Security Challenges Research Articles

Unraveling the root causes and challenges of organized crime at South Africa’s border posts

South Africa’s border posts are increasingly becoming crucial hubs for organized crime posing serious national and regional security implications with far-reaching consequences. The country’s national security, economic stability, and community safety are significantly jeopardised by organised criminal enterprises at border posts. As a result, the porous borders of South Africa have fostered an environment that is conducive to a variety of unlawful activities, such as the smuggling of drugs into the country and human trafficking. This paper seeks to identify political, economic, and social factors that lead to organised crime, corruption, and weak border management systems. The paper employed a secondary data analysis of existing scholarly articles, government reports as well as relevant case studies. The study found that local communities are most affected by illegal activities at the ports of entry. The findings further emphasize the importance of inclusive approaches in responding to security challenges that address cross-border flow regulation, fight corruption in service delivery, and promote community resilience. The paper concludes with recommendations for strengthening border controls towards enhancing cooperation between countries and curbing transnational crime networks.

Investigating Intrusion Detection System Using Federated Learning for IoT Security Challenges

Investigating Intrusion Detection System Using Federated Learning for IoT Security Challenges

Exploring the dynamics of socio-technical transitions: Advancing grid-connected wind and solar energy adoption in Nigeria

Globally, countries are transitioning their national electricity grid system to meet the obligations of the Paris Agreement. National grid-connected renewable electricity provides an opportunity to reduce greenhouse gas emissions. Specifically, in a developing country like Nigeria, renewable energies such as solar and wind power can play a critical role not only in transitioning away from fossil-fuel based electricity systems but also to address prevalent energy security challenges, electricity supply deficit and gaps in energy access. Policies, plans, and strategies to ensure grid-connected renewable energy have been put in place, but these measures have proved ineffective. Likewise, there are limited studies that investigate these transition problems, including drivers and enablers from a developing country perspective. This research fills that gap in knowledge by investigating these transition dynamics through a qualitative approach applying the multi-level perspective framework. Thirty-one (31) key energy and non-energy actors were interviewed, and the transcripts thematically analysed. Findings indicate that there is prospect for Nigeria to transform its electricity system. However, there are major deterrents including institutional, infrastructural, incumbent electricity system instability and economic barriers. The paper highlights that there is a level of regime stability/instability that will enable renewable energy development. In addition, practical and policy insights are offered to overcome identified challenges towards accelerating electricity system transformation. Future studies should also explore the degree of instability, stability and change in developing country settings necessary for energy transition to occur. Finally, this paper advances debates in energy transition and socio-technical system studies.

Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security

The Web of Things (WoT) represents a significant advancement on the Internet of Things (IoT), where web technologies are integrated to enhance device interoperability and accessibility. While this integration offers numerous benefits, it also introduces new and complex security challenges. This paper presents a critical analysis of WoT security, examining the ecosystem’s vulnerabilities and associated threats. Our contributions include an in-depth analysis of existing threat enumeration methodologies, highlighting misconceptions and inefficiencies that may weaken security measures. We further conduct a comprehensive survey of critical threats within the WoT environment, detailing potential attack vectors and misuses linked to these threats. To address these security gaps, we propose a set of defenses tailored to each identified threat, providing a holistic view of the WoT’s security landscape. We also develop abstract architectural models of the WoT using UML, serving as foundational tools for understanding the interactions and risks within WoT systems. Finally, we model a specific attack scenario, demonstrating how attacks unfold in real-world WoT environments and the importance of defense strategies. These findings aim to guide the development of secure WoT systems, ensuring robust defenses against evolving security threats.

Securing a Generative AI-Powered Healthcare Chatbot.

In Generative Artificial Intelligence (AI), Large Language Models (LLMs) like GPT-4, Gemini, Claude, and Llama, significantly impact healthcare by aiding in patient care, medical research, and administrative tasks. AI-powered chatbots offer real-time responses and manage chronic diseases, improving patient outcomes and operational efficiency. However, these models pose security and ethical challenges, necessitating robust data privacy, adversarial training, and ethical guidelines. This paper proposes a secure, ethical pipeline for deploying AI healthcare chatbots, integrating advanced privacy-preserving techniques and continuous security assessments to enhance data privacy, resilience, and user trust.

A Roadmap to Systematic Review: Evaluating the Role of Data Networks and Application Programming Interfaces in Enhancing Operational Efficiency in Small and Medium Enterprises

The adoption of Data Networks and Application Programming Interfaces (APIs) has become crucial for small and medium enterprises (SMEs) to streamline operations, improve efficiency, and reduce costs. However, SMEs often face challenges such as resource limitations and security vulnerabilities, which hinder their ability to fully leverage these technologies. This systematic review examines the role of Data Networks and APIs in enhancing operational efficiency within SMEs, focusing on key metrics such as speed, cost reduction, scalability, and security challenges. Following PRISMA 2020 guidelines, we conducted a systematic search across multiple databases including Web of Science, Scopus, IEEE Xplore, and Google Scholar. Studies published between 2014 and 2024, focused on SMEs, and addressing the role of Data Networks and APIs in operational efficiency were included. A total of 49 studies met the inclusion criteria and were analyzed for key outcomes related to operational efficiency, cost-effectiveness, and security risks. The review found that Data Networks and APIs significantly improve operational efficiency by increasing process speed (12% increase), reducing operational costs (8% reduction), and enhancing overall productivity. However, security challenges, particularly related to API vulnerabilities, were a major concern, with cyberattacks on APIs increasing by 400% in Q1 2023 alone. Despite these risks, the benefits of implementing Data Networks and APIs in SMEs, particularly in terms of scalability and real-time data processing, were evident across industries. Data Networks and APIs offer substantial improvements in operational efficiency for SMEs, although security remains a significant challenge. Future efforts should focus on developing security frameworks tailored to SMEs while maintaining the operational benefits of these technologies. Further research is needed to explore scalable and secure API models for SMEs.

Terrorism, Banditry and Separatism

This paper discusses the tripodal menace of terrorism, banditry and separatism as the centrifugal forces that threatened the existence of Nigerian State; and seeks to assuage the fear of morselization of Nigerian state by identifying six resiliencies that guard against the collapse of Nigeria. It concludes that the tripodal security challenges can be transformed into instruments for nation and state building. The paper therefore recommends the provision of homeland for nomadic Fulanis, an election of an Igbo President, as well as the restructuring of the polity as recommended in the 2014 National Conference Report. It also recommends the promotion of the Rule of Law, Justice, Equity and Fairness in the polity, especially in the handling of repentant terrorists and the rehabilitation of victims of these security challenges.

Federated Learning-Assisted Coati Deep Learning-Based Model for Intrusion Detection in MANET

MANET is a set of self-arranged, wirelessly connected nodes. Each mobile ad hoc network node acts as a router to send the packet from the source node to the destination node. MANET nodes’ random movements and decentralized architecture pose security challenges, making them vulnerable to various attacks like node selfishness, network partition, black hole, and DoS due to limited hardware resources. In this paper, a novel Hybrid Intrusion DEtection for MANet (HIDE-MAN) technique has been proposed to detect intrusion like DDoS and MitM attacks in MANET. The proposed HIDE-MAN framework initiates by preprocessing malicious data packets through data cleaning and data transformation resulting in the creation of high-dimensional vectors. The intrusion detection system then makes use of the CO-BiLSTM model, which is based on the actions of Coati and BiLSTM. It categorizes outputs into DDoS attacks, MitM attacks, or the absence of any attacks. Federated learning with GAN networks allows for the aggregation of updates from multiple local models distributed across MANET. Assessment metrics such as accuracy, precision, F1 score, detection rate, recall, and security rate have been utilized to assess the efficacy of the proposed HIDE-MAN method. The comparative analysis shows that the detection rate of the proposed HIDE-MAN is greater by 18.9%, 18.07%, and 4.03% than that of the current KBIDS, WOA-DNN, and MSA-GCNN techniques, respectively.

Exploring the Interplay Between Multiagency Security Challenges and Economic Security in Lamu County, Kenya

The article underscored challenges arising from multi agency security approach and economic security in Lamu County-Kenya. The specific objective of the study was to assess the efficacy of multi-agency security strategies adopted in enhancing economic security in Lamu County, Kenya. Various literatures were reviewed in a funnel shape from the global, continental, regional, national and then to area of study. The study was guided by system theory. It adopted descriptive research design since it was a mixed research approach. Purposive and snowballing sampling technique was utilized to reach respondents. Additionally, fishers formula was embraced to derive the sample size of 384 respondents since the target population was more than 10, 000 people from Lamu County, which has a total population of 143,920 persons according to Kenya 2019 census report. The data was analyzed by use of triangulation method of analysis whereby, content analysis, thematic analysis and verbatim quotations were used. The research article found out that lack of coordination and communication among security agencies significantly hampers multi-agency efforts to enhance economic security in Lamu County. Additionally, it emerged that, fragmented initiatives and resource limitations impede swift responses to evolving threats. Moreover, the research article concludes that; socio-political intolerance disrupts collaboration, fosters divided loyalties, and politicizes security efforts, undermining community trust and the impartiality of security measures. Therefore, the research article recommends that; in order to promote inclusivity and impartiality in security policies, ensuring measures are seen as fair and non-partisan, continuous training programs are essential for updating skills, particularly in technological and collaborative aspects of security.

Understanding ‘the Conflict Conundrum’: the Perspective of Microfinance Institutions’ Conflicts in Ghana

Across the globe, Microfinance Institutions (MFIs) have the potential of enhancing the standard of living of low-income groups, the socially excluded, as well as the informal sector through the provision of various cost-effective lending approaches. They contribute to employment creation, capacity building, community development, women’s empowerment, and capital provision, among others. Despite these contributions, in Ghana, the sector is saddled with varied degrees of conflict. Against this background, four MFIs in the Brong Ahafo Region, which have survived despite the collapse of many in recent times, were purposely selected in a multiple case study design with the view to examine how conflicts affect their operations. We selected a total of 58 respondents using purposive and simple random sampling techniques. The study found that creativity and innovation, group unity and cooperation, and improved decision-making have been facilitated by conflict. Notwithstanding, due to dysfunctional conflicts, MFIs are confronted with low productivity, loss of clients and staff, security challenges, and frequent and prolonged litigation, explaining the dual nature of conflicts. The paper recommends capacity-building on conflict management for MFI staff with the view of helping them minimise the negative effects of conflicts on the operation of these institutions.

Shaping the Global Future: The Strategic Influence of the Emerging Global South in the New International Order (2022)

The reconfiguration of the international order in 2022 signifies a pronounced shift in global power dynamics, with the ascendant Global South increasingly asserting itself as a transformative force in redefining governance structures and economic paradigms. This study interrogates the pivotal role of emerging economies across Africa, Asia, and Latin America in contesting the entrenched hegemony of the Global North. It foregrounds the expanding influence of multilateral coalitions such as BRICS, the G20, and various regional alignments, which collectively champion a more equitable and multipolar global order. Central to this inquiry is an examination of how these states strategically deploy economic cooperation, South-South trade, and alternative financial architectures to assert their geopolitical agency on the international stage. Furthermore, the analysis delves into the nuanced geopolitical manoeuvres these nations employ to navigate an increasingly complex global landscape marked by intensified great power rivalry, economic volatility, and emergent security challenges. Through a rigorous examination of case studies, this research posits that the Global South is transitioning from a reactive posture to a proactive role in forging a new international paradigm centred on inclusivity, sustainability, and diversified leadership. The findings suggest that the collective ascendancy of these nations heralds a pivotal transformation in global relations, with far-reaching consequences for the trajectory of global governance. Received: 21 August 2024 / Accepted: 5 November 2024 / Published: 20 November 2024

An Efficient Flow-Based Anomaly Detection System for Enhanced Security in IoT Networks.

The growing integration of Internet of Things (IoT) devices into various sectors like healthcare, transportation, and agriculture has dramatically increased their presence in everyday life. However, this rapid expansion has exposed new vulnerabilities within computer networks, creating security challenges. These IoT devices, often limited by their hardware constraints, lack advanced security features, making them easy targets for attackers and compromising overall network integrity. To counteract these security issues, Behavioral-based Intrusion Detection Systems (IDS) have been proposed as a potential solution for safeguarding IoT networks. While Behavioral-based IDS have demonstrated their ability to detect threats effectively, they encounter practical challenges due to their reliance on pre-labeled data and the heavy computational power they require, limiting their practical deployment. This research introduces the IoT-FIDS (Flow-based Intrusion Detection System for IoT), a lightweight and efficient anomaly detection framework tailored for IoT environments. Instead of employing traditional machine learning techniques, the IoT-FIDS focuses on identifying unusual behaviors by examining flow-based representations that capture standard device communication patterns, services used, and packet header details. By analyzing only benign traffic, this network-based IDS offers a streamlined and practical approach to securing IoT networks. Our experimental results reveal that the IoT-FIDS can accurately detect most abnormal traffic patterns with minimal false positives, making it a feasible security solution for real-world IoT implementations.

Flexibility and Legal Protection of Labor Relations: Telework, Flexible Employment and Workers' Rights and Interests

This study focuses on the changes in labor relations under the model of telework, analyzing the increased flexibility it brings and the security challenges it creates. Although telework breaks down geographical boundaries and enhances work efficiency, it also raises new issues about the stability of labor relations, occupational safety and health, and the protection of workers' rights and interests. The study emphasizes that a sound legal system, cutting-edge technological support and efficient communication mechanisms are the core of balancing flexibility and safety. Through an in-depth discussion of the characteristics of telework, case studies and targeted policy recommendations, this study aims to provide theoretical support and practical paths for the healthy development of telework, and to promote its sustainable progress driven by both technology and market.

Optimizing random forests to detect intrusion in the Internet of Things

The Internet of Things (IoT) has created new security challenges by connecting billions of smart devices to each other. One of these challenges is detecting attacks in IoT networks. Traditional attack detection methods are usually not suitable for large and complex networks such as IoT networks. In this research, a new model for detecting intrusion in IoT networks using Software-Defined Networking (SDN) is introduced. The main goal of the current research was to improve the stability of IoT networks against various attacks using an optimized machine learning model in a distributed manner. The presented approach uses the advantages of SDN, such as flexibility and centralized control, to improve intrusion detection performance. The proposed method includes two phases: first, the topology of the network is divided into a set of subdomains, and a controller node is assigned to each subdomain. Then, in the second phase, an ensemble classification model based on a random forest is utilized for detecting intrusion in each subdomain. This learning model is a forest of classification and regression trees (CARTs), each component of which is optimized by genetic algorithm (GA). Controller nodes can use this classification model to identify intrusion independently or cooperatively. The main novelty of the current work lies in optimizing multiple learning models and cooperatively utilizing them for intrusion detection goals. In an experimental environment based on MATLAB software, the effectiveness of this model for detecting intrusions on two databases, NSW-NB15 and NSLKDD, was evaluated. The findings of the experiments showed that this model can identify the attacks in these two databases with 98.06 % and 99.67 % accuracy respectively, which is significantly higher than the compared models.

Attenuated Total Reflectance Crystal of Silicon for Rapid Nitrate Sensing Combining Mid-Infrared Spectroscopy.

Rapid detection of NO3 --N is critical to address the challenges of food security, environmental degradation, and climate change. Conventional methods for sensing NO3 --N in water demand pretreatments and chemical reagents, which are time- and cost-consuming. Consequently, Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy has been well applied for the determination of NO3 --N. However, the conventional ATR crystals, i.e., zinc selenide (ZnSe) and diamond, showed a weakness in duration or cost since the ZnSe material was relatively soft and diamond was relatively expensive. In this study, comparing with ZnSe-ATR and diamond-ATR, a silicon-based ATR (Si-ATR) accessory was developed and used to explore the applicability and stability for sensing NO3 --N combining mathematic algorithms. It was found that partial least-squares regression (PLSR) showed a good performance comparing with the algorithms of principal component analysis (PCA) and linear regression (LR), and it was recommended for quantifying NO3 --N. For ZnSe-ATR, the residual prediction deviation (RPD) was more than 1.80, the determination coefficient (R 2) was more than 0.7725, and the root-mean-square error (RMSE) was less than 2.73 mg L-1. For diamond-ATR, the RPD was more than 1.76, the R 2 was more than 0.7309, and the RMSE was less than 8.22 mg L-1. For Si-ATR, the RPD was more than 1.42, the R 2 was 0.5198, and the RMSE was less than 11.02 mg L-1. It was confirmed that all three types of ATR could be applied in the quantification of NO3 --N in water for high nitrate concentrations. However, for the quantification of low nitrate concentrations (0-1 mg L-1 NO3 --N), ZnSe-ATR and diamond-ATR acquired the same accuracy, while Si-ATR had a lower accuracy. The pretreatment of Si-ATR-based spectra using the deconvolution algorithm could improve the prediction accuracy compared to water deduction for predicting low NO3 --N. Furthermore, a Si-ATR accessory was developed using Si-ATR, which was reliable for NO3 --N concentration quantification in water with the advantage of its low cost and long durability. Totally, samples with high nitrate concentrations implied a more reliable prediction for all crystals, and comprehensively, ZnSe-ATR was recommended for sensing low nitrate concentrations; diamond-ATR was recommended for samples with strong acid or base corrosion; and for sensing relatively high nitrate concentrations, such as in natural water bodies, Si-ATR was more economical because of its low cost and relatively long use life.

Genomic survey and evolution analysis of calcium-dependent protein kinases in plants and their stress-responsive patterns in populus

BackgroundCalcium-dependent protein kinases (CDPKs) phosphorylate downstream target proteins in response to signals transmitted by free calcium ions (Ca2+, one of the second messengers) and thus play important regulatory roles in many biological processes, such as plant growth, development, and stress response.ResultsA bioinformatic analysis, as well as thorough evolutionary and expression investigations, were conducted to confirm previous reports of functional evidence for plant CDPKs. Using the Phytozome database’s BLAST search engine and the HMM search tool in TBtools software, we discovered that CDPKs are well conserved from green algae to flowering angiosperms in various gene family sizes. Additional investigations of the obtained CDPKs revealed high conservation of domain and motif numbers, gene architectures, and patterns. However, this conservation differed among plant species. Phylogenetic analysis demonstrated that the CDPK gene family diverged from a common ancient gene. Similarly, investigations into plant interspecies evolutionary relationships revealed common ancestral plant species, suggesting speciation of plants and evolution based on plant adaptation and diversification. A search for the driving force of CDPK gene family expansion revealed that dispersed duplication events, among other duplication events, contributed largely to CDPK gene family expansion. Gene localization analysis in P. trichocarpa demonstrated that most CDPK genes are localized within several cell organelles and bind other kinases and proteins to perform their biological functions efficiently. Using RNA-seq data and qPCR analyses, we postulated that PtCDPKs play functional roles in abiotic stress responses by regulating cold, heat, drought and salt stress to varying extents.ConclusionThe CDPK genes are well conserved in plants and are critical entities in abiotic stress regulation, and further exploration and manipulation of these genes in the future may provide solutions to some of the challenges in agriculture, forestry and food security.

Predicting future climate scenarios: a machine learning perspective on greenhouse gas emissions in agrifood systems

Global climate change is an extensive phenomenon characterized by alterations in weather patterns, temperature trends, and precipitation levels. These variations substantially impact agrifood systems, encompassing the interconnected components of farming, food production, and distribution. This article analyzes 8,100 data points with 27 input features that quantify diverse aspects of the agrifood system’s contribution to predicted Greenhouse Gas Emissions (GHGE). The study uses two machine learning algorithms, Long-Short Term Memory (LSTM) and Random Forest (RF), as well as a hybrid approach (LSTM-RF). The LSTM-RF model integrates the strengths of LSTM and RF. LSTMs are adept at capturing long-term dependencies in sequential data through memory cells, addressing the vanishing gradient problem. Meanwhile, with its ensemble learning approach, RF improves overall model performance and generalization by combining multiple weak learners. Additionally, RF provides insights into the importance of features, helping to understand the significant contributors to the model’s predictions. The results demonstrate that the LSTM-RF algorithm outperforms other algorithms (for the test subset, RMSE = 2.977 and R2 = 0.9990). These findings highlight the superior accuracy of the LSTM-RF algorithm compared to the individual LSTM and RF algorithms, with the RF algorithm being less accurate in comparison. As determined by Pearson correlation analysis, key variables such as on-farm energy use, pesticide manufacturing, and land use factors significantly influence GHGE outputs. Furthermore, this study uses a heat map to visually represent the correlation coefficient between the input variables and GHGE, enhancing our understanding of the complex interactions within the agrifood system. Understanding the intricate connection between climate change and agrifood systems is crucial for developing practices addressing food security and environmental challenges.

Lightweight, Post-Quantum Secure Cryptography Based on Ascon: Hardware Implementation in Automotive Applications

With the rapid growth of connected vehicles and the vulnerability of embedded systems against cyber attacks in an era where quantum computers are becoming a reality, post-quantum cryptography (PQC) is a crucial solution. Yet, by nature, automotive sensors are limited in power, processing capability, memory in implementing secure measures. This study presents a pioneering approach to securing automotive systems against post-quantum threats by integrating the Ascon cipher suite—a lightweight cryptographic protocol—into embedded automotive environments. By combining Ascon with the Controller Area Network (CAN) protocol on an Artix-7 Field Programmable Gate Array (FPGA), we achieve low power consumption while ensuring high performance in post-quantum-resistant cryptographic tasks. The Ascon module is designed to optimize computational efficiency through bitwise Boolean operations and logic gates, avoiding resource-intensive look-up tables and achieving superior processing speed. Our hardware design delivers significant speed improvements of 100 times over software implementations and operates effectively within a 100 MHz clock while demonstrating low resource usage. Furthermore, a custom digital signal processing block supports CAN protocol integration, handling message alignment and synchronization to maintain signal integrity under automotive environmental noise. Our work provides a power-efficient, robust cryptographic solution that prepares automotive systems for quantum-era security challenges, emphasizing lightweight cryptography’s readiness for real-world deployment in automotive industries.

Security Challenges in Energy Flexibility Markets: A Threat Modelling-Based Cyber-Security Analysis

Flexibility markets are crucial for balancing the decentralised and renewable-driven energy landscape. This paper presents a security evaluation of a flexibility market system using a threat modelling approach. A reference architecture for a typical flexibility market system is proposed, and attack graph-driven simulations are performed to analyse potential attack pathways where malicious actors might infiltrate the system and the vulnerabilities they might exploit. Key findings include the identification of high-risk areas, such as the Internet links between market actors. To mitigate these risks, the paper proposes and evaluates multiple protection scenarios in reducing the identified attack vectors. The findings underline the importance of multi-layered security strategies to safeguard flexibility markets from increasingly sophisticated cyber threats.

A Comprehensive Study on Internet of Things (IoT): State-of-the-Art: Security Challenges, Future Directions, Applications and Opportunities.

This paper or comprehensive study on the Internet of Things (IoT) explores the current state of the art, highlighting recent advancements, key applications, and transformative potential across various industries. It reviews IoT's foundational technologies, including sensing, connectivity, data analytics and security, to provide a holistic understanding of the ecosystem. Major research challenges are discussed, such as security vulnerabilities, data privacy, energy efficiency and scalability, which require innovative solutions for broader adoption. The study also addresses future directions and opportunities, focusing on emerging trends like edge computing, artificial intelligence integration, and 5G adoption, which are poised to accelerate IoT's impact on sectors like healthcare, smart cities, agriculture and industrial automation. By addressing these challenges and seizing emerging opportunities, IoT stands to transform society and industry, making this research crucial for driving its evolution