Secure Operation Research Articles

Mitigating sub-synchronous oscillation using intelligent damping control of DFIG based on improved TD3 algorithm with knowledge fusion

The occurrence of sub-synchronous oscillation (SSO) phenomenon in doubly-fed induction generators (DFIGs)-based wind turbines threatens the secure and stable operation of the power grid. Conventional sub-synchronous damping controllers encounter challenges in adapting to the dynamic operating conditions of power systems. This paper introduces an Intelligent Sub-Synchronous Damping Controller (I-SSDC) for DFIGs that integrates deep reinforcement learning (DRL) and knowledge to address the limitations of conventional methods for SSO mitigation. The initial step involves formulating a framework for I-SSDC using the improved twin delayed deep deterministic policy gradient (TD3) algorithm incorporating Softmax. Following this, a surrogate model is constructed, employing Weighted Linear Regression and regularization. This model is designed to identify the predominant influencing factors of SSO, focusing on the selection of the output signal (installation position) to optimize decision-making in I-SSDC. The objective is to enhance the controller’s environmental adaptability and interpretability. Moreover, knowledge and experience related to SSOs are integrated into agent training to improve the exploration efficiency of the agent. Case studies under various operating conditions of the test power system validate the efficacy of the proposed I-SSDC in suppressing SSOs.

The transmission line foreign body detection algorithm based on weighted spatial attention.

The secure operation of electric power transmission lines is essential for the economy and society. However, external factors such as plastic film and kites can cause damage to the lines, potentially leading to power outages. Traditional detection methods are inefficient, and the accuracy of automated systems is limited in complex background environments. This paper introduces a Weighted Spatial Attention (WSA) network model to address the low accuracy in identifying extraneous materials within electrical transmission infrastructure due to background texture occlusion. Initially, in the model preprocessing stage, color space conversion, image enhancement, and improved Large Selective Kernel Network (LSKNet) technology are utilized to enhance the model's proficiency in detecting foreign objects in intricate surroundings. Subsequently, in the feature extraction stage, the model adopts the dynamic sparse BiLevel Spatial Attention Module (BSAM) structure proposed in this paper to accurately capture and identify the characteristic information of foreign objects in power lines. In the feature pyramid stage, by replacing the feature pyramid network structure and allocating reasonable weights to the Bidirectional Feature Pyramid Network (BiFPN), the feature fusion results are optimized, ensuring that the semantic information of foreign objects in the power line output by the network is effectively identified and processed. The experimental outcomes reveal that the test recognition accuracy of the proposed WSA model on the PL (power line) dataset has improved by three percentage points compared to that of the YOLOv8 model, reaching 97.6%. This enhancement demonstrates the WSA model's superior capability in detecting foreign objects on power lines, even in complex environmental backgrounds. The integration of advanced image preprocessing techniques, the dynamic sparse BSAM structure, and the BiFPN has proven effective in improving detection accuracy and has the potential to transform the approach to monitoring and maintaining power transmission infrastructure.

Quantum model prediction for frequency regulation of novel power systems which includes a high proportion of energy storage

As the proportion of renewable energy generation continues to increase, the participation of new energy stations with high-proportion energy storage in power system frequency regulation is of significant importance for stable and secure operation of the new power system. To address this issue, an energy storage control method based on quantum walks and model predictive control (MPC) has been proposed. First, historical frequency deviation signals and energy storage charge–discharge state signals are collected. Simulation data are generated through amplitude encoding and quantum walks, followed by quantum decoding. Subsequently, the decoded data are inputted into the MPC framework for real-time control, with parameters of the predictive model continuously adjusted through a feedback loop. Finally, a novel power system frequency regulation model with high-proportion new energy storage stations is constructed on the MATLAB/Simulink platform. Simulation verification is conducted with the proportional–integral–derivative (PID) and MPC methods as comparative approaches. Simulation results under step disturbances and random disturbances demonstrate that the proposed method exhibits stronger robustness and better control accuracy.

Cybersecurity in supply chains: Addressing emerging threats with strategic measures

This review paper explores the evolving landscape of cybersecurity threats within supply chains, highlighting strategic measures necessary to mitigate these risks. As supply chains integrate more deeply with digital technologies and global networks, they become more vulnerable to sophisticated cyber attacks that can severely disrupt operational integrity and security. This paper synthesizes existing literature to outline the major cybersecurity challenges faced by supply chains, examining case studies and best practices from various industries. Our findings underscore the prevalent threats such as ransomware, phishing, and insider attacks, which exploit the complex interdependencies and digital interfaces in supply chains. The review identifies effective strategic responses, including the integration of comprehensive cybersecurity frameworks, enhanced inter-organizational cooperation, and the implementation of real-time threat detection systems. Conclusively, the paper advocates for a holistic and proactive approach to cybersecurity, emphasizing continuous improvement and adaptive strategies to protect against the dynamic nature of cyber threats in supply chain contexts. Keywords: Cybersecurity, Supply Chain Management, Risk Assessment, Continuous Monitoring, Incident Response, Blockchain Technology, Artificial Intelligence (AI), Collaboration and Information Sharing, Cyber Threats, Emerging Technologies.

A novel correlation-based approach for combined estimation of state of charge and state of health of lithium-ion batteries

State of charge(SOC) and state of health(SOH) estimation are two critical aspects for the secure operation and echelon utilisation of lithium-ion batteries. Considering the two states are interrelated, combined estimation methods are desired in practice. However, previous machine learning-based or traditional filter-based techniques have high computational requirements or suffer from convergence issues. To this end, the combined SOC and SOH estimation method of lithium-ion batteries using the novel correlation between SOC and SOH is proposed in this study. Specifically, a correlation between the capacity ratio for constant-current charging process and constant-voltage charging process and capacity loss is established. Extended Kalman filter is adopted to track SOC values at the end of constant current charging process instead of relying on the entire charging process during online application and SOH estimation can be accomplished in only one step based on the established correlation. The results show that the proposed combined SOC and SOH estimation method enables precise SOC estimation during both charging and discharging processes and achieves reliable estimation results during battery degradation. Additionally, the proposed method offers a swift means of estimating SOH with guaranteed estimation accuracy.

Streamlining Cybersecurity: Unifying Platforms for Enhanced Defense

Cybersecurity leaders face the challenges of complexity, tool overlap, and blind spots resulting from using multiple cybersecurity vendors and tools. In response, adopting a cybersecurity platform consolidation framework simplifies security operations by streamlining products and improving risk posture. Cybersecurity platform consolidation offers various benefits such as improved efficiency, cost savings, and better integration of security tools. However, concerns exist regarding potential limitations in flexibility and agility, increased dependency on a single vendor, and the complexity of migration processes. This review aims to analyze the advantages and drawbacks of cybersecurity platform consolidation to provide a comprehensive understanding of its implications for organizations. As the adoption of cybersecurity platform consolidation continues to grow, it becomes essential to delve deeper into the implications for organizations. By offering a comprehensive analysis of both the advantages and potential drawbacks of cybersecurity platform consolidation, this review aims to provide valuable insights for organizations considering or undergoing this transformation. This will enable organizations to make informed decisions and develop strategies to maximize the benefits while mitigating the concerns associated with platform consolidation. This paper explores the key findings, benefits, and best practices associated with consolidating security solutions.

Key Actions to Enable Automation for Mobile Network Security Operations

Over time, the landscape of Cyberspace surrounding Internet Service Providers (ISPs) has undergone enduring transformations. Notably, mobile networks, integral to contemporary societal infrastructure, consistently encounter evolving cybersecurity threats and risks. ISP processes have adapted with a persistent focus on optimizing network performance and availability, yet the challenges emerge from a laborious and protracted network change management process, hindering the practical automation of network security. Addressing the rightful demand for the highest level of security from mobile network users, our research question probes: "How can we intensify the emphasis on network security and facilitate the automation of network security operations?" To delve into this, we conducted extensive interviews with ISPs globally, affirming the inherent difficulty in automating security operations. The findings categorize challenges into three domains: Security Culture, Operational Processes, and Tools. Cultivating a security culture demands a pivotal commitment to change from top management, coupled with dedicated time and resources. Essential to this is the enhancement of security competence, extending beyond specialists to encompass network engineering staff. Robust network security not only safeguards against threats but significantly influences various business processes. Initiating a secure network requires ISPs to articulate explicit security requirements during the network procurement process, exerting pressure on vendors to fortify systems with a security-by-design approach at the factory. Critical to this is the secure deployment of networks, integrating comprehensive network hardening during the build phase. However, findings indicate a prevalent oversight where network security configuration changes are often neglected or deprioritized in favor of network performance. Achieving a harmonious balance between security and performance necessitates a predefined agreement on a network security configuration baseline. This collaborative effort involves network security specialists and competent network engineers. To effectively monitor and enforce network security configuration, ISPs require automation-enabled tools with the predefined baseline, offering capabilities for monitoring and enforcing network assets. In conclusion, our research emphasizes the imperative need for a paradigm shift in organizational culture, operational processes, and tool utilization to enhance the focus on network security and enable the critical automation of network security operations within the ever-evolving landscape of Cyberspace.

Research on day-ahead and intraday scheduling strategy of distribution network based on dynamic partitioning

With the massive participation of new energy in the operation of distribution network (DN), the uncertainties arising from both load and renewable generation have brought challenges to its secure and reliable operation. In this paper, a model of day-ahead and intraday optimal scheduling based on dynamic partitioning (DP) of DN is constructed to realize the refinement of operation in both temporal and spatial dimensions, as well as to improve the reliability of system operation. For the strong fluctuation characteristics of the source-load in DN, the DP is achieved under the premise that the partition structure in each period is tight and the internal power supply has the ability to balance the power fluctuations. In order to improve the regional autonomy of DN, a day-ahead multi-objective optimal scheduling model is constructed based on the DP with the optimization objectives of weak inter-partition power coupling, lowest deficiency rate of flexibility, and lowest operating cost. And a scheme for articulation is proposed to ensure the effective guidance of day-ahead for intraday. Then the intraday dispatch model is constructed by shifting the focus to the incorporate characteristics of DP and quickly smooth the net load fluctuations. Finally, the performance and reliability of the proposed models are verified via case studies.

Peace Operations and Human Security

Peace Operations and Human Security

Analysing Multidimensional Strategies for Cyber Threat Detection in Security Monitoring

The escalating risk of cyber threats requires continuous advances in security monitoring techniques. This survey paper provides a comprehensive overview of recent research into novel methods for cyber threat detection, encompassing diverse approaches such as machine learning, artificial intelligence, behavioral analysis and anomaly detection. Machine learning plays a central role in cyber threat detection, highlighting the effectiveness of deep neural networks in identifying evolving threats. Their adaptability to changing attack patterns is emphasized, underlining their importance for real-time security monitoring. In parallel, ensemble learning is explored, combining multiple models to improve overall detection accuracy and create a robust defense against a spectrum of cyber threats. The literature reviewed highlights the importance of behavioral analysis, with a novel approach that integrates user behaviour profiling with anomaly detection. This has proven effective in identifying suspicious activity within a network, particularly insider threats and stealthy attacks. Another behavioral framework using User and Entity Behavior Analytics (UEBA) is presented for enhanced anomaly detection, highlighting the importance of context-aware monitoring in improving threat detection accuracy. Collaborative defense mechanisms emerge as a major focus of the research papers reviewed, exploring the potential of sharing threat information between organisations to enhance collective security monitoring. Their findings underscore the importance of a collaborative approach to staying ahead of rapidly evolving cyber threats. Some types of cyber-attacks are also analysed in the context of a security operations centre (SOC) monitoring environment using a security information and event management (SIEM) tool - Splunk. In conclusion, this survey paper synthesizes recent advances in cyber threat detection methods in security monitoring that integrate machine learning, behavioral analysis, and collaborative defense strategies. As cyber threats continue to evolve, these novel methods provide valuable insights for researchers, practitioners, and organisations seeking to strengthen their cybersecurity defenses. This concise overview emphasises the multi-dimensional approach required to secure digital ecosystems, providing a concise yet comprehensive guide to modern cyber threat detection strategies.

Building a Scalable Security Operations Center: A Focus on Open-source Tools

Building a Scalable Security Operations Center: A Focus on Open-source Tools

Wireless sensor network security management based on virtual resource scheduling model

ABSTRACT For lofting the performance of wireless sensor networks, a lightweight gradient elevator algorithm model is improved using a virtual resource scheduling model and sparrow search algorithm particle swarm optimization algorithm. A new network intrusion detection method (SSAPSO-LightGBM) has been proposed to improve detection accuracy and efficiency. Then, the data security transmission level and user historical priority resource scheduling model (SDT-HRU-VRSM) are used to schedule resources to ensure network resource revenue. The results show that the SSAPSO LightGBM algorithm has a detection accuracy of 99.61% for Normal, 98.42% for R2L, 97.03% for U2R, 96.01% for Probe, and 98.41% for DoS. The SDT-HRU-VRSM model not only meets the needs of secure data transmission, but also takes into account the stickiness of increasing high-quality users. And according to security requirements, the number of virtual resource scheduling is increased, reducing the number of service network slicing instances and improving security. This model can effectively increase the attack cost of malicious users and increase network revenue, which is crucial for ensuring the security and efficient operation of wireless sensor networks.

A Novel Techno-Economical Control of UPFC against Cyber-Physical Attacks Considering Power System Interarea Oscillations

In the field of electrical engineering, there is an increasing concern among managers and operators about the secure and cost-efficient operation of smart power systems in response to disturbances caused by physical cyber attacks and natural disasters. This paper introduces an innovative framework for the hybrid, coordinated control of Unified Power Flow Controllers (UPFCs) and Power System Stabilizers (PSSs) within a power system. The primary objective of this framework is to enhance the system’s security metrics, including stability and resilience, while also considering the operational costs associated with defending against cyber-physical attacks. The main novelty of this paper lies in the introduction of a real-time online framework that optimally coordinates a power system stabilizer, power oscillation damper, and unified power flow controller to enhance the power system’s resilience against transient disturbances caused by cyber-physical attacks. The proposed approach considers technical performance indicators of power systems, such as voltage fluctuations and losses, in addition to economic objectives, when determining the optimal dynamic coordination of UPFCs and PSSs—aspects that have been neglected in previous modern research. To address the optimization problem, a novel multi-objective search algorithm inspired by Harris hawks, known as the Multi-Objective Harris Hawks (MOHH) algorithm, was developed. This algorithm is crucial in identifying the optimal controller coefficient settings. The proposed methodology was tested using standard IEEE9-bus and IEEE39-bus test systems. Simulation results demonstrate the effectiveness and efficiency of this approach in achieving optimal system recovery, both technically and economically, in the face of cyber-physical attacks.

Enhancing Quadcopter Autonomy: Implementing Advanced Control Strategies and Intelligent Trajectory Planning

In this work, an in-depth investigation into enhancing quadcopter autonomy and control capabilities is presented. The focus lies on the development and implementation of three conventional control strategies to regulate the behavior of quadcopter UAVs: a proportional–integral–derivative (PID) controller, a sliding mode controller, and a fractional-order PID (FOPID) controller. Utilizing careful adjustments and fine-tuning, each control strategy is customized to attain the desired dynamic response and stability during quadcopter flight. Additionally, an approach called Dyna-Q learning for obstacle avoidance is introduced and seamlessly integrated into the control system. Leveraging MATLAB as a powerful tool, the quadcopter is empowered to autonomously navigate complex environments, adeptly avoiding obstacles through real-time learning and decision-making processes. Extensive simulation experiments and evaluations, conducted in MATLAB 2018a, precisely compare the performance of the different control strategies, including the Dyna-Q learning-based obstacle avoidance technique. This comprehensive analysis allows us to understand the strengths and limitations of each approach, guiding the selection of the most effective control strategy for specific application scenarios. Overall, this research presents valuable insights and solutions for optimizing flight stability and enabling secure and efficient operations in diverse real-world scenarios.

Circulating knowledge through disparate practices: the global pursuit of terrorist financing by Financial Intelligence Units (FIUs)

ABSTRACT Standardization and harmonization are widely studied by scholars of Science and Technology Studies (STS) to understand how knowledge and things circulate across geographies, yet it often remains unclear what these terms exactly refer to. In dictionaries, they are generally defined as actions or processes aimed at achieving uniformity, similarity, or alignment. In STS literature, it is often assumed that when a practice is (un)successfully standardized or harmonized, the circulation of knowledge and things across distance and difference is made (im)possible. However, it is questionable whether the geographical circulation of things and knowledge is necessarily enabled by the uniformity of practices. When analyzing the global exchange of financial intelligence, a different picture emerges, whereby the coordination of security operations and the exchange of financial intelligence are actually enabled by disparate quantification practices. Financial Intelligence Units (FIUs) – governmental organizations that globally exchange financial intelligence for combatting money laundering and terrorist financing – are geographically scattered and very different in nature, each constructing security threats such as terrorism or terrorist financing uniquely. Yet through their complex quantification practices, they make coordination possible while persisting in maintaining differences, without relying on an increasingly uniform and universal sameness of practices. For FIUs, quantification practices, even though differently practiced, generate a shared domain with its own transnational, depoliticized, and highly technical proxy grammar that makes it possible to work beyond and background otherwise incommensurable issues.

Transition meets instability: Chad after Idriss Déby Itno

Chadian President Idriss Déby Itno died on 20 April 2021 on the front lines against rebels from the Front for Change and Concord in Chad (FACT). Following Déby’s death, a military committee headed by his son, Mahamat Déby, seized power and initiated a transition that led to Mahamat’s election to presidency in May 2024. Domestic conditions have worsened in the interim, and the Lake Chad Basin and Sahel are now among the most unstable regions in the world. Idriss Déby was a key figure in regional security operations and employed gatekeeping and neopatrimonialism to direct resources towards strengthening Chad’s forces, but the prolonged transition has called into question the extent to which this strategy of rentier peacekeeping can be maintained. This article assesses why Chad is facing this crisis and how the transition has impacted domestic and regional conditions. This rests on theorisation of the Chadian state, analysis of Chad’s history and transition to date, and application of theories of gatekeeper states, neopatrimonialism and omnibalancing to assess where the state is headed in the immediate term, concluding that a transition lacking real democratic transformation will exacerbate rather than alleviate tensions in Chad and the surrounding region.

A novel nature-inspired nutcracker optimizer algorithm for congestion control in power system transmission lines

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers’ food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment.

Impact of NextGen Wireless 5G Network on Agriculture

The advent of next-generation wireless networks, particularly 5G, presents a transformative opportunity for the agriculture sector. The potential impact of 5G technology on agricultural practices, highlighting the opportunities it brings and the challenges that must be addressed for successful integration. Smart and precision farming allows farmers to be more informed and productive. The advent of 5G will considerably change the nature of jobs in farming and agriculture. The internet of things (IoT)- based cloud computing service in the 5G network provides flexible and efficient solutions for smart farming. This will allow the automated operation of various unmanned agricultural machines for the plowing, planting, and management phases of crop farming and will ultimately achieve secure, reliable, environmentally friendly, and energy-efficient operations and enable unmanned farms.

The Expanding Attack Surface: Securing AI and Machine Learning Systems in Security Operations

Cyber threats' increasing magnitude and intricacy require a fundamental change in security operations. Conventional approaches face difficulties in keeping up, which exposes organizations to risks. This paper examines the expanding attack surface: securing AI and machine learning systems in security operations as a remedy. A literature review, informed by the Diffusion of Innovation Theory, investigates how organizations absorb innovations in this study. The results demonstrate notable benefits of AI/ML in security, such as superior identification of threats, improved efficiency through automation, and optimized management of vulnerabilities. Nevertheless, achieving successful execution necessitates meticulous deliberation of obstacles. These tasks encompass guaranteeing data accuracy, preserving the capacity to understand how models work, reducing any potential prejudices in AI/ML models, and resolving security weaknesses in the systems themselves. The paper also discusses ethical considerations and emphasizes the important function of human monitoring. To address these difficulties, the study recommends prioritizing data quality, utilizing explainable AI methods, and developing tactics to mitigate bias. Furthermore, there is a strong emphasis on using a human-in-the-loop strategy to take advantage of humans' expertise and machine-learning capabilities. This study highlights the capacity of artificial intelligence and machine learning to transform security operations completely. By confronting the recognized obstacles, organizations may unleash the genuine potential of these technologies and establish a stronger and more proactive security position in response to constantly changing cyber threats.

Cost-Effective Target Capacity Assessment in the Energy Transition: The Italian Methodology

Long-term transmission expansion planning has to face the energy transition in a restructured electricity market environment. Increased transmission capacity within and between Member States is likely to play an essential role in maintaining the secure and economic operation of the whole European power system and ensuring the integration of growing renewable generation. This paper proposes a novel iterative methodology aimed at assessing an optimal level of interconnection between relevant bidding zones, simultaneously investigating different potential alternatives. Starting from a reference grid, a multi-criteria analysis is adopted to select the additional transmission capacities to be tested in each iteration via network and market simulations in order to confirm that transmission expansion benefits outweigh the estimated realization costs. The proposed approach is applied to the Italian case in two contrasting energy scenarios for the mid-term 2030 and very-long-term 2040 horizons: different development strategies are derived, and the least regret criterion is applied to define the most cost-effective as the target development strategy for the Transmission System Operator (TSO). Furthermore, sensitivity analyses on relevant input data variation are performed to test the robustness of the results obtained.