Secure Network Slice Mapping Strategy for Security and Stability Control System

Internet-based computing has lead to an emergence of a large number of threats. One of the major threat is DDoS (Distributed Denial of Service) attack. Recent incidents have shown that DDoS attacks have the capability of shutting a business not for a day but weeks. DDoS attacks have a greater impact on multi-tenant clouds than traditional infrastructure. DDoS attacks in the cloud, take the shape of EDoS (Economic denial of sustainability) attacks. In EDoS, instead of “Service Denial”, economic harms occur due to fake resource usage and subsequent addition or buying of resources using on-demand provisioning. To detect and mitigate DDoS attacks in the cloud, we argue that on-demand resource allocation (known as auto-scaling) should also be looked, in addition to network or application layer mitigation. We have proposed a novel mitigation strategy, DARAC, which makes auto-scaling decisions by accurately differentiating between legitimate requests and attacker traffic. Attacker traffic is detected and dropped based on human behavior analysis based detection. We also argue that most of the solutions in the literature, do not pay much attention to the service quality to legitimate requests during an attack. We calculate the share of legitimate clients in resource addition/buying and make subsequent accurate auto-scaling decisions. Experimental results show that DARAC mitigates various DDoS attack sets and take accurate and quick auto-scaling decisions for various legitimate and attacker traffic combinations saving from EDoS. We also show how proposed mechanism could make “arms-race” very difficult for the attackers as the resource need to defeat DARAC mechanism on a very small capacity server is huge. Results also show significant improvements in the average response time of the web-service under attack, in addition to infrastructure cost savings up to 50 % in heavy attack cases.KeywordsCloud ComputingVirtual MachineAttack ScenarioIdle ResourceCloud ConsumerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

While the differentiated services (DiftServ) infrastructure is scalable and robust in providing network quality of service (QoS), there are serious drawbacks with the services provided by DiffServ: (1) the services are coarse-grained and one-way only; (2) no service differentiation and resource isolation are provided to meta-data packets such as TCP SYN and ACK packets. Moreover the coarse-grained service differentiation and the lack of resource isolation at IP routers exposes its vulnerability to distributed denial of service (DDoS) attacks. Based on the concept of layer-4 service differentiation and resource isolation, where the transport-layer information is inferred from the IP headers and used for packet classification and resource management, we present a scalable fine-grained DiffServ (sf-DiffServ) architecture that provides fine-grained service differentiation and resource isolation among thinner behavior aggregates (BAs). The sf-DiffServ architecture consists of a fine-grained QoS classifier and an adaptive weight-based resource manager at IP routers. A two-stage packet classification mechanism is devised to decouple the fine-grained QoS lookup from the routing lookup at core routers. Due to its scalable QoS support for TCP control segments, sf-DiffServ supports bi-directional differentiated services for TCP sessions. Most importantly, the fine-grained resource isolation provided inside the sf-DiffServ is a powerful built-in protection mechanism to counter DDoS attacks, reducing the vulnerability of the Internet to DDoS attacks.

In the past few decades, there was an increase in the number of devices that have wireless capabilities such as phones, televisions, and home appliances. With the high demand for wireless networking, the fifth generation (5G) of mobile networks was designed to support the different services of new applications. In addition, one of the technical issues that 5G would evolve is the increase in traffic and the need to satisfy the user’s experience. With the evolution of wireless networking and 5G, Network Slicing has been introduced to accommodate the diverse requirements of the applications. Thus, network slicing is the concept of partitioning the physical network infrastructure into multiple self-contained logical pieces which can be identified as slices. Each slice can be customized to serve and meet different network requirements and characteristics. In terms of security, network security has allowed for new security vulnerabilities such as Distributed Denial of Service (DDoS) and resource exhaustion. However, slices can be isolated to provide better resource isolation. In addition, each slice is considered an end-to-end virtual network, operators would be able to allocate resources to the tenants which are the service providers. The isolated resources are controlled by the tenants; each tenant has control over how to use them to meet the requirements of the clients. One of the challenges in network slicing is RAN slicing. The target of RAN Slicing is to meet the QoS requirements of different services for each end-user. However, the coexistence of different services is challenging because each service has its requirements. Each slice must estimate its network demands based on the QoS requirements and control the admission to the slice. To solve this issue, we consider the scenario for the enhanced mobile broadband (eMBB) and the ultra-reliable-low-latency communication (URLLC) use cases’ coexistence, and we slice the RAN based on the priority of the user application

The exponential growth of interconnected systems across the Internet of Things (IoT), Software-Defined Networks (SDN), and cloud environments has led to a drastic increase in the scale and advancement of Distributed Denial of Service (DDoS) attacks. Conventional defences based on machine learning are often static, traffic-centric, and lack adaptivity to dynamic network behaviour, resulting in high false-positive rates and delay in mitigation responses. To address these challenges, this paper presents a conceptual framework for an AI-driven, context-aware DDoS detection and mitigation system. The proposed approach employs a optimized Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) model for efficient, real-time detection of DDoS attacks. The model captures spatial and temporal correlations in traffic while incorporating contextual parameters such as Quality of Service (QoS), Service Level Agreement (SLA) priority, and workload telemetry. These contextual attributes enable the model to distinguish legitimate network fluctuations from malicious activities, so that the false positive rate will be reduced without compromising accuracy. For adaptive response, a Reinforcement Learning (RL)-based mitigation layer—exploiting Deep Q-Network (DQN) and Actor–Critic (AC) variants—continuously learns optimal mitigation actions based on real-time feedback. The interaction between detection and mitigation components forms a context-aware feedback loop, enabling dynamic policy refinement and continuous system improvement. Comprehensive literature analysis reveals that existing AI-based DDoS defense mechanisms lack integration between detection and mitigation, and often overlook contextual intelligence. The proposed framework bridges this gap by introducing a self-adaptive, feedback-driven defense system capable of maintaining detection precision, low latency, and QoS preservation in heterogeneous environments.

Network slicing, where a single physical network is partitioned into several fit-for-purpose virtual networks with different degrees of isolation and quality of service (QoS), is a key enabler of 5G and beyond mobile networks. However, it is prone to security threats such as Distributed Denial-of-Service (DDoS) attacks. In this paper, we propose a solution based on Deep Learning (DL) that detects such attacks, and then creates a sinkhole-type slice with a small portion of physical resources to isolate and mitigate the attackers' action. Using our 5G prototype based on OpenAirInterface, we evaluate our approach by comparing several DL models in terms of detection accuracy, false positive rate, execution time, among other Machine Learning-related metrics. We also assess the performance of created 5G network slices in terms of benign/malicious users' throughput, as well as the processing time during the slicing operations. Results show that our approach is able to detect DDoS attacks in a timely manner with an accuracy of almost 97% and a false positive rate of less than 4%. We also show that our approach decreases the network throughput for the malicious users by a factor of 15, while maintaining a high network throughput for benign users.

Under the "dual-carbon" target vision, the construction of a new power system with a gradually increasing proportion of new energy has become a major direction. In the process of the continuous development of the power system, the new energy has the characteristics of large volatility and strong randomness, so it is necessary to constantly improve the regulation capacity of the power grid to ensure the stability of the power system. In this paper, the hardware reliability of the security and stability control system is detected by quantitative calculation. Based on two different operation modes of "one out of two" and "two out of two", we use Markov state-space method to calculate the rejection probability respectively, with the ratio of the device repair rate and the fault rate being the parameters. The calculation results are used to analyze the stability of the system, which guarantees the stable operation of the power grid.

In the present computer era, the vulnerabilities inherent in the Internet architecture enable various kinds of attacks. Distributed Denial of Service (DDoS) is one of such prominent attack that is a lethal threat to Internet domain that harnesses its computing and communication resources. The increase in network traffic rates of legitimate traffic and its flow similarity with attack traffic has made the DDoS detection very difficult despite deployment of diversified defense solutions. The ISPs are bound to invest heavily to counter such problems which has a significant impact on company finances. To provide uninterrupted quality services to the end users, ISPs needs to deploy a distributed solution for timely detection and discrimination of attack and behaviorally similar flash events (FE) traffic. Such distributed defense systems can be deployed at source-end, intermediate network-end or at the victim-end location. Since the volume of traffic to be analyzed is very large, the detection accuracy and low computational complexity of the proposed defense solution is always a challenging problem. This paper proposes an ISP level distributed, collaborative and automated (D-CAD) defense system for detecting DDoS attacks and FEs, and has the capability to effectively distinguishing the two. Additionally, D-CAD defense system is also capable of categorizing FE traffic and has low computational complexity. The proposed system is validated in novel software defined networks (SDN) using Mininet emulator. The results show that D-CAD defense system outperformed its existing counterparts on various detection system evaluation metrics.

In 5G networks, the deployment of network slices enabled by Software-Defined Networking (SDN) is becoming a critical component for delivering tailored services to meet diverse application needs. However, this introduces challenges in network management, particularly in efficiently allocating resources to ensure that each network slice meets its specific Quality of Service (QoS) and availability requirements. Simultaneously, it must optimize overall network performance and network operator’s profit, which is linked to the Quality of Experience (QoE) of the end-users. Existing works offer either an availability-based solution or a QoE-aware solution to this problem, but not both. This paper addresses the end-to-end network slice resource allocation problem by simultaneously considering QoS and availability requirements in slice placement, while employing a QoE-aware strategy for resource allocation. We propose a framework that optimizes the network operator profit i.e. the highest QoE with the least resource usage, and can be flexibly configured to model realistic scenarios. Arbitrary network slice requirements can be defined using slice-specific QoS/QoE mapping, resource requirements, end-to-end latency and availability. For solving the formulated problem a Mixed Integer Nonlinear Programming (MINLP) formulation and efficient heuristic methods are proposed. Our solution accounts for the non-linear QoS/QoE relationship, utilizes redundantly placed Service Function Chains (SFCs) to increase availability, and supports the sharing of Virtual Network Functions (VNFs) among SFCs to optimize resource usage. Through extensive simulations on realistic network topologies and slice requests, we demonstrate the framework’s effectiveness in offering flexible and efficient network slice placement and resource allocation, utilizing a baseline heuristic from related studies. The results indicate that while the exact method delivers an optimal solution, heuristic approaches are suitable for time-sensitive tasks, such as dynamic slice configuration.

Fifth-generation (5G) communication networks provide unprecedented opportunities for military operations, offering ultra-low latency, massive device connectivity, and enhanced bandwidth. At the same time, they introduce new vulnerabilities, including risks associated with network slicing, edge computing, and the proliferation of Internet of Things (IoT) devices. Traditional perimeter-based security models are insufficient to address these threats, particularly in contested or grey-zone environments where adversaries deliberately exploit ambiguities below the threshold of armed conflict. This paper proposes a Zero Trust Architecture (ZTA) framework for 5G-enabled military communication networks. The framework integrates continuous authentication, least-privilege access, microsegmentation of network slices, and AI-driven anomaly detection to ensure resilience against insider threats and distributed denial-of-service (DDoS) attacks. Scenario-based evaluation demonstrates that Zero Trust reduces both the likelihood and impact of such attacks, maintaining command-and-control (C2) continuity even under adversarial conditions. The findings underscore the importance of embedding Zero Trust into military communication infrastructures, both as a technical solution and as a strategic enabler of NATO and EU defence resilience.

Greater complexity in current computer networks, introduced in response to cloud computing, Internet of Things (IoT), and 5G technologies, has made complex approaches towards managing, optimizing, and securing network systems prominent.The traditional network management techniques, rooted primarily in strict rules and human intervention, are unable to cope with the amount of data and dynamics of current networks.Therefore, the use of Artificial Intelligence (AI) in networking is becoming a game-changer.Artificial Intelligence (AI) through such technologies as machine learning (ML), deep learning (DL), reinforcement learning (RL), and natural language processing (NLP) can indeed assist in enhancing the design, management, and security of the network system.There are a number of ways by which AI can optimize the networks.Traffic patterns for smart traffic control as well as resource allocation can be forecast using machine learning algorithms.The anomaly detection capabilities of machine learning-based systems also provide real-time security attack detection, hence mitigating the impact of attack vectors such as Distributed Denial of Service (DDoS) or malware attacks.Lastly but not the least, AI is capable of offering self-healing networks that automatically detect faults and heal themselves as required without human intervention, a business of unimaginable value in enormous systems.Reinforcement learning is very beneficial for dynamic routing and load balancing through constant adjustment of network parameters to changing conditions.Other applications of AI in the networks include optimization of Quality of Service (QoS), where applications with high priority such as video streaming or gaming are assigned the bandwidth, they require to function efficiently.In addition, with edge computing and 5G networks, the work of AI is ensuring that network resources are optimally distributed to edge devices for maximum scalability and performance.However, there are some limitations in the use of AI for networking.It needs to be extensively tested with data privacy issues, interpretability of the model, and computational complexity of the AI model.The requirement for high real-time performance puts constraints in processing the network, which can be itself a limiting factor for the use of AI in some applications.Even with these challenges, the potential of AI to revolutionize networking cannot be overstated, and work on network systems with AI at its foundation will probably yield more intelligent, more autonomous, and more secure networking choices.

With the gradual development of a high-proportion new energy power system to a new energy power supply as the main power system, the system’s security and stability characteristics and control behavior have gradually undergone substantial changes. In this paper, firstly, by incorporating smart electrical equipment such as wind power, photovoltaic, energy storage, and DC into the technical requirements of emergency control, the emergency control evaluation indicators are quantitatively analyzed, and the emergency control technical indicators of new energy stations are proposed based on the existing project implementation basis. Further, based on the existing security and stability control system architecture for upgrading and transformation, a distributed security and stability control system intelligent collaborative optimization control scheme is proposed, and the applicable conditions, overall architecture, information command flow and intelligent collaborative control strategy are elaborated in detail. Finally, based on the actual power grid, simulations verify the effectiveness of the proposed control architecture and control method.KeywordsSafety and stability controlNew energyOptimizationSmart collaboration

Electronic health record (EHR) security is a multidomain job that includes authentication, privacy protection, document storage security, etc. Trusted access control requires authentication, safe data transfer needs privacy, and preserved records security needs security to fend off data-level assaults. Researchers have suggested a broad range of techniques, such as biometric authentication, smartphone authentication, role-based access control, data encryption, hashing, etc., to carry out these responsibilities. These models are implemented utilizing centralized or decentralized strategies, which regulate the system’s overall data flow. Security issues with centralized methods include distributed denial of service (DDoS), Sybil, trojan horse attacks, etc. Decentralized systems’ slower performance restricts their ability to deploy in real time. This essay suggests a minimal complexity blockchain-based EHR management solution to lessen the likelihood of these problems. The suggested model describes the architecture of an unique semi-centralized technique for delivering EHR security and makes use of the benefits of centralized & decentralized approaches. When the suggested system’s security performance is compared to several previously produced models, it is found that the proposed model has superior security and a quicker reaction time. Additionally, this book suggests a few machine learning models that may be combined with the suggested model to enhance its performance in terms of security and quality of service.

Cloud computing is a technological marvel that transcends conventional boundaries by utilizing an Internet-based network of remote servers to store, manage, and process data and many other services. It represents a contemporary paradigm for delivering information technology services. Today, cloud computing services have become indispensable for both individuals and corporations. However, adopting cloud services presents fresh challenges in terms of service quality, resource optimization, data integration, cost governance, and operational security. The security of cloud services is of supreme importance, given the open and distributed nature of the environment, making it susceptible to various cyberattacks, such as Denial of Service (DoS) or Distributed DoS (DDoS) attacks, among others. Cyberattacks can have severe repercussions on the availability of cloud services, potentially causing complete DoS. In numerous instances, the detection of attacks is delayed, pushing cloud platforms to a breaking point. Emphasizing the importance of proactive measures, it becomes crucial to identify and alert about any suspicious access long before the latter reaches a critical stage, mitigating the risks and preventing potential service disruptions. This study introduces a preventive approach that utilizes artificial intelligence techniques to improve the security of cloud services. The proposed method aims to detect and flag potential attack behaviors well in advance before they affect service quality. To achieve this, the particular method involves periodic identification and measurement of critical information on service access and resource utilization. This can be accomplished by analyzing cloud server logs or integrating dedicated sniffing software to capture and store technical traffic details. Subsequently, the collected data are processed by analyzing traffic properties to proactively identify and report any indications of cyberattacks.

With the high-speed development of UHV power grid, the characteristics of power grid changed significantly, which puts forward new requirements for the safe operation of power grid and depend on Security and Stability Control System (SSCS) greatly. Based on the practical cases, this paper analyzes the principle of the abnormal criteria of the SSCS and its influence on the strategy of the SSCS, points out the necessity of the research on the locking strategy of the SSCS under the abnormal state. Taking the large-scale SSCS for an example, this paper analysis different control strategies of the stations in the different layered, and puts forward effective solutions to adapt different system functions. It greatly improved the effectiveness and reliability of the strategy of SSCS, and ensure the integrity of the system function. Comparing the different schemes, the principles of making the lock-strategy are proposed. It has reference significance for the design, development and implementation of large-scale SSCS.

With the rapid development of ultra-high voltage direct current (U-HVDC) transmission systems, the security and stability control system (SSCS) plays an increasingly important role in ensuring the reliable operation of the power grid as the second line of defense for power system protection. To analyze the impact of SSCS overall architecture on the system reliability, firstly, a reliability analysis method for the SSCS is proposed to correlate the hardware, software and communication channel of the U-HVDC's SSCS under different U-HVDC's SSCS operation modes by establishing a fault tree model of the SSCS. Secondly, the sequential Monte Carlo method is employed to analyze the reliability of the U-HVDC's SSCC model considering the failure rate and maintenance time for each part of the SSCS. The proposed fault tree model and reliability analysis method is used to simulate a real case of the U-HVDC's SSCS commissioned in China. The result compares the reliability of the system architecture under different operation modes and provides significant insights on the selection rules of the U-HVDC's SSCS operation mode for system reliability improvement.