Security Industry Research Articles

Ultra-Low Power Circuit Design Techniques for Audio-Based Environmental Sensing in IoT Networks

Abstract: ULP circuit design consideration is critical in enhancing AS-based ESL in IoT networks essential in designing IoT devices to work with minimal power consumption. These designs enable the sensors to operate continuously interesting environments while at the same time utilizing methods like duty cycling, adaptive sampling, and wake-up circuitries. AFE parts help to minimize pre-ADC signal manipulation within the hardware and true and near-threshold processing, as well as subthreshold processing, decreases the power dissipation even more without affecting utility. Energy harvesting including, solar and piezoelectric handling ensures kinetic energy is accumulated for efficient functioning without batteries. Furthermore, incorporating lightweight machine learning (ML) models allows for audio classification and anomalous event detection on the network edge and with low data transfer. In combination, these developments bring more efficient and resource-saving IoT networks providing essential data for applications in environmental and climate change sensing, smart cities, public safety, and industrial security. This approach is essential when building IoT systems that are to last, to meet the increasing need for intelligent, self-sustaining, energy-efficient systems

A Conceptual Blockchain-Based Framework for Secure Industrial IoT Remote Monitoring: Proof of Concept

A Conceptual Blockchain-Based Framework for Secure Industrial IoT Remote Monitoring: Proof of Concept

Variance‐driven security optimisation in industrial IoT sensors

AbstractThe Industrial Internet of Things (IIoT) has transformed industrial operations with real‐time monitoring and control, enhancing efficiency and productivity. However, this connectivity brings significant security challenges. This study addresses these challenges by identifying abnormal sensor data patterns using machine learning‐based anomaly detection models. The proposed framework employs advanced algorithms to strengthen industrial defences against cyber threats and disruptions. Focusing on temperature anomalies, a critical yet often overlooked aspect of industrial security, this research fills a gap in the literature by evaluating machine learning models for this purpose. A novel variance‐based model for temperature anomaly detection is introduced, demonstrating high efficacy with accuracy scores of 0.92 and 0.82 on the NAB and AnoML‐IOT datasets, respectively. Additionally, the model achieved F1 scores of 0.96 and 0.89 on these datasets, underscoring its effectiveness in enhancing IIoT security and optimising cybersecurity for industrial processes. This research not only identifies security vulnerabilities but also presents concrete solutions to improve the security posture of IIoT systems.

Smart Monitoring and Control System for Crude Oil Pipeline Vandalism

Crude oil pipeline vandalism, theft, and unauthorized interference present a significant challenge to the oil industry's security, profitability, and sustainability. Despite initial measures such as security patrols, traditional methods have proven inadequate, particularly in difficult terrains and aquatic regions. These methods often fail to promptly detect and prevent vandalism and theft, which results in significant economic, environmental, and safety risks. This study developed and implemented a smart monitoring and control system to address these challenges by improving the detection of oil pipeline vandalism, including incidents in aquatic environments. The Prototyping methodology was adopted, allowing for continuous testing and improvements throughout the development process. The system was built using C#.NET under the .NET Framework, with Microsoft SQL Server as the database, incorporating an encryption layer for database security. To enhance its effectiveness, an oil theft detection algorithm was integrated with a web-based interface to facilitate real-time monitoring, visualization, and reporting. Testing scenarios demonstrated that the system successfully detected threats and ensured seamless data flow from sensors to the central system. This solution marks a significant advancement in the security and sustainability of oil infrastructure, contributing to environmental protection and the safety of stakeholders

AI security in different industries: A comprehensive review of vulnerabilities and mitigation strategies

Artificial intelligence has quickly and gradually spread across all sectors and fields, achieving exceptional performance in operations and decision-making. Yet, as industries process their tasks with the help of AI systems, new threats appear that require proper solutions. In the following section of this paper, the use of security solutions involving AI in various industries, which are finance, health, manufacturing, and government, as well as a discussion of risks involving artificial intelligence in particular fields and the corresponding measures to be taken against them will be discussed. AI is often applied to detect fraud and credit scoring in the financial sector but has potential risks, such as adversarial attacks and data manipulation. Similarly, the healthcare industry uses AI for predictive healthcare diagnostics and patient information protection; threats are data theft and adversarial examples attacking diagnostic models. In manufacturing, AI individuals contain predictive servicing and excellent control, though they are constantly exposed to industrial espionage and the sabotage of AI models. In retail, however, AI is used to improve marketing and predict consumer behavior. While these benefits exist, questions about using Algorithms that could be biased and privacy infringements continue to raise their ugly head. Defensive and governmental organizations that apply AI for surveillance or automatic operating systems are most vulnerable to adversarial intervention with key control systems and innate security systems. To counter such threats, this paper provides details of industry-specific measures, including adversarial training data sanitization, AI model audit, and privacy-preserving approaches. We illustrate examples linked to every one of these methods to show how they can be deployed in a variety of industries and to guard AI systems. To this end, the following review of the relationship between AI and cybersecurity demonstrates that security measures that rely on artificial intelligence must be regularly checked and updated. This reveals the call for proper, protective, and industry-specific measures to manage the risks and protect AI applications to ensure that the world’s modern, interconnected world is safe for AI.

Enhancing Surveillance Systems with Cost-Effective Ultrasonic Radar for Object Detection and Tracking

Object detection is critical in surveillance systems because it enables timely identification of unauthorized objects, which is essential for maintaining security and mitigating risks. Existing approaches to surveillance typically suffer from limited coverage, variable detection range, moderate accuracy, high cost, and complex setup requirements. This article presents an approach to object detection using microcontroller-based ultrasonic sensors. Our approach leverages four distinct processing phases to create a radar-like system capable of real-time detection and autonomous decision-making. In this study, we conducted experiments by integrating more than six peripheral modules with the Arduino platform. By evaluating 17 diverse test cases in various scenarios and environments, the approach demonstrated enhanced object tracking and robustness compared to existing methods. The system effectively detects objects within the targeted area, providing precise distance measurements (around 50 cm) and position information (0 to 360 degrees). Moreover, our approach enabled the identification of three risk zones—medium, high-risk, and mild danger—within the critical region, contributing to improved security and risk management. The results reveal the system’s effectiveness in real-time detection, accurate distance estimation, and comprehensive risk assessment. Overall, our study significantly contributes to the advancement of object detection technology by effectively addressing the limitations of existing methods and offering a cost-effective and practical solution. Our research has the potential to monitor objects in surveillance systems across various domains, with significant implications for enhancing security, risk mitigation, and monitoring in industries reliant on effective object detection systems. Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 157−171

A Survey of Joint Security-Safety for Function, Information and Human in Industry 5.0

Industry 5.0 blows the whistle on a new industrial revolution, aiming to refocus industrial development by reintegrating the human factor into the technological equation. On the eve of the revolution, the comprehensive surveys for Industry 5.0 would provide important support for future development. However, current surveys for Industry 5.0 are still in their infancy and some gaps remain. (i) Current work lacks a comprehensive technical architecture for Industry 5.0 and an in-depth analysis of the enabling technologies that will drive Industry 5.0; (ii) There is no comprehensive survey on security issues of Industry 5.0, which will directly hinder its development; (iii) As Industry 5.0 introduces people into the technological equation, then it will also further consider the broader human interests in its security equation. It is an open issue that traditional security classifications cannot summarize these new security threats in Industry 5.0. Therefore, this survey starts by reviewing the latest key enabling technologies and proposing an overall technology hierarchical structure for Industry 5.0. Second, we investigate the triad of security issues in Industry 5.0, which includes threats and countermeasures for functional safety, information security, and humanized security. Among them, we define the third security issue in Industry 5.0, humanized security, which includes safeguarding the broader interests and rights of individuals, machines, and society. Finally, we summarize future challenges and research trends. To the best of our knowledge, this is the first comprehensive overview of security in Industry 5.0, in which humanized security is defined for the first time.

A study on the backtesting and correlation of systemic risk in Chinese financial institutions: an empirical analysis based on MES and Delta CoVaR

Effectively measuring the risk level of financial institutions is the key to preventing and defusing systemic risks. This paper uses the unconditional coverage test method to backtest the systemic risk of 35 listed financial institutions in China, and uses the quantile coherence method to construct a quantile frequency domain correlation network to explore the frequency domain mechanism of systemic risk correlation of financial institutions. The study found that: (1) During the financial market crisis, the Delta CoVaR commonly used systemic risk measurement indicators such as MES, CoVaR, and failed to effectively measure the systemic risk of China's financial institutions. (2) Compared with the short-term correlation layer and the medium-term correlation layer, China's financial institutions have obvious extreme risk linkage effects in the long-term correlation layer.(3) In the short-term, medium-term and long-term correlation layers, small and medium-sized financial institutions such as Ningbo Bank, Industrial Securities and Dongfang Energy all have high correlation importance. Relevant departments should pay attention to preventing the risk of "too correlated to fail" of small and medium-sized financial institutions in my country.

TOGAF's Approach in Developing an Enterprise Architecture for the Information Technology Security Industry

The information technology security industry, encompassing various activities such as risk identification and assessment, policy development, and solution implementation, plays a crucial role in maintaining the integrity and security of information systems. This study aims to develop and implement an efficient and effective enterprise architecture within the information security sector, focusing on three key core processes identified as the major revenue contributors: risk identification and assessment, security policy development, and security solution implementation. Utilizing the TOGAF-based Enterprise Architecture framework, this research identifies and designs architecture that integrates various systems, applications, and business processes, facilitating better alignment within the organization. The architecture design process involves a thorough analysis of operational needs and business strategies, leading to the development of a model that enhances efficiency and reduces the risk of failure in technology implementation. The outcomes of this study are intended to provide practical guidance for information security companies to optimize operations, simplify system complexities, and achieve strategic goals more effectively. It is anticipated that the application of the designed architecture will have a significant positive impact on the company's ability to address challenges and dynamic needs within the information security industry.

Reinforcement learning in autonomous defense systems: Strategic applications and challenges

Reinforcement learning (RL) is one of the most progressive ways to improve the efficiency of drones and robotic systems in the context of the defense and security industries. In Anomaly Detection (AD) systems, this paper discusses the potential of RL as a Model Selection Criteria (MSC) method and its associated issues. RL algorithms enable such systems to adapt to and learn from interactions with their environment, optimizing their response to complex and dynamic threats. This way, new states may be introduced with better decision-making processes, improving the effectiveness of the operations and enabling Reinforcement learning -powered systems to learn new scenarios as well as perform detailed defensive actions in a real-time context. The importance of RL in national defense can therefore be said to lie in its capacities for changing how threats are identified, how threats are responded to, and even what strategies of defense are thought of. Self-governing systems that incorporate Reinforcement learning are capable of functioning in an unpredictable environment, evaluating threats correctly, and carrying out defensive measures with the help of people practically at all. This versatility is critical in modern warfare because a preferred option is the right response to threats that were unknown a few years ago. However, incorporating RL into autonomous defense systems is not without big challenges. Some important problem areas are improved stability and accuracy of RL algorithms in various and critical situations, the legal and practical consequences of autonomous decision-making and possible threats that can arise due to adversarial manipulation of learning algorithms. In addition, such systems have to be developed and put into practice under national and international standards to meet certain requirements and build confidence in such applications. This paper explores these strategic uses and issues and presents a detailed overview of how RL can improve independent defense functionalities, as well as the significant problems relating to this approach. Thus, by providing case studies and theoretical analysis, it aims to demonstrate that RL has the potential to define the technological development of defense and benefit national security systems in the context of a growing threat.

A multi-step mathematical model-based predictive strategy for software release timing during testing stage

Mathematically precise modeling is important to be established to accurately examine the quantitative relationship between software testing and software reliability. Software testing process is complex since it is concerned with various factors such as test case execution, defect debugging, tester expertise, test case selection, and so forth. For this reason, it is required to be meticulous in formulating the software testing process in a manner which is mathematically concise. The software release life cycle or sequential release timeline, referring to the process related to the development, testing and distribution of a software product comprises several critical stages, and the length of this particular life cycle reveals variations depending on different factors like the type of product, the intended use of it, industry security, general standards and compliance. One consideration software engineers have is related to the release date of the software so that future commitments about the software’s release time can be formulated beforehand. In view of these aspects, a multi-step strategy for predicting software release dates is proposed in the current study along with the following stages: firstly, the proposed technique selects the utmost reliability growth model that very well fits the observed test data halfway through the testing period, and then employs it to forecast the probable date of release. This technique entails approximating the unknown parameters of suitable Software Reliability Growth Models (SRGMs). Finally, the chosen SRGM is used to forecast the release date of the software under test by fitting it to available fault data. The proposed method is straightforward and applied to test on a total of ten actual datasets collected from the literature. The results of the proposed technique reveal that in the majority of the situations, nearly exact approximation of date of release can be made halfway through the testing period. Moreover, the proposed method’s performance is also compared to that of a number of previous strategies present in the literature. The outcomes obtained by our study demonstrate that the proposed strategy may be used to forecast the release date of software in practical situations.

Digital Twin and IoT-Enabled Online Learning Model for Real- Time Monitoring and Control of Fraudulent Transaction in the Banking industry

Fraudulent transactions have long been a challenge in the banking industry, causing significant financial losses and operational burdens. Traditional fraud detection systems often struggle to adapt and respond in real-time to the evolving tactics of fraud. This study aims to develop a digital twin and IoT-enabled online learning model for real-time monitoring and control of fraudulent transactions in banking. The proposed system integrates digital twin technology and IoT sensors to create a virtual replica of transaction environments, enabling continuous learning and adaptation using an online learning model. Performance metrics such as detection speed, prediction accuracy, precision, recall, and F1 score were used to evaluate the system. The system was tested using a dataset of banking transactions, including legitimate and fraudulent activities. Key results include detecting fraudulent transactions 40% faster than traditional methods, reducing detection time from 5 minutes to 3 minutes. The model achieved an accuracy of 92%, an 8.24% improvement over traditional fraud detection systems. The false positive rate was reduced by 28%, meaning fewer legitimate transactions were wrongly flagged as fraudulent. Precision increased by 5.95%, reaching 89%, and recall improved by 8.75%, reaching 87%. This led to a 7.32% increase in the F1 score, reaching 88%. The system offers enhanced real-time fraud detection capabilities, reducing false positives and improving overall security and operational efficiency in the banking industry.

ENSURING ECONOMIC SECURITY: COMPARISON OF EU MEMBER STATES AND UKRAINE

The purpose of this paper is to conduct a comprehensive study of economic security in the EU Member States and Ukraine, to identify differences and to propose targeted policy interventions based on these differences. Methodology. The study uses a quantitative strategy that assigns numerical ratings to countries based on their performance against predefined thresholds for various economic security indicators. These indicators include industrial security, demographic security, energy security, foreign trade security, investment and innovation security, macroeconomic security, food security, social security and financial security. The study uses data from a variety of sources and applies a ranking system to reflect the level of economic security in each category. Results. The analysis revealed significant differences in the levels of economic security between EU Member States and Ukraine. The main findings include differences in agricultural efficiency, healthcare system efficiency, renewable energy consumption, trade balance, research and development expenditure, and socio-economic factors. For example, countries such as Belgium and Ireland have high yields, while Cyprus and Romania have much lower yields. Life expectancy and the quality of healthcare also vary: Spain and Sweden lead the way, while Bulgaria and Ukraine lag behind. Practical implications. The findings suggest the need for targeted policies and technological advances to improve economic security. For the younger generation, marketing strategies based on brand equity and exclusivity can continue even during economic downturns. For the older generation, strategies that focus on value for money or lower prices may be more effective. Understanding these metrics can help businesses align their strategies with national economic priorities and identify potential areas for investment and growth. Value / Оriginality. This study contributes to the existing literature by providing a detailed comparative analysis of economic security in the EU Member States and Ukraine, filling a gap in the understanding of the multifaceted aspects of economic security in these regions.

New era of information security in industry: challenges, threats, solutions

New era of information security in industry: challenges, threats, solutions

Evaluation of Information Technology Governance Maturity Using COBIT 2019: A Case Study on the IT Security Industry

This study aims to evaluate the maturity of IT governance in the IT security industry using COBIT 2019. The assessment covered 13 COBIT 2019 domains, namely APO03—Managed Enterprise Architecture, APO07—Managed Human Resources, APO12—Managed Risk, APO13—Managed Security, APO14—Managed Data, BAI02—Managed Requirements Definition, BAI03—Managed Solutions Identification & Build, BAI05—Managed Organizational Change, BAI06—Managed IT Changes, BAI07—Managed IT Change Acceptance and Transitioning, BAI09—Managed Assets, BAI10—Managed Configuration, and BAI11—Managed Projects. The research methodology included observation, domain-based question formulation, RACI interviews, data collection, and question validation testing, with maturity calculation performed using appropriate formulas. Results indicate that most domains are at Level 2 (Managed), with significant contributions to maturity at Levels 3 and 4. Significant gaps were found between the current state and the desired maturity targets for many domains, such as APO03 and BAI03. The percentage contribution from Level 2 is the highest, while contributions from Levels 3 and 4 vary, with very low contributions from Level 5. The total maturity score is 2.49, with percentage contributions from Levels 2, 3, 4, and 5 being 74%, 26%, 11%, and 3%, respectively. Recommendations include improving processes to achieve Levels 3 and 4 across more domains and investing in training and development for relevant teams.

산업보안을 위한 LMX의 질이 산업보안정책 준수의지에 미치는 영향 : 조직몰입의 매개효과

산업보안을 위한 LMX의 질이 산업보안정책 준수의지에 미치는 영향 : 조직몰입의 매개효과

Cyber Security and Artificial Intelligence in Aviation

Airports are becoming increasingly interconnected and technologically advanced ecosystems. Airports handle sensitive data and depend on interconnected systems, which, while improving efficiency, also heighten vulnerability to cyber threats like data breaches and ransom ware. Additionally, with technological advancements artificial intelligence also plays a huge role, providing a new definition to cyber security in aviation. This paper examines the current state of cyber security in the aviation industry, implementation of artificial intelligence and how we can make the best use of technology, whilst focusing on the vulnerabilities and threats that accompany the adoption of advanced technologies such as Internet of Things (IoT) and Supervisory Control and Data Acquisition (SCADA) systems. The paper also explores the types of cyber attacks that can target smart airport systems, emphasising the need for robust cyber security measures to protect these critical infrastructures.

Consistent representation joint adaptive adjustment for incremental zero-shot learning

Zero-shot learning aims to recognize objects from novel concepts through the model trained on seen class data and assisted by the semantic descriptions. Though it breaks the serious reliance on training data, it still fails to deal with the sequential streaming data in the open world. In this paper, we focus on the incremental zero-shot learning (IZSL) where the seen data arrives in the form of task sequence. In each incremental task, we only have access to the seen data of current task. The IZSL methods aim to depict the characteristics of current seen classes and avoid forgetting the previous ones, in the meantime, learn to generalize to the unseen classes. We summarize the challenges in IZSL as semantic drift which is further divided into task-recency bias and seen-class bias. To solve these issues, we propose a novel IZSL method termed as CRAA. Specifically, CRAA constructs consistent representations with satisfactory discrimination and generalization for all the seen and unseen classes. Based on these representations, CRAA learns a prototype classifier with a novel adaptive adjustment strategy to alleviate the task-recency bias and seen-class bias. Note that CRAA only needs a limited memory footprint to store the fixed scale model, and meets the demands of both memory restriction and data security in industry. We have conducted extensive experiments to evaluate our method on three widely used datasets. The results prove our method is superior to all the compared methods with significant improvements. Code is available at: https://github.com/changniu54/CRAA-Master.

Study on Information Security Industry Efficiency Measurement Based on DEA-GRA

Study on Information Security Industry Efficiency Measurement Based on DEA-GRA

Size matters: measuring the private security industry in the United Kingdom

The private security industry has become a central feature of the plural policing landscape in the United Kingdom. As such, it has received considerable attention in both academic and policy circles. Yet, perhaps surprisingly, there remain notable question marks over its precise size and shape. With this in mind, the article draws upon data from the Security Industry Authority and the Office of National Statistics Business Register and Employment Survey to measure the sector along three key dimensions: the number of private security officers, the number of companies and turnover. This novel methodological approach reveals a complex and contested picture of what the sector looks like at a fundamental level and, at the same time, allows for a reassessment of commonly deployed indicators such as growth rates and the ratio of private security officers to police officers during key time periods such as the Covid-19 pandemic.