actual risk of vulnerabilities in specific networks, this paper proposes a vulnerability risk calculation model based on impact relationships, which realizes the risk assessment and ranking of vulnerabilities in specific network scenarios. Finally, based on the research on automatic prediction of the impact relationship between vulnerabilities, this paper proposes a new Bayesian attack graph network risk assessment model for inferring the possibility of device intrusion in the network. The experimental results show that the model proposed in this study outperforms traditional evaluation methods in relationship prediction tasks, demonstrating its efficiency and accuracy in complex network environments. This model achieves efficient resource utilization by simplifying training parameters and reducing the demand for computing resources. In addition, this method can quantitatively evaluate the success probability of attacking specific devices in the network topology, providing risk assessment and defense strategy support for network security managers. [ABSTRACT FROM AUTHOR]

The rise of the Internet of Things (IoT) has led to an increased risk of cyber attacks on IoT devices, including botnet attacks. Botnets are networks of compromised devices that can be controlled remotely by cybercriminals to carry out malicious activities, such as distributed denial of service (DDoS) attacks. The Mirai botnet is one of the most notorious botnets that have caused significant damage to IoT devices worldwide. To protect IoT devices from such attacks, intrusion detection systems (IDSs) are used to detect and mitigate attacks. However, the effectiveness of IDSs against botnet attacks is still a subject of debate, as botnets are becoming more sophisticated and difficult to detect. To address this issue, this research paper aims to analyse and compare the effectiveness of three widely used IDSs, namely Snort, Zeek, and Suricata, against the Mirai botnet. The study involves simulating Mirai botnet attacks in a controlled environment and evaluating the performance of each IDS in terms of detection and mitigation capabilities. The findings of this research can help organisations to select and implement effective IDSs to mitigate the impact of botnet attacks on IoT devices. By understanding the strengths and weaknesses of different IDSs, organisations can improve the security of their IoT devices and reduce the risk of cyber attacks. Overall, this research paper contributes to the existing literature on IDSs and their effectiveness against botnet attacks, highlighting the importance of selecting and implementing effective IDSs to protect IoT devices from cyber threats. [ABSTRACT FROM AUTHOR]

Awareness of Network Security Situation (abbreviated as NSS for short) technology is in a period of vigorous development recently. NSS technology means network security situational awareness technology. It refers to the technology of collecting, processing, and analyzing various real-time information in the network to understand and evaluate the current network security status. It can not only find network security threats, but also reflect the NSS in the system security metrics, and provide users with targeted security protection measures. Based on data mining methods, this paper analyzed and models perceived threats and security events with data mining algorithms, and improved information security measurement methods based on association analysis. This paper proposed network security information analysis and NSS based on data mining, and analyzed the experimental results of network awareness of NSS information security measurement. The experimental results showed that when the Timer was 8, the accuracy of the awareness of NSS information security measurement method based on data mining can reach 92.89%. The data mining model had the highest accuracy of 93.14% in situation understanding and evaluation of KDDCup-99 dataset. The results showed that the model can accurately predict the NSS. When Timer was 6, the highest accuracy of the model was 92.71%. In general, the NSS prediction mining model based on KDDCup-99 can better understand, evaluate and predict the situation. [ABSTRACT FROM AUTHOR]

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. User's data is stored in large database. The stored data can be accessed and modified by the clients over the Internet. The data is monitored by the Third Party Auditor (TPA) on behalf of the client. Therefore, integrity is lacked by the data stored on the servers. The data integrity is ensured by the cloud services that provide trust to the privacy of users. Aiming at the urgent problem of network data security in cloud computing operations, this paper proposed a security situation assessment system to grasp the security situation in real time. A set of cloud computing network data storage security is proposed. Through this model, the extraction of network data storage security situation elements, the design of network data storage security situation assessment scheme and the calculation method of network data storage security situation value are completed. The experimental results show the error of the predicted value obtained by the network data storage security situation assessment system. The effectiveness of the system model and the superiority of the improved algorithm is verified by the experimental results. This paper uses the cloud model to predict the cloud computing network security situation. On the other hand, the security situation value obtained by the situation assessment process can be used directly without training the original situation value. Performance improvement by the proposed technique over existing technique is seen and it is observe that the proposed technique is 23% and 34% better than the existing techniques. [ABSTRACT FROM AUTHOR]

Malicious encrypted traffic detection is a critical component of network security management. Previous detection methods can be categorized into two classes as follows: one is to use the feature engineering method to construct traffic features for classification and the other is to use the end‐to‐end method that directly inputs the original traffic to obtain traffic features for classification. Both of the abovementioned two methods have the problem that the obtained features cannot fully characterize the traffic. To this end, this paper proposes a hierarchical multimodal deep learning model (HMMED) for malicious encrypted traffic detection. This model adopts the abovementioned two feature generation methods to learn the features of payload and header, respectively, then fuses the features to get the final traffic features, and finally inputs the final traffic features into the softmax classifier for classification. In addition, since traditional deep learning is highly dependent on the training set size and data distribution, resulting in a model that is not very generalizable and difficult to adapt to unseen encrypted traffic, the model proposed in this paper uses a large amount of unlabeled encrypted traffic in the pretraining layer to pretrain a submodel used to obtain a generic packet payload representation. The test results on the USTC‐TFC2016 dataset show that the proposed model can effectively solve the problem of insufficient feature extraction of traditional detection methods and improve the ACC of malicious encrypted traffic detection. [ABSTRACT FROM AUTHOR]

Identifying owners of devices on the Internet can enable numerous network security applications. For example, accurate Owner Name Entity Recognition (ONER) of websites is critical to find influenced owners in light of new security threats. In this situation, as a specific task of Multimodal Named Entity Recognition (MNER), ONER is essential and helpful for network security. Currently, most existing MNER models only use texts and images, so they cannot effectively utilize the multimodal data of devices to achieve ONER accurately. Also, most of the existing MNER models separately use information in each modality and between modalities. Thus, the fusion is inconsistent, so the effect is not satisfied. Therefore, the paper proposes HDGT: A heterogeneous and Dynamic Graph Transformer, to improve the performance of ONER. The core components in HDGT to realize MNER are a dynamic graph and two-stream mechanism, which could learn the relationship between different modalities during training and the graph's structure well. The paper manually labels a multimodal dataset containing texts, images, and domains to prove the performance of HDGT. Also, the paper conducts experiments on existing and public MNER datasets. The results show that HDGT achieves 84.88% F1 scores on the recognition of owner entities, 75.21% F1 on Twitter2015, and 87.03% F1 on Twitter2017, which outperforms other existing MNER models. [ABSTRACT FROM AUTHOR]

With the development of science and technology and the popularization of e-commerce, the application of wireless network technology in the business field has gradually become a hot spot of academic research, which also brings us new topics and challenges. The secure payment system based on the improved TOTP algorithm proposed in this paper involves the WAP wireless secure payment theory and the application of mobile e-commerce. This paper first studies the WAP wireless network theory of mobile payment system, and proposes the WAP algorithm flow based on identity encryption. Then, the secure wireless execution environment of the WAP encryption payment algorithm is analyzed in depth. This paper discusses the security requirements of the WAP wireless network payment system. Based on the identity encryption algorithm, several practical algorithms are proposed, and a wireless network mobile payment model combining NTRU and ECC is constructed to improve the WAP algorithm. This paper proposes a WAP wireless network security payment system based on the improved TOTP algorithm, constructs its overall structure, and designs a core algorithm with strong adaptability, including the improved TOTP algorithm and risk assessment algorithm. Finally, the performance of the WAP e-commerce secure payment system is tested and analyzed. The research found that when the 256-bit key was used to encrypt the login information, the number of tests was 23,588, the number of concurrent threads was 200, the number of requests per second was 518, the average response time was 0.219 s, the average traffic was 500 KB/s, and the error rate was 2%. It can be seen from the data that the WAP wireless secure payment system operates normally with low network delay and can respond to operation requests quickly under the condition of concurrent access by multiple users, with strong overall pressure resistance. The work of this paper has a good reference value for the construction of the WAP wireless network payment system and the design and application of mobile e-commerce, which is conducive to further research. [ABSTRACT FROM AUTHOR]

The exploration of ecological security patterns (ESPs) can help people find those areas that are in urgent need of restoration, which is an effective way to realize ecological protection. It is of utmost significance for promoting regional sustainable development to construct ESP and put forward sub-regional optimization suggestions based on the supply and demand ratio of ecosystem services (ESs). In this paper, we assessed the level of supply and demand for five ESs based on multi-source data in 2020 with the help of InVEST, ArcGIS, and IUEMS. Based on the results of supply and demand, we calculated the supply and demand ratio of ESs and extracted the ecological source areas (ESAs) on this basis. Then, we used the Linkage Mapper tool to construct the ESP based on the principle of the minimum cumulative resistance (MCR) model and circuit theory in the Chengdu–Chongqing economic circle (CCEC). Our results indicated that there were apparent spatial differences in the supply and demand of five ESs. There were 35 ESAs in the ESP network, covering an area of about 7914 km2, and most of their land use types were woodland. The CCEC was interconnected by a network of 91 ecological corridors (ECs), spanning a total length of approximately 10,701 km. From the ECs, we extracted 29 ecological pinch points (EPPs) and 16 ecological barrier points (EBPs), which each accounted for about 0.3% of the planned area of the CCEC. Finally, we divided the ecological spaces into four types and put forward the corresponding optimization suggestions. Among them, the proportion of ecological restoration area was 7.7%, which was located in Chengdu City, northwest of the study area. The findings of this paper can give some theoretical guidance and serve as a reference for making decisions in the pursuit of ecological civilization in this region. [ABSTRACT FROM AUTHOR]

A botnet attack is a major cybersecurity threat that involves coordinated control of a network of infected computers, enabling large-scale distributed denial of service (DDoS) attacks, malware spreading, and other cybercrime activities. Proactive security measures and advanced threat intelligence systems are essential to detect and mitigate these assaults. This paper proposes the Harris Hawks Optimization (HHO) algorithm, which employs exploration and exploitation techniques to find optimal solutions for analyzing botnet attack pathways. The proposed approach involves HHO as a feature selector for extracting features from anomalous network traffic. The algorithm’s impact on botnet IP positioning performance is analyzed, considering different optimization modes and control center accuracy. The paper is organized into sections covering attack path establishment and analysis, system testing and verification, and a central leadership entity controls it [1]. Botnets are created based on the use of malicious software packages to infect important and sensitive devices in the network, thus making servers, computers, and Internet of Things devices vulnerable [2]. To detect these attacks and limit their impact requires many proactive security measures such as strong network security settings, regular software upgrades, etc. [3]. HHO is a powerful method that has the potential to solve many functional optimization problems and provides a suitable environment for engineering applications, as it mimics the exploration and exploitation phases during the foraging process of Harris Hawks [4]. A model based on HHO algorithm is proposed in this paper that has the ability to track and analyze bot attack paths by extracting a set of features during abnormal network traffic. The results were analyzed and their impact on the performance of robot networks was discussed, based on the use of different experimental results. After configuring the network topology and determining the attack path based on HHO, the performance of the algorithm and its effectiveness in preventing IP addresses from being spoofed are verified. The results showed convergence in being able to correct attack paths and effective performance in repelling the interference of fake IP addresses. [ABSTRACT FROM AUTHOR]

The integration of the Internet and communication technologies has improved our lives in numerous ways. To date, the Internet of Things has evolved the healthcare industry from healthcare 1.0 to 4.0 which is known as the MIoT. In Healthcare 4.0, data is shared among multiple stakeholders through the use of cloud computing, fog computing, and telehealth. MIoT devices and technology's interconnected, heterogeneous nature raises new concerns about access to confidential patient data, which is often done without patients' or medical staff's knowledge. This is because security and privacy concerns for MIoT devices and technology are frequently ignored or undermined by relevant stakeholders. Medical IoT security and privacy are becoming increasingly important as a result of the increasing number of security breaches targeting the MIoT in healthcare. To impartially reveal the research scenario of Security of MIoT biblioshiny and VOS viewer software are used to conduct a quantitative assessment of research papers belonging to this field for the period 2012-2021 in the Web of Science (WoS) and Scopus databases. From the results, it is seen that for the past 10 years, the sum of articles on Security of MIoT has increased. This research domain covers 35 territories. MIoT, Security, Privacy, authentication, access control are the most often used keywords in this field of research in recent years. The research flashpoints in the field primarily emphasize research directions such as the, the security and privacy concerns related to MIoT sensor data and how to get rid of them. The top five nations considering research volume are the China, India, Saudi Arabia, USA, and Korea, but a lack of collaborative work amongst these countries is seen. Finally, the paper provides future research directions on security of MIoT, such as Privacy of patient information, Lightweight Protocols for MIoT Devices, Trust management, patient data sharing and insecure networks. [ABSTRACT FROM AUTHOR]

Social robot accounts controlled by artificial intelligence or humans are active in social networks, bringing negative impacts to network security and social life. Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships, which makes it difficult to accurately describe the difference between the topological relations of nodes, resulting in low detection accuracy of social robots. This paper proposes a social robot detection method with the use of an improved neural network. First, social relationship subgraphs are constructed by leveraging the user's social network to disentangle intricate social relationships effectively. Then, a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph, thereby generating comprehensive social relation subgraph features, and the feature-wise linear modulation module of the model can better learn the differences between the nodes. Next, user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features. Finally, social robots can bemore accurately identified by combining user behavioral and relationship features. By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15, the suggested method's detection accuracies can achieve 86.73% and 97.86%, respectively. Compared with the existing mainstream approaches, the accuracy of the proposed method is 2.2% and 1.35% higher on the two datasets. The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks. [ABSTRACT FROM AUTHOR]

With the rapid development of Industrial Internet of Things (IIoT), network security issues have become increasingly severe, making intrusion detection one of the key technologies for ensuring IIoT security. However, existing intrusion detection systems face challenges such as incomplete data features, missing labels, parameter leakage, and high communication overhead. To address these challenges, this paper proposes a federated learning-based intrusion detection algorithm (NIDS-FGPA) that utilizes gradient similarity model aggregation. This algorithm leverages a federated learning architecture and combines it with Paillier homomorphic encryption technology to ensure the security of the training process. Additionally, the paper introduces the Gradient Similarity Model Aggregation (GSA) algorithm, which dynamically selects and weights updates from different models to reduce communication overhead. Finally, the paper designs a deep learning model based on two-dimensional convolutional neural networks and bidirectional gated recurrent units (2DCNN-BIGRU) to handle incomplete data features and missing labels in network traffic data. Experimental validation on the Edge-IIoTset and CIC IoT 2023 datasets achieves accuracies of 94.5% and 99.2%, respectively. The results demonstrate that the NIDS-FGPA model possesses the ability to identify and capture complex network attacks, significantly enhancing the overall security of the network. [ABSTRACT FROM AUTHOR]

The secure and normal operation of distributed networks is crucial for accurate parameter estimation. However, distributed networks are frequently susceptible to Byzantine attacks. Considering real-life scenarios, this paper investigates a probability Byzantine (PB) attack, utilizing a Bernoulli distribution to simulate the attack probability. Historically, additional detection mechanisms are used to mitigate such attacks, leading to increased energy consumption and burdens on distributed nodes, consequently diminishing operational efficiency. Differing from these approaches, an adaptive updating distributed estimation algorithm is proposed to mitigate the impact of PB attacks. In the proposed algorithm, a penalty strategy is initially incorporated during data updates to weaken the influence of the attack. Subsequently, an adaptive fusion weight is employed during data fusion to merge the estimations. Additionally, the reason why this penalty term weakens the attack has been analyzed, and the performance of the proposed algorithm is validated through simulation experiments. [ABSTRACT FROM AUTHOR]

Steganography, the use of algorithms to embed secret information in a carrier image, is widely used in the field of information transmission, but steganalysis tools built using traditional steganographic algorithms can easily identify them. Steganography without embedding (SWE) can effectively resist detection by steganography analysis tools by mapping noise onto secret information and generating secret images from secret noise. However, most SWE still have problems with the small capacity of steganographic data and the difficulty of extracting the data. Based on the above problems, this paper proposes image steganography without embedding carrier secret information. The objective of this approach is to enhance the capacity of secret information and the accuracy of secret information extraction for the purpose of improving the performance of security network communication. The proposed technique exploits the carrier characteristics to generate the carrier secret tensor, which improves the accuracy of information extraction while ensuring the accuracy of secret information extraction. Furthermore, the Wasserstein distance is employed as a constraint for the discriminator, and weight clipping is introduced to enhance the secret information capacity and extraction accuracy. Experimental results show that the proposed method can improve the data extraction accuracy by 10.03% at the capacity of 2304 bits, which verifies the effectiveness and universality of the method. The research presented here introduces a new intelligent information steganography secure communication model for secure communication in networks, which can improve the information capacity and extraction accuracy of image steganography without embedding. [ABSTRACT FROM AUTHOR]

Background: With the development of steganography technology, lawbreakers can implement covert communication in social networks more easily, exacerbating network security risks. Sterilization of image steganography methods can eliminate secret messages to block the transmission of illegal covert communication. However, existing methods overly rely on cover-stego image pairs and are unable to sanitize unknown image, which reduces stego image blocking rate in social networks. Methods: To address the above problems, this paper proposes an effective sterilization of image steganography method using self-supervised convolutional neural network (SS-Net), which does not require any prior knowledge of image steganography schemes. SS-Net includes a purification module and a refinement module. Firstly, the pixel-shuffle down-sampling in purification module is adopted to reduce the spatial correlation of pixels in the stgeo image, and improve the learning mode from supervised learning to self-supervised learning. Secondly, centrally masked convolutions and dilated convolution residual blocks are merged to eliminate secret messages and avoid image quality degradation. Finally, a refinement module is employed to improve image texture details and boundaries. Results: A series of experiments show that SS-Net from BOSSbase test sets is able to balance the destruction of secret messages with image quality, achieving 100% blocking rate of stego image. Meanwhile, our method outperforms the state-of-the-art methods in secret messages elimination ability and image quality preserving ability. [ABSTRACT FROM AUTHOR]

The security of Internet of Things (IoT) networks has become a integral problem in view of the exponential growth of IoT devices. Intrusion detection and prevention is an approach, used to identify, analyze, and block cyber threats to protect IoT from unauthorized access or attacks. This paper introduces an adaptive and incremental intrusion detection and prevention system based on RNNs, to the ever changing field of IoT security. IoT networks require advanced intrusion detection systems that can identify emerging threats because of their various and dynamic data sources. The complexity of IoT network data makes it difficult for traditional intrusion detection techniques to detect potential threats. Using the capabilities of RNNs, a model for creating and deploying an intrusion detection and prevention system (IDPS) is proposed in this paper. RNNs work particularly well for sequential data processing, which makes them an appropriate choice for IoT network traffic monitoring. NSL-KDD dataset is taken, pre-processed, features are extracted, and RNN-based model is built as a part of the proposed work. The experimental findings illustrate how effective the suggested approach is at identifying and blocking intrusions in Internet of Things networks. This paper not only demonstrates the effectiveness of RNNs in enhancing IoT network security but also opens avenues for further exploration in this burgeoning field. It presents a scalable, adaptive intrusion detection and prevention solution, responding to the evolving landscape of IoT security. As IoT networks continue to expand, the research enriches the discourse on developing resilient security strategies to combat emerging threats in scalable computing environments. [ABSTRACT FROM AUTHOR]

A new energy industry represented by photovoltaic and wind power has been developing rapidly in recent years, and its randomness and volatility will impact the stable operation of the power system. At present, it is proposed to enrich the regulation of the power grid by tapping the regulation potential of load-side resources. This paper evaluates the overall voltage regulation capability of substations under the premise of considering the impact on network voltage security and providing a theoretical basis for the participation of load-side resources of distribution networks in the regulation of the power grid. This paper proposes a Zbus linear power flow model based on Fixed-Point Power Iteration (FFPI) to enhance power flow analysis efficiency and resolve voltage sensitivity expression. Establishing the linear relationship between the voltages of PQ nodes, the voltage of the reference node, and the load power, this paper clarifies the impact of reactive power compensation devices and OLTC (on-load tap changer) tap changes on the voltages of various nodes along the feeder. It provides theoretical support for evaluating the voltage regulation range for substations. The day-ahead focus is on minimizing network losses by pre-optimizing OLTC tap positions, calculating the substation voltage regulation boundaries within the day, and simultaneously optimizing the total reactive power compensation across the entire network. By analyzing the calculated examples, it was found that a pre-scheduled OLTC (on-load tap changer) can effectively reduce network losses in the distribution grid. Compared with traditional methods, the voltage regulation range assessment method proposed in this paper can optimize the adjustment of reactive power compensation devices while ensuring the voltage safety of all nodes in the network. [ABSTRACT FROM AUTHOR]

The traditional electrical grid has to be digitally improved as digitalization and effective integration of renewable energy bring better efficiency, intelligence, and safety into the grid; hence, the transition from the traditional grid to a smart grid. A smart grid is a modernized and digitalized standard electrical infrastructure that has a key component known as the advanced metering infrastructure (AMI). AMI, also known as smart metering, is a key technological enabler of the smart grid that allows automatic collection and reporting of power-consumed data via two-way communication networks. However, the collected power consumption data is confidential; hence, its privacy must be maintained. Similarly, for the benefit of the smart grid to be consistently maximized, the AMI data and network security must always be intact despite the evolving threats and attacks targeted at it. This paper provides a comprehensive review of the existing vulnerabilities/attacks, security and privacy challenges associated with the smart metering data and network system, its open issues, and future direction. The major contributions of this review paper lie in the AMI vulnerabilities, AMI state-of-the-art security schemes with their pros and cons, its communication protocols analysis, and its emerging security measures. This gave enumerated recommendations for the efficiency improvement of AMI security in terms of its latency reduction while implementing efficient security measures in its future work. [ABSTRACT FROM AUTHOR]

Fair exchange protocols play a critical role in enabling two distrustful entities to conduct electronic data exchanges in a fair and secure manner. These protocols are widely used in electronic payment systems and electronic contract signing, ensuring the reliability and security of network transactions. In order to address the limitations of current research methods and enhance the analytical capabilities for fair exchange protocols, this paper proposes a formal model for analyzing such protocols. The proposed model begins with a thorough analysis of fair exchange protocols, followed by the formal definition of fairness. This definition accurately captures the inherent requirements of fair exchange protocols. Building upon event logic, the model incorporates the time factor into predicates and introduces knowledge set axioms. This enhancement empowers the improved logic to effectively describe the state and knowledge of protocol participants at different time points, facilitating reasoning about their acquired knowledge. To maximize the intruder's capabilities, channel errors are translated into the behaviors of the intruder. The participants are further categorized into honest participants and malicious participants, enabling a comprehensive evaluation of the intruder's potential impact. By employing a typical fair exchange protocol as an illustrative example, this paper demonstrates the detailed steps of utilizing the proposed model for protocol analysis. The entire process of protocol execution under attack scenarios is presented, shedding light on the underlying reasons for the attacks and proposing corresponding countermeasures. The developed model enhances the ability to reason about and evaluate the security properties of fair exchange protocols, thereby contributing to the advancement of secure network transactions. [ABSTRACT FROM AUTHOR]

This paper uses data mining technology to dynamically monitor tobacco Industrial Enterprise' information systems. This paper builds an Internet security situation awareness system under a big data environment. The weight clustering method is used to classify users' network behavior. The spacing of weights is optimized to ensure the maximum difference in classification. Then, NAWL-ILSTM technology establishes a security situational awareness model for the Internet environment. In this project, the extended and short-memory Nadam optimal algorithm (NAWL) is used to realize data deep learning. Finally, the tobacco industry network security situation assessment method is designed to complete the dynamic monitoring of tobacco industry network security based on data mining. Simulation results show that the proposed method can effectively improve the safety evaluation performance of the system and reduce evaluation errors. [ABSTRACT FROM AUTHOR]

Federated machine learning (Fed ML) is a new distributed machine learning technique using clients' local data applied to collaboratively train a global model without transmitting the datasets. Nodes, the participating devices in the ML training, only send parameter updates (e.g., weight updates in the case of neural networks), which are fused together by the server to build the global model without compromising raw data privacy. Fed ML guarantees its confidentiality by not divulging node data to third party, central servers. Privacy of data is a crucial network security aspect of Fed ML that will enable the technique for use in the context of data-sensitive Internet of Things (IoT) and mobile applications (including smart geo-location and smart grid infrastructure). However, most IoT and mobile devices are particularly energy constrained, which requires optimization of the Fed ML process for efficient training tasks and optimized power consumption. This paper, to the best of our knowledge, is the first Systematic Mapping Study (SMS) on Fed ML for energy constrained devices. First, we selected a total of 67 from 800 papers that satisfy our criteria, then provide a structured overview of the field using a set of carefully chosen research questions. Finally, we attempt to offer an analysis of the state-of-the-art Fed ML techniques and outline potential recommendations for the research community. [ABSTRACT FROM AUTHOR]

Cybersecurity in modern vehicles has received increased attention from the research community in recent years. Intrusion Detection Systems (IDSs) are one of the techniques used to detect and mitigate cybersecurity risks. This paper proposes a novel implementation of an IDS for in-vehicle security networks based on the concept of multi-scale histograms, which capture the frequencies of message identifiers in CAN-bus in-vehicle networks. In comparison to existing approaches in the literature based on a single histogram, the proposed approach widens the informative context used by the IDS for traffic analysis by taking into consideration sequences of two and three CAN-bus messages to create multi-scale dictionaries. The histograms are created from windows of in-vehicle network traffic. A preliminary multi-scale histogram model is created using only legitimate traffic. Against this model, the IDS performs traffic analysis to create a feature space based on the correlation of the histograms. Then, the created feature space is given in input to a Convolutional Neural Network (CNN) for the identification of the windows of traffic where the attack is present. The proposed approach has been evaluated on two different public data sets achieving a very competitive performance in comparison to the literature. [ABSTRACT FROM AUTHOR]

This paper analyzes priority-based distributed access control in vehicular networks. Characterizing the spatial correlation between vehicles, users, and roads, this paper uses Cox point processes to model vehicles and users, respectively. Then, this paper proposes that each vehicle transmits if it has the highest priority among all of its neighbors. Using the stochastic geometry framework, this paper derives the probability that a typical vehicle is retained to transmit. Then, this paper derives a second-order moment measure of the retained point process to evaluate the signal-to-interference (SIR) distribution of a typical user. The derived SIR distribution is compared to the system-level simulation of the SIR distribution of a typical user. [ABSTRACT FROM AUTHOR]

The integration of edge computing with IoT (EC-IoT) systems provides significant improvements in addressing security and privacy challenges in IoT networks. This paper examines the combination of EC-IoT and artificial intelligence (AI), highlighting practical strategies to improve data and network security. The published literature has suggested decentralized and reliable trust measurement mechanisms and security frameworks designed explicitly for IoT-enabled systems. Therefore, this paper reviews the latest attack models threatening EC-IoT systems and their impacts on IoT networks. It also examines AI-based methods to counter these security threats and evaluates their effectiveness in real-world scenarios. Finally, this survey aims to guide future research by stressing the need for scalable, adaptable, and robust security solutions to address evolving threats in EC-IoT environments, focusing on the integration of AI to enhance the privacy, security, and efficiency of IoT systems while tackling the challenges of scalability and resource limitations. [ABSTRACT FROM AUTHOR]

As blockchain technology advances, cross-chain interoperability has become a crucial requirement for achieving seamless interaction among multiple chains. To solve this problem, this paper proposes a scholarly approach to the development of a cross-chain-secure transmission technique, incorporating the Trusted Platform Module (TPM) and the SM algorithm, which is based on trusted roots. This research paper elaborates on the design and implementation of the cross-chain-secure transmission technique based on TPM and the SM algorithm, emphasizing the academic rigor necessary for addressing the security requirements of cross-chain interactions. The research shows that compared with the previous method, the throughput of production capacity is not significantly different from that before and after adding the network delay, and the success rate of preventing attacks is increased by 46.9% by simulating related attacks. After multiple rounds of experiments, the effectiveness and feasibility of this technology in protecting cross-chain communication data are verified. [ABSTRACT FROM AUTHOR]

Anomaly detection for network traffic aims to analyze the characteristics of network traffic in order to discover unknown attacks. Currently, existing detection methods have achieved promising results against high-intensity attacks that aim to interrupt the operation of the target system. In reality, attack behaviors that are commonly exhibited are highly concealed and disruptive. In addition, the attack scales are flexible and variable. In this paper, we construct a multiscale network intrusion behavior dataset, which includes three attack scales and two multiscale attack patterns based on probability distribution. Specifically, we propose a stacked ensemble learning-based detection model for anomalous traffic (or SEDAT for short) to defend against highly concealed multiscale attacks. The model employs a random forest (RF)-based method to select features and introduces multiple base learning autoencoders (AEs) to enhance the representation of multiscale attack behaviors. In addressing the challenge of a single model's inability to capture the regularities of multiscale attack behaviors, SEDAT is capable of adapting to the complex multiscale characteristics in network traffic, enabling the prediction of network access behavior. Comparative experiments demonstrate that SEDAT exhibits superior detection capabilities in multiscale network attacks. In particular, SEDAT achieves an improvement of at least 5% accuracy over baseline methods for detecting multiscale attacks. [ABSTRACT FROM AUTHOR]

Wireless network attacks are some of the most common network security threats dealt with daily. Their ease of execution and effectiveness make them commonplace within most public networks. The goal of this paper is to develop a tool which provides defenses against these attacks, one which can also generate the attacks to test its own effectiveness in defending against them. The research involved the design, testing, and implementation of attacks/defenses tool, which benefits from a user-friendly user interface that simplifies the testing process. The attacks were generated using existing tools, linked to one central interface. The defense methodology was scriptbased and created entirely from scratch. It was also linked to a single interface which continuously monitors logs to detect and prevent attacks in an efficient timely manner. The results showed that the proposed defenses to the studied wireless attacks were effective at mitigation, or outright prevention. They were also more lightweight than existing solutions, making them more appealing for less powerful hardware. [ABSTRACT FROM AUTHOR]

Honeypots are computer systems that deceive cyber attackers into believing they are ordinary computer systems designed for invasion, when in fact they are primarily designed to collect data about attack methods, resulting in better protection and defense against malicious actors. As a result, developing reliability metrics for measuring the performance, strength, and effectiveness of honeypot deception is advantageous. Despite extensive and mature research on honeynet system, reliability modeling, analysis and performance prediction and evaluation, based on copula techniques for accurately testing, estimating and optimizing the overall performance of honeynet systems remain lacking. To start, a copula approach for analyzing and optimizing the performance of honeynet systems was proposed. Any honeynet system’s performance can be classified based on its availability, dependability and profit generated. As a result, the current paper sought to investigate the performance of a multi-state honeynet system in terms of availability, dependability and expected profit. This paper examines two types of repairs. Type I repairs are known as general repairs and they are used to recover from a partial or non-lethal failure to a perfect state, whereas Type II repairs are known as copula repairs they are used to recover from a complete or lethal failure to a perfect state. For the sake of generality, the supplementary variable technique and Laplace transforms were used to develop the performance models that are essential to this research, such as availability, reliability, mean time to failure (MTTF), sensitivity and profit function. The models’ numerical validation was fully carried out. The results are shown in tables and figures, enabling us to draw the conclusion that Type II repair is a superior repair policy. Type II repair, according to the findings, can more accurately portray system structure and states while still allowing for efficient assessment. [ABSTRACT FROM AUTHOR]

This paper predicts the network security posture of an ICS, focusing on the reliability of Industrial Control Systems (ICSs). Evidence reasoning (ER) and belief rule base (BRB) techniques are employed to establish an ICS network security posture prediction model, ensuring the secure operation and prediction of the ICS. This model first integrates various information from the ICS to determine its network security posture value. Subsequently, through ER iteration, information fusion occurs and serves as an input for the BRB prediction model, which necessitates initial parameter setting by relevant experts. External factors may influence the experts' predictions; therefore, this paper proposes the Projection Equalization Optimization (P-EO) algorithm. This optimization algorithm updates the initial parameters to enhance the prediction of the ICS network security posture through the model. Finally, industrial datasets are used as experimental data to improve the credibility of the prediction experiments and validate the model's predictive performance in the ICS. Compared with other methods, this paper's prediction model demonstrates a superior prediction accuracy. By further comparing with other algorithms, this paper has a certain advantage when using less historical data to make predictions. [ABSTRACT FROM AUTHOR]

From the point of view of signal camouflage, this paper proposes an active network defense system. Then, the optimal camouflage target selection scheme is proposed. Its goal is to solve the problems existing in the information construction of Chinese military equipment support. This method establishes a multistage game model based on weapon support information network attack and defense. The attack and defense benefits are quantitatively calculated based on the cost of signal concealment. The solution to the refined Bayesian balance problem is given. Then, a multistage optimization algorithm for camouflage signal selection is proposed. Finally, experimental research proves the proposed algorithm to be reasonable and practical. [ABSTRACT FROM AUTHOR]

As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. If we regard these distributed network devices and connection relationships as a complex network, we can identify the influential nodes to find the crucial points for optimizing the imbalance of the reliability of devices in federated learning system. This paper will analyze the advantages and disadvantages of existing algorithms for identifying influential nodes in complex networks, and propose a method from the perspective of information dissemination for finding influential nodes based on Kullback–Leibler divergence model within the neighborhood (KLN). Firstly, the KLN algorithm removes a node to simulate the scenario of node failure in the information dissemination process. Secondly, KLN evaluates the loss of information entropy within the neighborhood after node removal by establishing the KL divergence model. Finally, it assesses the damage influence of the removed node by integrating the network attributes and KL divergence model, thus achieving the evaluation of node importance. To validate the performance of KLN, this paper conducts an analysis and comparison of its results with those of 11 other algorithms on 10 networks, using SIR model as a reference. Additionally, a case study was undertaken on a real epidemic propagation network, leading to the proposal of management and control strategies for daily protection based on the influential nodes. The experimental results indicate that KLN effectively evaluates the importance of the removed node using KL model within the neighborhood, and demonstrate better accuracy and applicability across networks of different scales. [ABSTRACT FROM AUTHOR]

The rapid development of 5G networks and the anticipation of 6G technologies have ushered in an era of highly customizable network environments facilitated by the innovative concept of network slicing. This technology allows the creation of multiple virtual networks on the same physical infrastructure, each optimized for specific service requirements. Despite its numerous benefits, network slicing introduces significant security vulnerabilities that must be addressed to prevent exploitation by increasingly sophisticated cyber threats. This review explores the application of cutting-edge technologies—Artificial Intelligence (AI), specifically Machine Learning (ML), Software-Defined Networking (SDN), and Network Functions Virtualization (NFV)—in crafting advanced security solutions tailored for network slicing. AI's predictive threat detection and automated response capabilities are analysed, highlighting its role in maintaining service integrity and resilience. Meanwhile, SDN and NFV are scrutinized for their ability to enforce flexible security policies and manage network functionalities dynamically, thereby enhancing the adaptability of security measures to meet evolving network demands. Thoroughly examining the current literature and industry practices, this paper identifies critical research gaps in security frameworks and proposes innovative solutions. We advocate for a holistic security strategy integrating ML, SDN, and NFV to enhance data confidentiality, integrity, and availability across network slices. The paper concludes with future research directions to develop robust, scalable, and efficient security frameworks capable of supporting the safe deployment of network slicing in next-generation networks. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication technology, positioning technology, and modern smart devices, Internet of Vehicles (IoVs) smart vehicles have brought great convenience to human production and life. Meanwhile, privacy and security issues are becoming extremely serious, with serious consequences if sensitive data such as vehicle location and trip patterns are leaked. This paper focuses on the demands for vehicular network security, especially privacy protection and existing privacy-protection techniques, including common cryptography methods and cryptography-based advanced technologies. At the same time, this paper also analyzes the advantages and challenges of these technologies in protecting privacy and network security in the Internet of Vehicles, such as the challenges of computational resource requirements and security efficiency in the implementation process, as well as the complexity of realizing effective privacy protection in the interactions among different entities. Finally, this paper envisions the development of privacy-preserving application scenarios and the prospects for crypotography-based privacy-preserving technologies. [ABSTRACT FROM AUTHOR]

The swift explosion of Internet of Things (IoT) devices has brought about a new era of interconnectivity and ease of use while simultaneously presenting significant security concerns. Intrusion Detection Systems (IDS) play a critical role in the protection of IoT ecosystems against a wide range of cyber threats. Despite research advancements, challenges persist in improving IDS detection accuracy, reducing false positives (FPs), and identifying new types of attacks. This paper presents a comprehensive analysis of recent developments in IoT, shedding light on detection methodologies, threat types, performance metrics, datasets, challenges, and future directions. We systematically analyze the existing literature from 2016 to 2023, focusing on both machine learning (ML) and non-ML IDS strategies involving signature, anomaly, specification, and hybrid models to counteract IoT-specific threats. The findings include the deployment models from edge to cloud computing and evaluating IDS performance based on measures such as accuracy, FP rates, and computational costs, utilizing various IoT benchmark datasets. The study also explores methods to enhance IDS accuracy and efficiency, including feature engineering, optimization, and cutting-edge solutions such as cryptographic and blockchain technologies. Equally, it identifies key challenges such as the resource-constrained nature of IoT devices, scalability, and privacy issues and proposes future research directions to enhance IoT-based IDS and overall ecosystem security. [ABSTRACT FROM AUTHOR]

As the global data strategy deepens and data elements accelerate integrating and flowing more rapidly, the demand for data security and privacy protection has become increasingly prominent. Confidential computing emerges as a crucial security technology to solve security and privacy problem, and it is also a hot subject of in contemporary security technologies. Leveraging collaborative security in both hardware and software, it builds a trusted execution environment to ensure confidentiality and integrity protection for data in use. This paper provides a comprehensive overview of the development process of confidential computing, summarizing its current research status and issues, which focuses on the security requirements for data security and privacy protection. Furthermore, it deeply analyses the common technical features of confidential computing, and proposes a trusted confidential computing architecture based on collaborative hardware and software trust. Then, it elaborates on the research status and issues of confidential computing from four aspects: hardware security, architecture and key technologies, applications, and standards and evaluation. Finally, this paper provides a synthesis and outlook for the future development of confidential computing. In summary, confidential computing is currently in a rapidly developing stage and will play an important role in cyber security in the future. [ABSTRACT FROM AUTHOR]

Flying Ad hoc Networks have emerged as a promising technology for number of real-time applications. However, the flexible and unstructured characteristics of these networks make them vulnerable to security threats posed by malicious nodes, such as denial of service attacks, node impersonation, and information breaches. Another major issue is the consideration of those nodes being unable to prove their trustworthiness due to factors like hardware or software failure, or by link interruptions, during the processing of detection of false nodes in the network. The existing mechanisms encompassing encryption, authentication, and intrusion detection highlight limitations to secure real-time applications and services due to the high speed of flying nodes and the absence of fixed network structures. To overcome these constraints, this research paper incorporates a novel framework for evaluating and improving the security of network by introducing an innovative cluster-based approach. Moreover, it presents a fuzzy model that dynamically estimates the trust levels of both individual nodes and clusters, by assigning weight to the parameters to address vulnerabilities. Additionally, a trust reconfiguration mechanism is further proposed to address the issue of nodes unable to substantiate their trust by providing them with additional chances based on the collective trust from previous evaluations. Further, the paper incorporates a dynamic reputation system to proficiently identify and separate malicious and selfish nodes from the network. Simulation results indicate a significant improvement in performance metrics, with a considerable reduction in delay and drop ratio by 41.46% and 36.37%, respectively, while the sufficient rise of 54.71% and 46.05% in throughput and coverage, respectively, comparing with the considered state-of-art. [ABSTRACT FROM AUTHOR]

The Act of 5 July 2018 on the National Cybersecurity System and its accompanying executive regulations have introduced into Polish law the provisions of the Directive of the European Parliament and of the Council of 6 July 2016 concerning measures for a high common level of security of network and information systems across the Union (UE) 2016/1148. The fundamental reason for these regulations was to establish a coherent system to ensure the cyber security of the Republic of Poland with accordance to standards adopted for European Union Member States. This paper presents the legal situation of operators of essential services and digital service providers that was created by the provisions of the ANCS. The ANCS not only identifies operators of essential services, digital service providers, and their assigned obligations, but also addresses the competent authorities' tasks of supervising, inspecting and imposing penalties within the cyber security system. The findings, assessments and conclusions presented here are based on the interpretation of the provisions of the ANCS and are supported by prominent claims of academic representatives. The analyses contained within this paper aim to show that despite the comprehensible and contemporary ratio legis -- which falls within the framework of pursuing the state of digital safety -- the provisions of the ANCS require adjustments that acknowledge the legal situation of operators of essential services and digital service providers. [ABSTRACT FROM AUTHOR]

Along with the progression of Internet of Things (IoT) technology, network terminals are becoming continuously more intelligent. IoT has been widely applied in various scenarios, including urban infrastructure, transportation, industry, personal life, and other socio-economic fields. The introduction of deep learning has brought new security challenges, like an increment in abnormal traffic, which threatens network security. Insufficient feature extraction leads to less accurate classification results. In abnormal traffic detection, the data of network traffic is high-dimensional and complex. This data not only increases the computational burden of model training but also makes information extraction more difficult. To address these issues, this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection. To fully utilize the multi-scale information in network traffic, a Multi-scale Dilated feature extraction (MD) block is introduced. This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model's receptive field. The proposed Max-feature-map Residual with Dual-channel pooling (MRD) block integrates the maximum feature map with the residual block. This module ensures the model focuses on key information, thereby optimizing computational efficiency and reducing unnecessary information redundancy. Experimental results show that compared to the latest methods, the proposed abnormal traffic detection model improves accuracy by about 2%. [ABSTRACT FROM AUTHOR]

Artificial intelligence-generated content (AIGC) technology has had disruptive results in AI, representing a new trend in research and application and promoting a new era of AI. The potential benefits of this technology are both profound and diverse. However, the benefits of generative tools are accompanied by a series of significant challenges, the most critical of which is that it may cause AI information pollution on social media and mislead the public. Traditional network security models have shown their limitations in dealing with today's complex network threats, so ensuring that generated content published on social media accurately reflects the true intentions of content creators has become particularly important. This paper proposes a security framework called "secToken". The framework adopts multi-level security and privacy protection measures. It combines deep learning and network security technology to ensure users' data integrity and confidentiality while ensuring credibility of the published content. In addition, the framework introduces the concept of zero trust security, integrates OAuth2.0 ideas, and provides advanced identity authentication, fine-grained access control, continuous identity verification, and other functions, to comprehensively guarantee the published content's reliability on social media. This paper considers the main issues of generative content management in social media and offers some feasible solutions. Applying the security framework proposed in this paper, the credibility of generated content published on social media can be effectively ensured and can help detect and audit published content on social media. At the operational level, when extracting key information summaries from user-generated multimodal artificial intelligence-generated content and binding them to user identity information as a new token to identify user uniqueness, it can effectively associate user identity information with the current network status and the generated content to be published on the platform. This method significantly enhances system security and effectively prevents information pollution caused by generative artificial intelligence on social media platforms. This innovative method provides a powerful solution for addressing social and ethical challenges and network security issues. [ABSTRACT FROM AUTHOR]

The complexity of marine information types, data diversity, data collection difficulties and other aspects makes the network security of marine information management more and more prominent, and has become a major issue affecting the stability of the country and society, so it is urgent to establish a marine information management network security system. Traditional network security technology adopts a passive approach and cannot actively detect viruses, trojans, and other hidden objects in the network. Antivirus software would only be used when attacked. If the risk of network attack is too great, the consequences would be unimaginable. This paper designed a marine information management network security system based on artificial intelligence embedded technology, which improved the efficiency of marine information security management. This paper also applied the embedded technology of AI to the network security management, and proposed the k-means clustering algorithm (K-Means) of AI, which can greatly improve the network security. The experimental results in this paper showed that the intrusion detection rates of System 1 and System 2 were 56.3% and 78.3% respectively when the number of viruses was 50 at 30M, and 65.5% and 80.1% respectively when the number of viruses was 50 at 60M. It showed that the intrusion detection rate of System 2 was higher both at 30M and 60M. [ABSTRACT FROM AUTHOR]

This survey paper provides a comprehensive overview of recent research and development in network security that uses graphs and graph-based data representation and analytics. The paper focuses on the graph-based representation of network traffic records and the application of graph-based analytics in intrusion detection and botnet detection. The paper aims to answer several questions related to graph-based approaches in network security, including the types of graphs used to represent network security data, the approaches used to analyze such graphs, the metrics used for detection and monitoring, and the reproducibility of existing works. The paper presents a survey of graph models used to represent, store, and visualize network security data, a survey of the algorithms and approaches used to analyze such data, and an enumeration of the most important graph features used for network security analytics for monitoring and botnet detection. The paper also discusses the challenges and limitations of using graph-based approaches in network security and identifies potential future research directions. Overall, this survey paper provides a valuable resource for researchers and practitioners in the field of network security who are interested in using graph-based approaches for analyzing and detecting malicious activities in networks. [ABSTRACT FROM AUTHOR]