Sixth generation (6G)-enabled massive network MANO orchestration, alongside distributed supervision and fully reconfigurable control logic that manages dynamic arrangement of network components, such as cell-free, Open-Air Interface (OAI) and RIS, is a potent enabler for the upcoming pervasive digitalization of the vertical use cases. In such a disruptive domain, artificial intelligence (AI)-driven zero-touch “Network of Networks” intent-based automation shall be able to guarantee a high degree of security, efficiency, scalability, and sustainability, especially in cross-domain and interoperable deployment environments (i.e., where points of presence (PoPs) are non-independent and identically distributed (non-IID)). To this extent, this paper presents a novel breakthrough, open, and fully reconfigurable networking architecture for 6G cellular paradigms, named 6G-BRICKS. To this end, 6G-BRICKS will deliver the first open and programmable O-RAN Radio Unit (RU) for 6G networks, termed as the OpenRU, based on an NI USRP-based platform. Moreover, 6G-BRICKS will integrate the RIS concept into the OAI alongside Testing as a Service (TaaS) capabilities, multi-tenancy, disaggregated Operations Support Systems (OSS) and Deep Edge adaptation at the forefront. The overall ambition of 6G-BRICKS is to offer evolvability, granularity, while, at the same time, tackling big challenges such as interdisciplinary efforts and big investments in 6G integration.

The fixed security solutions and related security configurations may no longer meet the diverse requirements of 6G networks. Open Radio Access Network (O-RAN) architecture is going to be one key entry point to 6G where the direct user access is granted. O-RAN promotes the design, deployment and operation of the RAN with open interfaces and optimized by intelligent controllers. O-RAN networks are to be implemented as multi-vendor systems with interoperable components and can be programmatically optimized through centralized abstraction layer and data driven closed-loop control. However, since O-RAN contains many new open interfaces and data flows, new security issues may emerge. Providing the recommendations for dynamic security policy adjustments by considering the energy availability and risk or security level of the network is something lacking in the current state-of-the-art. When the security process is managed and executed in an autonomous way, it must also assure the transparency of the security policy adjustments and provide the reasoning behind the adjustment decisions to the interested parties whenever needed. Moreover, the energy consumption for such security solutions are constantly bringing overhead to the networking devices. Therefore, in this paper we discuss XAI based green security architecture for resilient open radio access networks in 6G known as XcARet for providing cognitive and transparent security solutions for O-RAN in a more energy efficient manner.

The fixed security solutions and related security configurations may no longer meet the diverse requirements of 6G networks. Open Radio Access Network (O-RAN) architecture is going to be one key entry point to 6G where the direct user access is granted. O-RAN promotes the design, deployment and operation of the RAN with open interfaces and optimized by intelligent controllers. O-RAN networks are to be implemented as multi-vendor systems with interoperable components and can be programmatically optimized through centralized abstraction layer and data driven closed-loop control. However, since O-RAN contains many new open interfaces and data flows, new security issues may emerge. Providing the recommendations for dynamic security policy adjustments by considering the energy availability and risk or security level of the network is something lacking in the current state-of-the-art. When the security process is managed and executed in an autonomous way, it must also assure the transparency of the security policy adjustments and provide the reasoning behind the adjustment decisions to the interested parties whenever needed. Moreover, the energy consumption for such security solutions are constantly bringing overhead to the networking devices. Therefore, in this paper we discuss XAI based green security architecture for resilient open radio access networks in 6G known as XcARet for providing cognitive and transparent security solutions for O-RAN in a more energy efficient manner.

In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures. This study highlights the necessity of implementing a Zero Trust Architecture (ZTA) to enhance the security of unmanned aerial vehicles (UAVs), hence departing from conventional perimeter defences that may expose vulnerabilities. The Zero Trust Architecture (ZTA) paradigm requires a rigorous and continuous process of authenticating all network entities and communications. The accuracy of our methodology in detecting and identifying unmanned aerial vehicles (UAVs) is 84.59\%. This is achieved by utilizing Radio Frequency (RF) signals within a Deep Learning framework, a unique method. Precise identification is crucial in Zero Trust Architecture (ZTA), as it determines network access. In addition, the use of eXplainable Artificial Intelligence (XAI) tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) contributes to the improvement of the model s transparency and interpretability. Adherence to Zero Trust Architecture (ZTA) standards guarantees that the classifications of unmanned aerial vehicles (UAVs) are verifiable and comprehensible, enhancing security within the UAV field.

Deep learning models are being utilized and further developed in many application domains, but challenges still exist regarding their interpretability and consistency. Interpretability is important to provide users with transparent information that enhances the trust between the user and the learning model. It also gives developers feedback to improve the consistency of their deep learning models. In this paper, we present a novel architectural design to embed interpretation into the architecture of the deep learning model. We apply dynamic pixel-wised weights to input images and produce a highly correlated feature map for classification. This feature map is useful for providing interpretation and transparent information about the decision-making of the deep learning model while keeping full context about the relevant feature information compared to previous interpretation algorithms. The proposed model achieved 92\% accuracy for CIFAR 10 classifications without finetuning the hyperparameters. Furthermore, it achieved a 20\% accuracy under 8/255 PGD adversarial attack for 100 iterations without any defense method, indicating extra natural robustness compared to other Convolutional Neural Network (CNN) models. The results demonstrate the feasibility of the proposed architecture.

In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures. This study highlights the necessity of implementing a Zero Trust Architecture (ZTA) to enhance the security of unmanned aerial vehicles (UAVs), hence departing from conventional perimeter defences that may expose vulnerabilities. The Zero Trust Architecture (ZTA) paradigm requires a rigorous and continuous process of authenticating all network entities and communications. The accuracy of our methodology in detecting and identifying unmanned aerial vehicles (UAVs) is 84.59\%. This is achieved by utilizing Radio Frequency (RF) signals within a Deep Learning framework, a unique method. Precise identification is crucial in Zero Trust Architecture (ZTA), as it determines network access. In addition, the use of eXplainable Artificial Intelligence (XAI) tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) contributes to the improvement of the model s transparency and interpretability. Adherence to Zero Trust Architecture (ZTA) standards guarantees that the classifications of unmanned aerial vehicles (UAVs) are verifiable and comprehensible, enhancing security within the UAV field.

The fixed security solutions and related security configurations may no longer meet the diverse requirements of 6G networks. Open Radio Access Network (O-RAN) architecture is going to be one key entry point to 6G where the direct user access is granted. O-RAN promotes the design, deployment and operation of the RAN with open interfaces and optimized by intelligent controllers. O-RAN networks are to be implemented as multi-vendor systems with interoperable components and can be programmatically optimized through centralized abstraction layer and data driven closed-loop control. However, since O-RAN contains many new open interfaces and data flows, new security issues may emerge. Providing the recommendations for dynamic security policy adjustments by considering the energy availability and risk or security level of the network is something lacking in the current state-of-the-art. When the security process is managed and executed in an autonomous way, it must also assure the transparency of the security policy adjustments and provide the reasoning behind the adjustment decisions to the interested parties whenever needed. Moreover, the energy consumption for such security solutions are constantly bringing overhead to the networking devices. Therefore, in this paper we discuss XAI based green security architecture for resilient open radio access networks in 6G known as XcARet for providing cognitive and transparent security solutions for O-RAN in a more energy efficient manner.

6G networks are beginning to take shape, and it is envisaged that they will be made up of networks from different vendors, and with different technologies, in what is known as the network-of-networks. The topology will be constantly changing, allowing it to adapt to the capacities available at any given moment. 6G networks will be managed automatically and natively by AI, but allowing direct management of learning by technical teams through Explainable AI. In this context, security becomes an unprecedented challenge. In this paper we present a flexible architecture that integrates the necessary modules to respond to the needs of 6G, focused on managing security, network and services through choreography intents that coordinate the capabilities of different stakeholders to offer advanced services.

In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures. This study highlights the necessity of implementing a Zero Trust Architecture (ZTA) to enhance the security of unmanned aerial vehicles (UAVs), hence departing from conventional perimeter defences that may expose vulnerabilities. The Zero Trust Architecture (ZTA) paradigm requires a rigorous and continuous process of authenticating all network entities and communications. The accuracy of our methodology in detecting and identifying unmanned aerial vehicles (UAVs) is 84.59\%. This is achieved by utilizing Radio Frequency (RF) signals within a Deep Learning framework, a unique method. Precise identification is crucial in Zero Trust Architecture (ZTA), as it determines network access. In addition, the use of eXplainable Artificial Intelligence (XAI) tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) contributes to the improvement of the model s transparency and interpretability. Adherence to Zero Trust Architecture (ZTA) standards guarantees that the classifications of unmanned aerial vehicles (UAVs) are verifiable and comprehensible, enhancing security within the UAV field.

With the continuous enrichment of intelligent applications, it is anticipated that 6G will evolve into a ubiquitous intelligent network. In order to achieve the vision of full-scenarios intelligent services, how to collaborate AI capabilities in different domains is an urgent issue. After analyzing potential use cases and technological requirements, this paper proposes an endto-end (E2E) cross-domain artificial intelligence (AI) collaboration framework for next-generation mobile communication systems. Two potential technical solutions, namely cross-domain AI management and orchestration and RAN-CN convergence, are presented to facilitate intelligent collaboration in both E2E scenarios and the edge network. Furthermore, we have validated the performance of a cross-domain federated learning algorithm in a simulated environment for the prediction of received signal power. While ensuring the security and privacy of terminal data, we have analyzed the communication overhead caused by cross-domain training.

In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures. This study highlights the necessity of implementing a Zero Trust Architecture (ZTA) to enhance the security of unmanned aerial vehicles (UAVs), hence departing from conventional perimeter defences that may expose vulnerabilities. The Zero Trust Architecture (ZTA) paradigm requires a rigorous and continuous process of authenticating all network entities and communications. The accuracy of our methodology in detecting and identifying unmanned aerial vehicles (UAVs) is 84.59\%. This is achieved by utilizing Radio Frequency (RF) signals within a Deep Learning framework, a unique method. Precise identification is crucial in Zero Trust Architecture (ZTA), as it determines network access. In addition, the use of eXplainable Artificial Intelligence (XAI) tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) contributes to the improvement of the model s transparency and interpretability. Adherence to Zero Trust Architecture (ZTA) standards guarantees that the classifications of unmanned aerial vehicles (UAVs) are verifiable and comprehensible, enhancing security within the UAV field.

6G networks are beginning to take shape, and it is envisaged that they will be made up of networks from different vendors, and with different technologies, in what is known as the network-of-networks. The topology will be constantly changing, allowing it to adapt to the capacities available at any given moment. 6G networks will be managed automatically and natively by AI, but allowing direct management of learning by technical teams through Explainable AI. In this context, security becomes an unprecedented challenge. In this paper we present a flexible architecture that integrates the necessary modules to respond to the needs of 6G, focused on managing security, network and services through choreography intents that coordinate the capabilities of different stakeholders to offer advanced services.

With the rapid development of cloud computing services and big data applications, the number of data centers is proliferating, and with it, the problem of energy consumption in data centers is becoming more and more serious. Data center energy-saving has received more and more attention as a way to reduce carbon emissions and power costs. The main energy consumption of data centers lies in IT equipment energy consumption and end air conditioning energy consumption. In this paper, we propose a data center energy-saving application system based on fog computing architecture to reduce air conditioning energy consumption, and thus reduce data center energy consumption. Specifically, the intelligent module is placed in the fog node to take advantage of the low latency, proximal computing, and proximal storage of fog computing to shorten the network call link and improve the stability of acquiring energy-saving policies and the frequency of energy-saving regulation, thus solving the disadvantages of high latency and instability in the energy-saving approach of cloud computing architecture. The AI technology is used in the intelligent module to generate energy-saving strategies and remotely regulate the end air conditioners to achieve better energy-saving effects. This solves the shortcomings of the traditional manual regulation based on expert experience with low adjustment frequency and serious loss of cooling capacity of the terminal air conditioner. According to the experimental results, statistics show that compared with the traditional manual regulation based on expert experience, the data center energy-saving application based on fog computing can operate safely and efficiently, and reduce the PUE to 1.04. Compared with the AI energy-saving strategy based on cloud computing, the AI energy-saving strategy based on fog computing generates strategies faster and with lower latency, and the speed is increased by 29.84\%.

AI systems face potential hardware security threats. Existing AI systems generally use the heterogeneous architecture of CPU + Intelligent Accelerator, with PCIe bus for communication between them. Security mechanisms are implemented on CPUs based on the hardware security isolation architecture. But the conventional hardware security isolation architecture does not include the intelligent accelerator on the PCIe bus. Therefore, from the perspective of hardware security, data offloaded to the intelligent accelerator face great security risks. In order to effectively integrate intelligent accelerator into the CPU’s security mechanism, a novel hardware security isolation architecture is presented in this paper. The PCIe protocol is extended to be security-aware by adding security information packaging and unpacking logic in the PCIe controller. The hardware resources on the intelligent accelerator are isolated in fine-grained. The resources classified into the secure world can only be controlled and used by the software of CPU’s trusted execution environment. Based on the above hardware security isolation architecture, a security isolation spiking convolutional neural network accelerator is designed and implemented in this paper. The experimental results demonstrate that the proposed security isolation architecture has no overhead on the bandwidth and latency of the PCIe controller. The architecture does not affect the performance of the entire hardware computing process from CPU data offloading, intelligent accelerator computing, to data returning to CPU. With low hardware overhead, this security isolation architecture achieves effective isolation and protection of input data, model, and output data. And this architecture can effectively integrate hardware resources of intelligent accelerator into CPU’s security isolation mechanism.

Edge computing enables the computation and analytics capabilities to be brought closer to data sources. The available literature on AI solutions for edge computing primarily addresses just two edge layers. The upper layer can directly communicate with the cloud and comprises one or more IoT edge devices that gather sensing data from IoT devices present in the lower layer. However, industries mainly adopt a multi-layered architecture, referred to as the ISA-95 standard, to isolate and safeguard their assets. In this architecture, only the upper layer is connected to the cloud, while the lower layers of the hierarchy get to interact only with the neighbouring layers. Due to these added intermediate layers (and IoT edge devices) between the top and lower layers, existing AI solutions for typical two-layer edge architectures may not be directly applicable in this scenario. Moreover, not all industries prefer to send and store their private data in the cloud. Implementing AI solutions tailored to a hierarchical edge architecture would increase response time and maintain the same degree of security by working within the ISA-95-compliant network architecture. This paper explores a possible strategy for deploying a centralized federated learning-based AI solution in a hierarchical edge architecture and demonstrates its efficacy through a real deployment scenario.

As a result of globalization, the COVID-19 pandemic and the migration of data to the cloud, the traditional security measures where an organization relies on a security perimeter and firewalls do not work. There is a shift to a concept whereby resources are not being trusted, and a zero-trust architecture (ZTA) based on a zero-trust principle is needed. Adapting zero trust principles to networks ensures that a single insecure Application Protocol Interface (API) does not become the weakest link comprising of Critical Data, Assets, Application and Services (DAAS). The purpose of this paper is to review the use of zero trust in the security of a network architecture instead of a traditional perimeter. Different software solutions for implementing secure access to applications and services for remote users using zero trust network access (ZTNA) is also summarized. A summary of the author s research on the qualitative study of “Insecure Application Programming Interface in Zero Trust Networks” is also discussed. The study showed that there is an increased usage of zero trust in securing networks and protecting organizations from malicious cyber-attacks. The research also indicates that APIs are insecure in zero trust environments and most organization are not aware of their presence.

In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures. This study highlights the necessity of implementing a Zero Trust Architecture (ZTA) to enhance the security of unmanned aerial vehicles (UAVs), hence departing from conventional perimeter defences that may expose vulnerabilities. The Zero Trust Architecture (ZTA) paradigm requires a rigorous and continuous process of authenticating all network entities and communications. The accuracy of our methodology in detecting and identifying unmanned aerial vehicles (UAVs) is 84.59\%. This is achieved by utilizing Radio Frequency (RF) signals within a Deep Learning framework, a unique method. Precise identification is crucial in Zero Trust Architecture (ZTA), as it determines network access. In addition, the use of eXplainable Artificial Intelligence (XAI) tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) contributes to the improvement of the model s transparency and interpretability. Adherence to Zero Trust Architecture (ZTA) standards guarantees that the classifications of unmanned aerial vehicles (UAVs) are verifiable and comprehensible, enhancing security within the UAV field.

Modern network defense can benefit from the use of autonomous systems, offloading tedious and time-consuming work to agents with standard and learning-enabled components. These agents, operating on critical network infrastructure, need to be robust and trustworthy to ensure defense against adaptive cyber-attackers and, simultaneously, provide explanations for their actions and network activity. However, learning-enabled components typically use models, such as deep neural networks, that are not transparent in their high-level decision-making leading to assurance challenges. Additionally, cyber-defense agents must execute complex long-term defense tasks in a reactive manner that involve coordination of multiple interdependent subtasks. Behavior trees are known to be successful in modelling interpretable, reactive, and modular agent policies with learning-enabled components. In this paper, we develop an approach to design autonomous cyber defense agents using behavior trees with learning-enabled components, which we refer to as Evolving Behavior Trees (EBTs). We learn the structure of an EBT with a novel abstract cyber environment and optimize learning-enabled components for deployment. The learning-enabled components are optimized for adapting to various cyber-attacks and deploying security mechanisms. The learned EBT structure is evaluated in a simulated cyber environment, where it effectively mitigates threats and enhances network visibility. For deployment, we develop a software architecture for evaluating EBT-based agents in computer network defense scenarios. Our results demonstrate that the EBT-based agent is robust to adaptive cyber-attacks and provides high-level explanations for interpreting its decisions and actions.

In the ever-evolving landscape of cybersecurity threats, Intrusion detection systems are critical in protecting network and server infrastructure in the ever-changing spectrum of cybersecurity threats. This research introduces a hybrid detection approach that uses deep learning techniques to improve intrusion detection accuracy and efficiency. The proposed prototype combines the strength of the XGBoost and MaxPooling1D algorithms within an ensemble model, resulting in a stable and effective solution. Through the fusion of these methodologies, the hybrid detection system achieves superior performance in identifying and mitigating various types of intrusions. This paper provides an overview of the prototype s architecture, discusses the benefits of using deep learning in intrusion detection, and presents experimental results showcasing the system s efficacy.

In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures. This study highlights the necessity of implementing a Zero Trust Architecture (ZTA) to enhance the security of unmanned aerial vehicles (UAVs), hence departing from conventional perimeter defences that may expose vulnerabilities. The Zero Trust Architecture (ZTA) paradigm requires a rigorous and continuous process of authenticating all network entities and communications. The accuracy of our methodology in detecting and identifying unmanned aerial vehicles (UAVs) is 84.59\%. This is achieved by utilizing Radio Frequency (RF) signals within a Deep Learning framework, a unique method. Precise identification is crucial in Zero Trust Architecture (ZTA), as it determines network access. In addition, the use of eXplainable Artificial Intelligence (XAI) tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) contributes to the improvement of the model s transparency and interpretability. Adherence to Zero Trust Architecture (ZTA) standards guarantees that the classifications of unmanned aerial vehicles (UAVs) are verifiable and comprehensible, enhancing security within the UAV field.

Zero Day Threats (ZDT) are novel methods used by malicious actors to attack and exploit information technology (IT) networks or infrastructure. In the past few years, the number of these threats has been increasing at an alarming rate and have been costing organizations millions of dollars to remediate. The increasing expansion of network attack surfaces and the exponentially growing number of assets on these networks necessitate the need for a robust AI-based Zero Day Threat detection model that can quickly analyze petabyte-scale data for potentially malicious and novel activity. In this paper, the authors introduce a deep learning based approach to Zero Day Threat detection that can generalize, scale, and effectively identify threats in near real-time. The methodology utilizes network flow telemetry augmented with asset-level graph features, which are passed through a dual-autoencoder structure for anomaly and novelty detection respectively. The models have been trained and tested on four large scale datasets that are representative of real-world organizational networks and they produce strong results with high precision and recall values. The models provide a novel methodology to detect complex threats with low false positive rates that allow security operators to avoid alert fatigue while drastically reducing their mean time to response with near-real-time detection. Furthermore, the authors also provide a novel, labelled, cyber attack dataset generated from adversarial activity that can be used for validation or training of other models. With this paper, the authors’ overarching goal is to provide a novel architecture and training methodology for cyber anomaly detectors that can generalize to multiple IT networks with minimal to no retraining while still maintaining strong performance.

Developing network intrusion detection systems (IDS) presents significant challenges due to the evolving nature of threats and the diverse range of network applications. Existing IDSs often struggle to detect dynamic attack patterns and covert attacks, leading to misidentified network vulnerabilities and degraded system performance. These requirements must be met via dependable, scalable, effective, and adaptable IDS designs. Our IDS can recognise and classify complex network threats by combining the Deep Q-Network (DQN) algorithm with distributed agents and attention techniques.. Our proposed distributed multi-agent IDS architecture has many advantages for guiding an all-encompassing security approach, including scalability, fault tolerance, and multi-view analysis. We conducted experiments using industry-standard datasets including NSL-KDD and CICIDS2017 to determine how well our model performed. The results show that our IDS outperforms others in terms of accuracy, precision, recall, F1-score, and false-positive rate. Additionally, we evaluated our model s resistance to black-box adversarial attacks, which are commonly used to take advantage of flaws in machine learning. Under these difficult circumstances, our model performed quite well.We used a denoising autoencoder (DAE) for further model strengthening to improve the IDS s robustness. Lastly, we evaluated the effectiveness of our zero-day defenses, which are designed to mitigate attacks exploiting unknown vulnerabilities. Through our research, we have developed an advanced IDS solution that addresses the limitations of traditional approaches. Our model demonstrates superior performance, robustness against adversarial attacks, and effective zero-day defenses. By combining deep reinforcement learning, distributed agents, attention techniques, and other enhancements, we provide a reliable and comprehensive solution for network security.

Network intrusion detection technology has developed for more than ten years, but due to the network intrusion is complex and variable, it is impossible to determine the function of network intrusion behaviour. Combined with the research on the intrusion detection technology of the cluster system, the network security intrusion detection and mass alarms are realized. Method: This article starts with an intrusion detection system, which introduces the classification and workflow. The structure and working principle of intrusion detection system based on protocol analysis technology are analysed in detail. Results: With the help of the existing network intrusion detection in the network laboratory, the Synflood attack has successfully detected, which verified the flexibility, accuracy, and high reliability of the protocol analysis technology. Conclusion: The high-performance cluster-computing platform designed in this paper is already available. The focus of future work will strengthen the functions of the cluster-computing platform, enhancing stability, and improving and optimizing the fault tolerance mechanism.

Cloud computing (CC) is vulnerable to existing information technology attacks, since it extends and utilizes information technology infrastructure, applications and typical operating systems. In this manuscript, an Enhanced capsule generative adversarial network (ECGAN) with blockchain based Proof of authority consensus procedure fostered Intrusion detection (ID) system is proposed for enhancing cyber security in CC. The data are collected via NSL-KDD benchmark dataset. The input data is fed to proposed Z-Score Normalization process to eliminate the redundancy including missing values. The pre-processing output is fed to feature selection. During feature selection, extracting the optimum features on the basis of univariate ensemble feature selection (UEFS). Optimum features basis, the data are classified as normal and anomalous utilizing Enhanced capsule generative adversarial networks. Subsequently, blockchain based Proof of authority (POA) consensus process is proposed for improving the cyber security of the data in cloud computing environment. The proposed ECGAN-BC-POA-IDS method is executed in Python and the performance metrics are calculated. The proposed approach has attained 33.7\%, 25.7\%, 21.4\% improved accuracy, 24.6\%, 35.6\%, 38.9\% lower attack detection time, and 23.8\%, 18.9\%, 15.78\% lower delay than the existing methods, like Artificial Neural Network (ANN) with blockchain framework, Integrated Architecture with Byzantine Fault Tolerance consensus, and Blockchain Random Neural Network (RNN-BC) respectively.

Network intrusion detection technology has developed for more than ten years, but due to the network intrusion is complex and variable, it is impossible to determine the function of network intrusion behaviour. Combined with the research on the intrusion detection technology of the cluster system, the network security intrusion detection and mass alarms are realized. Method: This article starts with an intrusion detection system, which introduces the classification and workflow. The structure and working principle of intrusion detection system based on protocol analysis technology are analysed in detail. Results: With the help of the existing network intrusion detection in the network laboratory, the Synflood attack has successfully detected, which verified the flexibility, accuracy, and high reliability of the protocol analysis technology. Conclusion: The high-performance cluster-computing platform designed in this paper is already available. The focus of future work will strengthen the functions of the cluster-computing platform, enhancing stability, and improving and optimizing the fault tolerance mechanism.