Security Issues Research Articles

Architecture for Enhancing Communication Security with RBAC IoT Protocol-Based Microgrids.

In traditional power grids, the unidirectional flow of energy and information has led to a decrease in efficiency. To address this issue, the concept of microgrids with bidirectional flow and independent power sources has been introduced. The components of a microgrid utilize various IoT protocols such as OPC-UA, MQTT, and DDS to implement bidirectional communication, enabling seamless network communication among different elements within the microgrid. Technological innovation, however, has simultaneously given rise to security issues in the communication system of microgrids. The use of IoT protocols creates vulnerabilities that malicious hackers may exploit to eavesdrop on data or attempt unauthorized control of microgrid devices. Therefore, monitoring and controlling security vulnerabilities is essential to prevent intrusion threats and enhance cyber resilience in the stable and efficient operation of microgrid systems. In this study, we propose an RBAC-based security approach on top of DDS protocols in microgrid systems. The proposed approach allocates roles to users or devices and grants various permissions for access control. DDS subscribers request access to topics and publishers request access to evaluations from the role repository using XACML. The overall implementation model is designed for the publisher to receive XACML transmitted from the repository and perform policy decision making and enforcement. By applying these methods, security vulnerabilities in communication between IoT devices can be reduced, and cyber resilience can be enhanced.

3D Lightweight Cryptosystem Design For IoT Applications Based On Composite S-Box

The security of the Internet of Things (IoT) depends on strong cryptographic functions that require extensive computation and resources. The security of Internet of Things (IoT) relies on the use of strong cryptographic functions that demand extensive computation and resources. Therefore, the selection of a cryptographic system is influenced by the computational and communicational capabilities of IoT devices, including their energy requirements, memory constraints, and execution time. This paper aims to develop a lightweight and secure encryption algorithm for IoT applications, focusing on advancing cryptanalysis techniques. We propose a new lightweight algorithm named enhanced 3D RECTANGLE, designed to deliver robust security for IoT applications and optimized for cell phones with minimal memory usage, low power consumption, and efficient performance. The RECTANGLE block cipher was chosen for this research due to its high efficiency and speed relative to other lightweight algorithms, despite some security issues. The proposed enhanced 3D RECTANGLE algorithm improves confusion and diffusion properties through a new 3D array block rotation method for 4x4 plaintext, based on a 128-bit key and 16 rounds. Additionally, we designed a 4x4 composite S-Box with a Galois field pipelining structure, offering a more advanced solution compared to the Look-Up Table method. The cryptanalysis, avalanche effect, and bit error rate tests were conducted to verify the security strength of the proposed algorithm. The proposed algorithm was evaluated against two existing algorithms, RECTANGLE and Extended 3D RECTANGLE. The enhanced 3D RECTANGLE algorithm provides better cryptanalysis, demonstrating a 40% avalanche effect and achieving a bit error rate (BER) of 31%, indicating its greater effectiveness compared to the other two lightweight algorithms.

Application and Evaluation of the Effectiveness of Artificial Intelligence in Personalized Learning

The application of artificial intelligence (AI) in personalized learning has demonstrated remarkable advantages, including intelligent assessment, resource recommendation, and teaching optimization, effectively enhancing learning outcomes and engagement. However, this process has also revealed several issues. Firstly, technical challenges and limitations hinder AI's ability to fully adapt to each student's individual needs, requiring further improvements in algorithm accuracy and reliability. Secondly, data privacy and security issues have become increasingly prominent, necessitating robust measures to protect student data from misuse and leakage. Lastly, challenges in the educational system and mindset must be addressed, focusing on how to integrate AI technology into traditional education models while maintaining teaching quality. To address these issues, continuous research and development, enhanced data protection, and the updating of educational systems and mindsets are required to fully harness the potential of AI in personalized learning.

Enhancing Public Safety: A Comprehensive Analysis of Crime Vision Strategies

There is an abnormal increase in the crime rate and also the number of criminals are increasing, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is limited. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. This Real time criminal identification system based on face recognition works with a fully automated facial recognition system. HAAR feature-based cascade classifier and OpenCV LBPH (Local Binary Pattern Histograms) Algorithms are used for Face detection and recognition. This system will be able to detect face and recognize face automatically in real time. An accurate location of the face is still a challenging task. This Framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. The automatically tagging feature adds a new dimension to sharing pictures among the people who are in the picture and also gives the idea to other people about who the person is in the image. In our project, we have studied and implemented a pretty simple but very effective face detection using CNN algorithm which takes human skin color into account. Our aim, which we believe we have reached, was to develop a system that can be used by police or investigation department to recognize criminal from their faces. The method of face recognition used is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and technique.

Space Security and the Law of International Space Cooperation: The Need for Leadership and Coordination

In recent times space exploration and technological advancements hold prominent importance, the merger of space security and international collaboration must be paid attention too. Whereby, during the recent advancements in space technologies which include the proliferation of satellite and emergence of various space programs, the non-commercial and sustainable utilization of space must be taken care of. This paper explores and access the methodologies adopted and put into motion by nation having a well-established space program to address the difficulties faced while maintaining the space security along with ongoing space explorations. This research study will also pay emphasis towards analysing the already existing legal framework which directly governs various outer space activities, international organizations, role of governments and private industry players towards shaping of governance in outer space. In order to build trust and furthering the shared objectives in outer space and resolution of disputes among nation states, emphasis on how significant and valued international cooperation and diplomacy can prove to be. The various risks factors such as deliberate actions which are testing of anti-satellite weapons and dispersion of space debris in high volumes that the assets in space encounter are explored in this research. This study also discusses about case studies that have impacted the outer space and space explorations and other space missions positively as well as negatively in past instances. This study concludes the necessity of proactive leadership and teamwork in addressing the issue of space security and the legislation of international space cooperation. Roles of significant stakeholders, implementation of future course of actions with necessary changes to guide and provide policymakers in understanding the complexity of space reforms for peaceful utilization of outer space for both current and future human race.

Public Perception of the Food Estate Program of Cassava Cultivation as a Strategic Logistic Reserve in Realizing Regional Food Security in Gunung Mas Regency, Central Kalimantan

This research examines the implementation challenges of Indonesia’s Strategic Logistics Reserve Development Program (CLS), specifically focusing on cassava cultivation in Gunung Mas Regency. Launched as part of the 2020-2024 National Strategic Program (PSN), the initiative aims to address potential food security issues exacerbated by the COVID-19 pandemic, land degradation, and climate change. The program's primary goal is to develop food estates to enhance national food security. The study employs a mixed-methods approach, including interviews with key stakeholders, analysis of program documentation, and community surveys to assess the program’s effectiveness and impact. Key findings reveal significant obstacles, including policy misalignment, unsuitable land conditions for cassava, inadequate infrastructure, and a top-down implementation strategy that lacks local community involvement. Results indicate that while the program has led to some positive outcomes, such as job creation and increased food independence, it has also faced severe criticism. Issues such as suboptimal cassava growth, environmental damage, and socio-cultural impacts have led to negative public perceptions. The failure to issue a crucial Presidential Decree, poor inter-governmental coordination, and inadequate understanding of the program by local communities have compounded these challenges. Analysis suggests that the program’s shortcomings are due to a combination of regulatory delays, environmental mismatches, and insufficient stakeholder engagement. To address these issues, the research recommends a comprehensive evaluation of the program, restoration of damaged lands, enhanced community participation, and stronger environmental safeguards. Improved collaboration between central and regional governments and better alignment of policies and infrastructure are essential for the program’s success in achieving its food security objectives.

Designing an Entrepreneurial Model of Remote Communication Technologies in Clinical Medicine: A Qualitative Study

Background: Today, information technology has permeated all fields, transforming numerous aspects of our lives, while entrepreneurship has become increasingly essential. In medicine, the integration of this technology is becoming crucial. Objectives: This research focuses on designing an entrepreneurial model that leverages modern communication and information technologies in the health sector. By capitalizing on created opportunities, entrepreneurs can drive innovation, leading to cost savings, time efficiency, and greater access to health and medical services for people everywhere. Methods: In this qualitative study, the sample included faculty members from Iran University of Medical Sciences, selected through purposive sampling. Following this, the sample was expanded using snowball sampling, introduced through key experts, to include additional informed individuals. Data collection was conducted through semi-structured interviews with faculty members, experts, and knowledgeable individuals who were willing to participate. The interviews continued until data saturation was reached. Data analysis was carried out using content analysis, with coding performed using MAXQDA10 software. Results: After coding and identifying the main and sub-themes, content analysis was completed, resulting in 10 main themes and 57 sub-themes. All interviewees highlighted sub-themes such as "infrastructure and hardware," "internet bandwidth," "access to electronic health records," "equipment and supplies," "data privacy," "qualified physicians," and "counseling." Conclusions: When designing an entrepreneurial model in any region or country, it is essential to consider the existing needs and conditions, environmental demands, population characteristics, culture, habits, preferences of service recipients, infrastructural capabilities, governing laws, and security issues. Additionally, the model must incorporate specific leadership, management, and oversight to ensure it becomes a responsive and effective system.

A Multi-Scale Temporal Feature Extraction Approach for Network Traffic Anomaly Detection

With the rapid advancement of network technology and the expanding scale of the Internet, network traffic data has grown exponentially, leading to increasingly prominent issues in network security. Many researchers are developing new network traffic anomaly detection models, but these models are difficult to effectively capture the multi-scale temporal characteristics of network traffic data and learn the correlation and importance among various feature dimensions. To this end, we propose a multi-scale temporal feature network (MSTFN-AM) based on an attention mechanism. MSTFN-AM integrates original data with temporal information through the temporal position encoding module and extracts multi-scale temporal features through the temporal feature extraction module. The temporal self-attention module can effectively identify dependencies and correlations between different time points. Ultimately, experiments conducted on two public datasets demonstrate that the MSTFN-AM model outperforms all baseline models in prediction performance.

Survey of fault management techniques for edge-enabled distributed metaverse applications

The metaverse, envisioned as a vast, distributed virtual world, relies on edge computing for low-latency data processing. However, ensuring fault tolerance – the system’s ability to handle failures – is critical for a seamless user experience. This paper analyzes existing research on fault tolerance in edge computing over the past six years, specifically focusing on its applicability to the metaverse. We identify common fault types like node failures, communication disruptions, and security issues. The analysis then explores various fault management techniques including proactive monitoring, resource optimization, task scheduling, workload migration, redundancy for service continuity, machine learning for predictive maintenance, and consensus algorithms to guarantee data integrity. While these techniques hold promise, adaptations are necessary to address the metaverse’s real-time interaction requirements and low-latency constraints. This paper analyzes existing research and identifies key areas for improvement, providing valuable research guidelines and insights to pave the way for the development of fault management techniques specifically tailored to the metaverse, ultimately contributing to a robust and secure virtual world.

An enhanced Deep-Learning empowered Threat-Hunting Framework for software-defined Internet of Things

The Software-Defined Networking (SDN) powered Internet of Things (IoT) offers a global perspective of the network and facilitates control and access of IoT devices using a centralized high-level network approach called Software Defined-IoT (SD-IoT). However, this integration and high flow of data generated by IoT devices raises serious security issues in the centralized control intelligence of SD-IoT. Motivated by the aforementioned challenges, we present a new Deep-Learning empowered Threat Hunting Framework named DLTHF to protect SD-IoT data and detect (binary and multi-vector) attack vectors. First, an automated unsupervised feature extraction module is designed that combines data perturbation-driven encoding and normalization-driven scaling with the proposed Long Short-Term Memory Contractive Sparse AutoEncoder (LSTMCSAE) method to filter and transform dataset values into the protected format. Second, using the encoded data, a novel Threat Detection System (TDS) using Multi-head Self-attention-based Bidirectional Recurrent Neural Networks (MhSaBiGRNN) is designed to detect cyber threats and their types. In particular, a unique TDS strategy is developed in which each time instances is analyzed and allocated a self-learned weight based on the degree of relevance. Further, we also design a deployment architecture for DLTHF in the SD-IoT network. The framework is rigorously evaluated on two new SD-IoT data sources to show its effectiveness.

Analysis of Key Technologies for Data Security Protection Based on Cloud Computing

With the rapid development of cloud computing, more and more enterprises and institutions are migrating their data and business operations to the cloud. However, this shift has brought data security issues in cloud environments to the forefront. This paper primarily investigates the key technologies for data security protection in cloud computing environments. It begins by introducing the basic concepts and architecture of cloud computing and analyzing the fundamental concepts and threats related to data security protection. The paper then delves into critical technologies such as data encryption, access control, data storage and backup security, and data privacy protection, detailing the algorithmic principles and implementation methods of these technologies. Additionally, the paper explores the challenges faced by current cloud computing data security protection technologies and their corresponding solutions, analyzing the effectiveness of these technologies through practical case studies. Finally, the paper forecasts the future development trends of cloud computing data security technologies, particularly the potential impact of emerging technologies on data security protection. This research aims to provide theoretical and technical support for data security protection in cloud computing environments.

Data Security Issues in Intelligent Cloud Computing Systems

This project takes the intelligent cloud computing system as the main research object. It mainly uses the literature analysis method to explore the data security problems in the intelligent cloud computing system. By investigating the development background of intelligent cloud computing, the author summarizes the definition of cloud computing and the characteristics of cloud computing. The author also makes a specific analysis of the advantages of cloud computing, and concludes that cloud computing is possible through safe and reliable data, low demand for clients, shared cloud data and unlimited possibilities. These four points are the main reasons major enterprises choose cloud computing to replace traditional computing resources. In the topic, the author studied the data security problems in cloud computing from three aspects: the problems exposed in the development process of cloud computing, the effective measures to protect data security in cloud computing and predicting the future development trend of improving data security. The authors believe that future research should focus on a balance between network security issues in cloud computing and building a secure and reliable data system.

ReckDroid: Detecting red packet fraud in Android apps

Recently, red packets have appeared widely in various mobile apps. Related security issues like fraud are gradually coming into the public eye. As a new means of fraud, red packet fraud has not yet been explored or addressed. In this paper, based on our empirical study on red packets, we propose a novel approach ReckDroid for red packet fraud detection. Our approach adopts a heuristic algorithm to identify red packets and then detects red packet fraud by analyzing the network traffic dynamically generated during the automated exploration of mobile apps. Our experiments are performed on hundreds of labeled real-world apps. Experimental results show that ReckDroid identifies red packets with a precision of 98.0% and a recall of 93.3%, and detects red packet fraud with a precision of 88.6% and a recall of 92.5%. By applying ReckDroid to over 1000 Android apps in the wild, we find that apps with red packets account for 17.6% of apps from seven app markets (including Google Play) while red packet fraud mainly occurs in Chinese app markets.

A Review on Power System Security Issues in the High Renewable Energy Penetration Environment

A Review on Power System Security Issues in the High Renewable Energy Penetration Environment

Food security in the story of the Prophet Ibrahim in the Holy Qur’an - A Contemporary Reading

The idea of this article appears in analyzing the positions related to food security in the story of Prophet Ibrahim in the Holy Quran, highlighting its importance in reaching the overall landmarks related to food security in the Quranic approach, and the practical realistic applications associated with it. The article examines the issue of food security in the story of Prophet Ibrahim in verses from Surahs: Al-Baqarah, Hud, Ibrahim, and Adh-Dhariyat, as well as comparing between the stories of Prophet Ibrahim and Prophet Yūsuf in the same matter. This article has yielded several results, including the contexts that included food security positions in the story of Prophet Ibrahim in the Holy Quran being confined to the context of Allah's selection of Prophet Ibrahim, the call to monotheism and establishing religion, the context of honoring guests, and the centrality of the issue of food security in the Holy Quran. Therefore it has been indicated by its diverse presence in Meccan and Medinan Surahs, and the integrated concept of achieving food security in the stories of the prophets in the Holy Quran; it has included the belief aspect, the ethical aspect represented in some positions in the story of Prophet Ibrahim, and the practical aspect represented in the story of Prophet Yūsuf.

A framework for evaluating the barriers to adopting Industry 4.0 in Indian SMEs: an approach of best-worst method

This study aims to highlight the need for Industry 4.0 in a manufacturing system and explore the importance of the barriers to adopting Industry 4.0 technologies in Indian SMEs. Many barriers to implementing Industry 4.0 were explored through a literature review. These barriers are prioritized using the Best-Worst Method (BWM). The framework has illustrated the approach to exploring the barriers and ranking these barriers based on feedback from industry experts. Some of the barriers such as lack of infrastructure, lack of financial resources, lack of government initiatives, high complexity, and cyber security and data ownership issues are observed to be very influential in SMEs to adopting Industry 4.0. The proposed framework can also be used in other industries for implementing Industry 4.0 technologies. Prioritizing and overcoming the barriers step-by-step may help the manager to digitalize the systems.

(De)politicization of the environmental agenda in Russian media

In the early 2020s, climate change and environmental challenges became increasingly important issues in the Russian political discourse. This was followed by growing media interest in environmental and climate reporting. This article explores how environmental journalists in Russian media outlets make sense of and discursively frame the environmental agenda. Based on in-depth interviews with environmental journalists working in the Russian media inside the country and in exile, we explore the role of the environmental agenda in a authoritarian regime such as Russia’s. The research reveals the forces shaping the environmental agenda and journalists’ perceptions of it. Drawing on theoretical perspectives on the politicization and depoliticization of the environmental debate, we explain how the prevailing forces shaping the environmental agenda lead to the depoliticization of environmental beats. As a result, some topics are treated as purely scientific problems that do not affect society or the state at large. On the other hand, regional environmental issues that do not affect state security issues allow for freer and more investigative reporting.

Immigration and Its Impact on Europe's Societal Security: Examining the Rise of Far-Right Parties (2014-2024)

This study examines the relationship between immigration and the rise of far-right parties in Europe from 2014 to 2024, focusing on the implications for societal security. It explores how far-right parties have capitalized on immigration, framing it as a critical threat to national identity and social stability. Applying the Copenhagen School’s securitization theory, the research analyzes how these parties have redefined immigration as a national security issue, thereby legitimizing more stringent immigration policies and exclusionary practices. The findings indicate that far-right parties have effectively utilized public anxieties and uncertainties surrounding immigration to influence the political discourse across Europe. This shift has significant socio-political consequences, posing challenges to the European Union’s foundational principles of integration, democracy, and human rights. The study highlights concerns over democratic backsliding, the erosion of social policies, and threats to freedom of movement and human rights. Additionally, it examines the potential impacts on the EU’s cohesion policy, foreign policy, enlargement process, and environmental sustainability. The research underscores a major transformation in Europe’s political landscape with far-reaching implications for the stability of the European Union and its member states.

Applications of blockchain technology in privacy preserving and data security for real time (data) applications

SummaryBlockchain (BC) technology has been incorporated into the infrastructure of different kinds of applications that require transparency, reliability, security, and traceability. However, the BC still has privacy issues because of the possibility of privacy leaks when using publicly accessible transaction information, even with the security features offered by BCs. Specifically, certain BCs are implementing security mechanisms to address data privacy to prevent privacy issues, facilitates attack‐resistant digital data sharing and storage platforms. Hence, this proposed review aims to give a comprehensive overview of BC technology, to shed light on security issues related to BC, and to emphasize the privacy requirements for existing applications. Many proposed BC applications in asset distribution, data security, the financial industry, the Internet of Things, the healthcare sector, and AI have been explored in this article. It presents necessary background knowledge about BC and privacy strategies for obtaining these security features as part of the evaluation. This survey is expected to assist readers in acquiring a complete understanding of BC security and privacy in terms of approaches, ideas, attributes, and systems. Subsequently, the review presents the findings of different BC works, illustrating several efforts that tackled privacy and security issues. Further, the review offers a positive strategy for the previously described integration of BC for security applications, emphasizing its possible significant gaps and potential future development to promote BC research in the future.

The Promise and Failure of Israeli-Palestinian Peace Process in 1990s: A Literature Review

In 2023, the world marked 30 years since the start of the Oslo process, which sought to resolve the Israeli-Palestinian conflict. Given the ultimate breakdown of these negotiations, it is crucial to examine the evolution of academic research on this process, identifying its key themes and trends. This literature review focuses on both empirical and theoretical studies of the Oslo process, with a particular emphasis on research that investigates the reasons behind its failure. Such studies provide valuable insights for developing more effective models of negotiation and conflict resolution. Initially, the commencement of the Oslo negotiations was met with optimism in the academic community, with scholars highlighting the pivotal role of individual leaders in successfully launching the process. However, as the peace process faltered, the Israeli-Palestinian negotiations were analyzed through existing theoretical frameworks. Various scholars critically examined the Oslo Accords from the perspective of international law and highlighted economic factors that obstructed the path to a lasting peace. Other studies explored indirect influences on the peace process, such as public opinion and electoral dynamics, often employing Robert Putnam's “two-level games” theory. Nevertheless, these studies mainly address the immediate causes of the peace process’s failure and do not fully account for the underlying motivations driving the behavior of actors in the negotiations. Research into the deeper causes of the Oslo process’s collapse has highlighted socio-psychological barriers, such as the “conflict syndrome” and issues of ontological security. Scholars have investigated the roles of historical memory, collective trauma, and cultural identities in shaping negotiation outcomes. The rise of interdisciplinary approaches—combining insights from psychology, philosophy, and linguistics—has been essential for understanding the complex dimensions of this conflict. The diverse body of research and theories inspired by the Oslo peace process continues to influence the field of negotiation and conflict resolution studies, underscoring the enduring significance of this critical episode in diplomatic history.