System Security Research Articles

Network-Based Intrusion Detection for Industrial and Robotics Systems: A Comprehensive Survey

In the face of rapidly evolving cyber threats, network-based intrusion detection systems (NIDS) have become critical to the security of industrial and robotic systems. This survey explores the specialized requirements, advancements, and challenges unique to deploying NIDS within these environments, where traditional intrusion detection systems (IDS) often fall short. This paper discusses NIDS methodologies, including machine learning, deep learning, and hybrid systems, which aim to improve detection accuracy, adaptability, and real-time response. Additionally, this paper addresses the complexity of industrial settings, limitations in current datasets, and the cybersecurity needs of cyber–physical Systems (CPS) and Industrial Control Systems (ICS). The survey provides a comprehensive overview of modern approaches and their suitability for industrial applications by reviewing relevant datasets, emerging technologies, and sector-specific challenges. This underscores the importance of innovative solutions, such as federated learning, blockchain, and digital twins, to enhance the security and resilience of NIDS in safeguarding industrial and robotic systems.

Sustainable Nitrogen Management in Rice Farming: Spatial Patterns of Nitrogen Availability and Implications for Community-Level Practices

Sustainable nitrogen management is crucial for long-term food security and environmental protection in rice farming systems. However, the spatial patterns of nitrogen availability at the community level remain poorly understood, hindering the development of effective sustainable management strategies. This study introduces a novel application of spatial autoregressive analysis to investigate available nitrogen distribution in paddy soils across a rice farming community in Kyoto, Japan. Soil samples from 61 plots, including organically farmed ones, were analyzed for available nitrogen and various physicochemical properties. Contrary to the hypothesis of high variability between adjacent plots, significant positive spatial autocorrelation in available nitrogen was observed, revealing previously unrecognized community-level patterns. The spatial Durbin model outperformed traditional regression approaches and revealed complex spatial interactions in soil properties. Water-soluble organic carbon and humus content showed strong but opposing effects, with a positive direct impact but negative spatial interaction, suggesting topography-driven accumulation processes. Water-soluble nitrogen exhibited reverse patterns with negative direct effects but positive spatial interaction, indicating potential nutrient transport through water movement. These findings highlight the importance of considering both direct and indirect spatial effects in understanding soil fertility patterns, challenging the conventional plot-by-plot management approach. This methodological advancement provides new perspectives for more effective, community-scale soil management strategies in rice farming systems. Moreover, it demonstrates an innovative approach to maximizing the value of outsourced soil analysis data, providing a model for more comprehensive utilization of such data in agricultural research. By enabling more targeted and efficient nitrogen management practices that consider both plot-level processes and landscape-scale interactions, this study potentially contributes to the development of more sustainable and resilient rice production systems.

Assessing Satellite-Augmented Connected Vehicle Technology for Security Credentials and Traveler Information Delivery

Vehicle-to-Everything (V2X) technology has the capability to enhance road safety by enabling wireless exchange of telematics and spatiotemporal information between connected vehicles (CVs). Effective V2X communication depends on rapid information sharing between Roadside Units (RSUs), in-vehicle On-Board Units (OBUs), and other connected infrastructure. However, there are increasing concerns with RSUs related to installation needs, reliability, and coverage, especially on rural roadways. This study aims to evaluate the benefits of augmenting CV infrastructure with satellite technology in situations where RSU access or coverage is limited while maintaining V2X security protocols and critical information exchange. The study utilizes data from over 400 personal, fleet, and commercial CVs collected during two real-world pilot deployments in the United States, one in an urban environment in Florida and one in a rural environment in Wyoming. The analysis performed shows that the delivery of critical security credential information and traveler information messages (TIMs) to CVs is dependent on a multitude of environmental and operational reliability factors. Overall, information delivery is faster with dense RSU infrastructure as compared to satellites. However, we show that by augmenting RSU infrastructure with satellite technology, the delivery of information is more robust, improving V2X system reliability, security, and overall road safety.

Sustainable Agro-Food Systems and Their Importance for Farmers in the Moxotó History, State of Pernambuco, Brazil

Objective: To analyze the sustainable agri-food systems and their relevance for farmers in Sertão do Moxotó, state of Pernambuco, Brazil, implemented by the Alternative Technology Service - SERTA through the Sustainable Semiarid project, in order to identify the changes that have occurred in farmers, and what they turned to believe and practice from this experience. Theoretical Framework: Based on topics, family farming, agroecology, agri-food system, food and nutritional security, water, nutrients and energetics, and Technical Assistance and Rural Extension - ATER. Method: The adopted methodology was action research, with individual visits to 20 farmers from nine communities, in a universe of 40 farmers from 19 communities. Data collection was carried out through interviews, questionnaires, and SERTA bibliographic documents. Results and Discussion: The results showed significant changes in the lives of farmers, with 10 farmers participating in agro-ecological-based fairs, increased production without pesticides, family involvement, concern for water, soil, and air, and trust in SERTA. Among the elements presented, it was noted that the project's discontinuity generated insecurity since the SERTA team was considered a family. Implications of the Research: Therefore, the results are innovative and relevant to strengthening and disseminating agroecology and inspiring others to change their practices in a conscious and economically viable way. Originality/Value: This study reveals the importance of social projects aimed at farmers, with technical and pedagogical training focused on food and nutritional security, nutrients, water, and energy, which will provide positive impacts on changes in conceptions and practices from the perspective of a good living.

Mathematical Techniques in the Design of Robust Control Systems

Solid control frameworks are exceptionally imperative for making beyond any doubt that energetic frameworks remain steady and work well in a parcel of diverse circumstances and with a parcel of questions. This diagram tells you everything you wish to know about the numerical strategies utilized to form steady control systems. Strong control tries to bargain with issues like unknowns within the system model, stuns from the exterior, and changes within the system's parameters. It does this to form beyond any doubt that the framework keeps working well and remaining steady in spite of these issues. Lyapunov soundness hypothesis and the H-infinity (H∞) optimization strategy are two of the foremost critical arithmetic apparatuses. Lyapunov steadiness hypothesis gives us a way to see at how steady a framework is, and H∞ minimizes the worst-case pick up from unsettling influence to yield. State-space representations are moreover utilized to show and ponder multi-input multi-output (MIMO) frameworks, which makes control plan more organized. When making controls that guarantee steady execution, strategies like Direct Framework Disparities (LMIs) and Riccati conditions are utilized. The thought of unwavering quality limits is additionally displayed to degree how well the framework can handle factors. The solid control plan incorporates frequency-domain apparatuses, such as the Nyquist and Bode plots, to see at how the framework acts at distinctive frequencies. This makes it simpler to form controllers that can bargain with high-frequency occasions. We conversation around progressed strategies like α-synthesis and strong shaft situation that can offer assistance with certain control problems in dubious settings. Down to earth illustrations appear how these scientific strategies can be used, showing how vital they are in numerous building areas, such as the airplane, vehicle, and prepare control industries. The unique emphasizes how imperative these numerical strategies are for making beyond any doubt that solid control frameworks can handle the challenges of the genuine world, which improves system security, execution, and steadfastness in a wide run of circumstances. The abstract's objective with this outline is to provide you a basic idea of the math that's critical to the field of solid control framework plan.

Cybersecurity Challenges in Net Energy Metering: Legal Frameworks and Practical Solutions

Net Energy Metering (NEM) has emerged as a key factor in Modern Energy Management, empowering consumers to produce their own electricity and return Net Excess Generation (NEG) to the grid. However, the increased usage of NEM has introduced significant network security challenges. As millions of electronic devices are interconnected through communication networks across critical power facilities cybersecurity has become a concerning factor, directly affecting the reliability of such extensive infrastructure. This paper gives an insight into the rising cyber offences associated with NEM systems, stipulates the legal framework, e-governance, and liabilities under the Information Technology (IT) Act, 2000. Categorization of potential cyber threats like Access control, database security and encryption, intrusion detection, malicious software, operating system vulnerabilities and security, application security has been highlighted. Evaluation of Time of Day (ToD) pricing mechanism focusing on real time monitoring and energy efficiency during peak and off-peak consumption. Incorporating Virtual Private Networks (VPNs) and other cybersecurity measures to safeguard the integrity and reliability of NEM systems. The paper illustrates the impact of cybercrimes on NEM, by exploring existing applications and case studies. It underscores the necessity of implementing robust cybersecurity practices to protect these systems. The objective of the paper is to raise awareness about the cybersecurity challenges encountered in NEM systems and to propose practical solutions to mitigate these risks, ensuring a safe and regulated environment for the functioning of smart grids in a progressively growing digital world.

Real-Time Data Governance and Compliance in Cloud-Native Robotics Systems

This study investigates the frameworks and challenges of real-time data governance and compliance in cloud-native robotics systems, focusing on data integrity, cloud security, regulatory adherence, and cybersecurity risks. Using extensive datasets from the Amazon AWS Open Data Registry, the EU GDPR Enforcement Tracker, and Kaggle’s IoT dataset, the analysis explores cloud-native systems' data accuracy, security, and governance. Data were extracted through a standardized process: performance metrics, including latency and error rates, were gathered from Amazon AWS to assess system efficiency, GDPR violation records were analyzed from the EU Enforcement Tracker to understand compliance trends, and data volume and governance metrics from Kaggle’s IoT dataset were correlated to identify governance challenges. Together, these data sources provide comprehensive insights into how cloud-native systems can be optimized for real-time operations. The study highlights the cloud security benefits and governance advantages inherent to cloud-native frameworks, such as real-time monitoring, automated threat detection, and data encryption, which collectively reduce unauthorized access risks while supporting operational efficiency. Findings indicate high data accuracy (0.51% error rate) and low latency (mean of 48.96 ms) across systems; however, processing time variability (standard deviation of 28.61 ms) signals a need for further optimization in time-sensitive environments. The regression analysis of GDPR violations reveals a substantial penalty increase of €53,789.41 per violation, emphasizing the financial risks of non-compliance. Correlation analysis (r = 0.083 for data volume and governance failures) suggests that external cybersecurity threats have a greater impact on governance than internal metrics, underscoring the importance of adaptive governance frameworks that support both data integrity and regulatory compliance in cloud-native robotics systems.

Minimising cybersecurity risk exposures in industrial control system environments: a techno-human vulnerability analysis approach

ABSTRACT Organisations operating IoT-enabled industrial control systems (ICSs) are concerned about growing cybersecurity risks and impacts to their systems. Cyber-attacks on ICSs demonstrate that technology alone is neither a problem nor a solution to the growing cybersecurity issues affecting these systems. As socio-technical systems, ICSs encompass the functions and interactions of social and technological system elements to enable and/or sustain industrial processes. Thus, a more effective cybersecurity risk management process needs to consider human and technology factors, especially for high-value industrial process targets. Combining critical reviews and gap analysis of existing vulnerability assessment methods with conceptual modelling, a Vulnerability Analysis Critical Impact Point Process (VACIP) methodology is proposed which considers both human and technological vulnerabilities within a cyber-physical system environment to inform an improved insight about attack criticality and impacts. VACIP is validated using a simulated industrial mini testbed; showing that it can enable practicable support for security vulnerability discovery, impact criticality analysis, weak link identification, and prioritised controls. Its novelty is demonstrated in its combination of technology and human vulnerability evaluations in the minimisation of system security exposures. It provides a useful guide for adopting effective cybersecurity risk assessment and exposure reduction strategies.

MANAGEMENT OF THE FINANCIAL FLOWS OF LOGISTICS SYSTEMS

In the context of globalization and integration of the world economy, the main task of every business entity is to ensure financial stability and security of its operations. The logistics industry is no exception. In such a context, there is a necessity of fundamentally new approaches to the development of an effective system for managing financial flows of logistics systems. The insufficiency of the theoretical and practical study of the problems of managing financial flows of logistics systems, the difficult current economic conditions of management, outlined by the impact of the financial crisis and economic instability, the necessity to manage and control the level of financial stability of logistics enterprises in order to prevent bankruptcy, are the key reasons for the relevance of the study. In article considers issues of management of financial flows of logistics systems. The analysis of management processes in the system of financial activity of the logistics industry has been conducted. It is noted that the relevant aspects of managing the financial flows of logistics systems should include measures to improve the efficiency of using borrowed capital and investment resources. The study theoretically substantiates and solves a set of issues related to the development of an effective concept for managing financial flows of logistics systems under conditions of uncertainty. The analysis of the status and trends in the development of the logistics sector, as well as topical aspects of financial management of logistics enterprises, has allowed to develop a set of measures to improve the system of management of financial flows of logistics systems, which should be implemented on the principles of financial management. The concept of management of financial flows of logistics systems is proposed. A scheme for ensuring the financial security of logistics systems has been developed. The key principles of management of financial flows of logistics systems under conditions of uncertainty are determined. The study made it possible to state that the conceptual approach to managing the financial flows of logistics systems will help to prevent the crisis, increase the efficiency of activities and the successful development of the industry as a whole.

Administrative and legal framework for the introduction of the electronic notarial system in Ukraine

The article is dedicated to the study of the administrative and legal foundations for the implementation of the electronic notarial system in Ukraine. The author examines the process of transitioning from the traditional paper-based notarial system to a digital one, highlighting the benefits of increased efficiency, document security, and accessibility. The study focuses on the phased approach adopted by the government to implement the e-notary system, outlining key stages such as the creation of electronic registries, the development of a digital archive for notarial acts, and the introduction of a system for online notarial services. These developments represent a significant modernization of Ukraine’s notarial services. The author emphasizes the need for substantial legal reforms to support the full-scale digitalization of notarial services. It is highlighted that the Ukrainian Law on Notariate currently lacks explicit provisions for the use of digital technologies, which necessitates amendments to accommodate electronic notarial acts, remote notarization, and the integration of digital signatures. Additionally, the article discusses the challenge of aligning the e-notary system with European standards, especially regarding the cross-border recognition of electronic documents. The alignment of Ukrainian notarial laws with international frameworks is crucial for ensuring the system’s compatibility with global legal standards. The article identifies several key advantages of the e-notary system, including the automation of processes, which significantly reduces the time and administrative burden involved in notarial acts. Enhanced security measures, such as digital signatures and QR codes, provide protection against forgery and unauthorized alterations of notarial documents. Furthermore, the integration of the e-notary system with state databases facilitates efficient information exchange, thereby improving the accuracy and reliability of notarial services. These factors contribute to a more transparent and effective notarial system that can better meet the needs of modern society. Of particular note is the author’s focus on the importance of cybersecurity and data protection in ensuring the long-term success of the e-notary system. The article underscores the need for robust security measures to protect sensitive legal information from cyber threats, which are increasingly becoming a concern in the digital age. The author concludes by outlining prospects for further research, particularly in the areas of legal reform, public trust in digital notarial services, and the technological infrastructure needed to ensure the resilience and security of the electronic notarial system.

Malware Classification Using Few-Shot Learning Approach

Malware detection, targeting the microarchitecture of processors, has recently come to light as a potentially effective way to improve computer system security. Hardware Performance Counter data are used by machine learning algorithms in security mechanisms, such as hardware-based malware detection, to categorize and detect malware. It is crucial to determine whether or not a file contains malware. Many issues have been brought about by the rise in malware, and businesses are losing vital data and dealing with other issues. The second thing to keep in mind is that malware can quickly cause a lot of damage to a system by slowing it down and encrypting a large amount of data on a personal computer. This study provides extensive details on a flexible framework related to machine learning and deep learning techniques using few-shot learning. Malware detection is possible using DT, RF, LR, SVM, and FSL techniques. The logic is that these algorithms make it simple to differentiate between files that are malware-free and those that are not. This indicates that their goal is to reduce the number of false positives in the data. For this, we use two different datasets from an online platform. In this research work, we mainly focus on few-shot learning techniques by using two different datasets. The proposed model has an 97% accuracy rate, which is much greater than that of other techniques.

Data and IT Systems Security in Contemporary Organizations

ABSTRACT Data and IT systems security is a critical aspect and plays a significant role in protecting an organization’s business. Organizations must safeguard their information and assets to sustain their value and reputation. Security is essential to information systems because it concerns personal and confidential data belonging to users or companies. The study aims to indicate how enterprises in Poland guarantee the security of data and IT systems, as well as to estimate the complexity of this phenomenon. We have confirmed that small and medium enterprises (SMEs) organizations with a lower level of computerization generally invest fewer resources and possess less expertise in establishing and maintaining IT security policies and strategies.

Accounting Information Systems and Performance of Manufacturing Companies in Rwanda. Case of CIMERWA Plc

Purpose: The general objective of this study was to assess the effect of Accounting Information Systems (AIS) on the performance of manufacturing companies in Rwanda, focusing on a case study of CIMERWA Plc. Specifically, the study assessed the effect of Accounting Information Systems security on the performance of CIMERWA Plc, examined the effect of Accounting Information Systems accuracy on the performance of CIMERWA Plc and analysed the effect of Accounting Information Systems reliability on the performance of CIMERWA Plc. Methodology: The study was descriptive as well as correlational. A population of 157 from various departments within CIMERWA Plc. A sample size of 113 was determined by the researcher using Slovin's formula. Every member of the population has an equal chance of getting chosen when a simple random sampling is used. Questionnaire and interview guide all played a role in the data collection process. Within the scope of the research, SPSS 25 was used. Findings: The results indicate coefficient for AIS security is 0.307, suggesting that for each unit increase in AIS security, performance improves by 0.307 units, with this relationship being statistically significant. Similarly, the coefficient for AIS accuracy is 0.323, indicating that a one-unit increase in AIS accuracy corresponds to a 0.323 increase in performance, which is also statistically significant. Additionally, the coefficient for AIS reliability is 0.348, signifying those improvements in AIS reliability enhance performance by 0.348 units, with a statistically significant impact. Unique Contribution to Theory, Practice and Policy: The study recommends that CIMERWA PLC enhance AIS security through multi-factor authentication, employee training, and advanced technologies; improve AIS accuracy via audits, automated validation, and staff training; and strengthen AIS reliability through regular updates, user support systems, and feedback mechanisms to ensure seamless operations and optimal data management.

Towards Water Systems Security and Sustainability Using Deep Learning

Wastewater treatment plants (WWTPs) face significant challenges due to varying influent conditions, multiple operational constraints, and a constant lack of reliable datasets to manage and monitor water quality and flow using automated approaches. This paper introduces a novel framework showcasing soft sensors that are aimed at enhancing the sustainability and security of wastewater quality indicators using deep learning. We develop a trustworthy soft sensor that utilizes artificial intelligence (AI) approaches to provide nitrate NO3 predictions at the WWTP, as well as context-based evaluations to estimate overall predictive uncertainty. Contextual elements are injected into the model to allow for more accurate and relevant water quality monitoring, especially in different conditions (such as rain and snow). In addition, in this paper, we present a time-series Generative Adversarial Network (GAN), namely H2OGAN to address data scarcity and to improve model training by generating synthetic data that mirrors the statistical properties of water datasets from both controlled and real-world environments. Data in turn also train against data poisoning attacks on water supply systems, rendering these systems more secure. Our results indicate the potential uses of the integration of soft sensors and H2OGAN to significantly improve the operational efficiency of WWTPs by providing robust AI-driven tools for offering secure and sustainable water monitoring solutions.

Review of the opportunities and challenges to accelerate mass‐scale application of smart grids with large‐language models

AbstractSmart grids represent a paradigm shift in the electricity industry, moving from traditional one‐way systems to more dynamic, interconnected networks. These grids are characterised by their intelligent automation, robust structure, and enhanced interaction with customers, backed by comprehensive monitoring and data analytics. The key of this transformation is the integration of data‐driven methods into smart grids. Compared to previous big data solutions, large language models (LLMs), with their advanced generalisation abilities and multi‐modal competencies, are crucial in effectively managing and integrating diverse data sources. They address challenges such as data inconsistency, inadequate quality, and heterogeneity, thereby enhancing the operational efficiency and reliability of smart grids. Furthermore, at the system level, LLMs improve human–system interactions, making smart grids more user‐friendly and intuitive. Last but not the least, the structure of LLMs performs inherent advantages in bolstering system security and privacy, alongside in resolving issues related to system compatibility and integration. The paper reviews the data‐empowered smart grids and for the first time finds and proposes opportunities and future directions for adopting LLMs to accelerate the mass‐scale application of Smart Grids.

A Comprehensive Survey on Cyber-Physical System Security in the Internet of Things (IoT): Addressing and Solutions

Background: Cyber-Physical Systems (CPSs) integrate computing, control, and communication technologies, bridging cyberspace and the physical world to enhance critical infrastructure and safety-critical systems. Existing surveys often address CPS security from a single perspective, necessitating a more comprehensive approach. Methods: This paper presents a thorough review of CPS security from three perspectives: the physical domain, the cyber domain, and the cyber-physical domain. We examine attacks on physical components like sensors, cyber-attacks targeting CPSs, and integrated cyber-physical attacks. For each domain, we analyse corresponding detection and defence mechanisms. Results: Our review reveals that CPSs face significant security threats across all domains. In the physical domain, attacks on sensors can disrupt system operations, but various defences are available. In the cyber domain, CPSs are vulnerable to malware, hacking, and denial-of-service attacks, with several detection and defence strategies in place. The cyber-physical domain highlights complex threats where cyber-attacks cause physical damage, requiring comprehensive security approaches. Conclusion: By examining CPS security from multiple perspectives, this review provides a holistic understanding of current threats and defence mechanisms. It identifies future research directions to enhance CPS security, aiming to better protect critical infrastructure against evolving cyber threats.

Implementasi Program Badan Penyelenggara Jaminan Sosial (BPJS) Siap Membantu dalam Mengatasi Pengaduan Masyarakat

Public services in the government bureaucracy are an indicator of a country's progress. One concrete form of public service is health service, because good health service will improve the welfare of the people in a country. The BPJS SATU (BPJS Ready to Help) program is one of the innovations carried out by BPJS Health to make it easier for participants to get community complaint services at hospitals. In its implementation, the problems related to BPJS services are that there are still many people who do not know the health insurance procedures for health facilities, then there are still concerns from hospitals that BPJS will not pay patient arrears, and there are doubts about the security of the BPJS participant data system. So this research aims to examine further the implementation of BPJS SATU (Ready to Help) services in dealing with public complaints at Dr Soetomo Hospital, Surabaya City. The research method used in this research is a descriptive qualitative method with the focus of the research here being based on indicators of success in implementing the Edward III program, including communication, resources, disposition and bureaucratic structure. The results of research on communication focus show that communication has been created that creates inclusive services and focuses on achieving the goals of program implementation. The resource focus shows that this program has quality human resources and a supporting system within it. The disposition focus shows that BPJS Health is strongly committed to improving the quality of complaint handling in the BPJS SATU program. The focus on organizational structure has been running well as evidenced by the available procedures that can be carried out by participants and implementing officers.

Enhancing the SRAM PUF with an XOR Gate

This study focuses on designing enhanced Physically Unclonable Functions (PUFs) based on SRAM devices and improving the security of cryptographic systems. Most SRAM PUFs are limited in their number of CRPs, which makes them vulnerable to enrollment attacks. In this research, we present an SRAM-based PUF design that greatly increases the number of CRPs and the entropy of the generated bits by performing exclusive-or (XOR) on the responses of two SRAM devices. This was implemented using a readily available development board, SRAM devices, and a user-friendly custom circuit board for cryptographic key generation. The cryptographic protocol was implemented using both C++ and python3. The proposed SRAM PUF design was experimentally demonstrated and showed substantial improvements in the security of various cryptographic applications as a hardware authentication device. It also addresses the specific vulnerabilities of legacy designs.

Multi-parametric Analysis for Mixed Integer Linear Programming: An Application to Transmission Upgrade and Congestion Management

Upgrading the capacity of existing transmission lines is essential for meeting the growing energy demands, facilitating the integration of renewable energy, and ensuring the security of the transmission system. This study focuses on the selection of lines whose capacities and by how much should be expanded from the perspective of the Independent System Operators (ISOs) to minimize the total system cost. We employ advanced multi-parametric programming and an enhanced branch-and-bound algorithm to address complex mixed-integer linear programming (MILP) problems, considering multi-period time constraints and physical limitations of generators and transmission lines. To characterize the various decisions in transmission expansion, we model the increased capacity of existing lines as parameters within a specified range. This study first relaxes the binary variables to continuous variables and applies the Lagrange method and Karush-Kuhn-Tucker (KKT) conditions to obtain optimal solutions and identify critical regions associated with active and inactive constraints. Moreover, we extend the traditional branch-and-bound (B&B) method by determining the problem’s upper and lower bounds at each node of the B&B decision tree, helping to manage computational challenges in large-scale MILP problems. We compare the difference between the upper and lower bounds to obtain an approximate optimal solution within the decision-makers’ tolerable error range. In addition, the first derivative of the objective function on the parameters of each line is used to inform the selection of lines for easing congestion and maximizing social welfare. Finally, the capacity upgrades are selected by weighing the reductions in system costs against the expense of upgrading line capacities. The findings are supported by numerical simulations and provide transmission-line planners with decision-making guidance.

Exploring the ALNS method for improved cybersecurity: A deep learning approach for attack detection in IoT and IIoT environments

With the emergence of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), the flow of data across the world is experiencing a rapid expansion. Unfortunately, this exponential growth is accompanied by a proportional increase in cyber threats, jeopardizing the security and integrity of computer systems. In this context, intrusion detection becomes a necessity to protect networks and systems against potential attacks, ensuring their proper functioning and reliability. In this paper, we propose a deep learning-based model for attack detection. This model utilizes a convolutional neural network to train the datasets, which are first cleaned and preprocessed. The model inputs are selected using an optimization method called adaptive large neighborhood search. The results obtained for the four datasets used - CICIDS2017, Edge-IIoTset, ToN-IoT windows7, and ToN-IoT windows10 - demonstrate the model’s effectiveness for both multi-class and binary classification cases. In the binary case, the accuracy reaches 99.85%, 100%, 99.97%, and 100%, respectively, and in the multi-class case, it stands at 99.81%, 94.98%, 99.92%, and 99.84%, respectively.