Security Issues Research Articles

Comparative analysis of federated learning algorithms under non-IID data

With the increasing global attention to data privacy and security issues, how to effectively utilize distributed data while protecting personal privacy has become an important research topic. Federated learning (FL) aims to address data silos and privacy issues by enabling multiple devices or servers to train shared models in collaboration without submitting raw data to a central server. Although a variety of federated learning algorithms have been proposed, there is still a gap in the research on their performance differences under the same model. The goal of this study is to compare and analyze the performance differences of different federated learning algorithms on the same model through experiments. Based on the Fashion-MNIST dataset, this paper compares four commonly used federated learning algorithms in detail: Federal Averaging (FedAvg), Federated Stochastic Gradient Descent (FedSGD), Stochastic Controlled Averaging for Federated Learning (SCAFFOLD), and Federated Proximal (FedProx). The experimental results show that FedProx performs best in all evaluation indicators, followed by SCAFFOLD and FedAvg, while FedSGD performs the worst. These insights into algorithm performance with non-IID data inform practical application suitability and guide future research.

OPIN-ITP: Optimized Physics Informed Network with Trimmed Score Regression Based Insider Threats Prediction in Cloud Computing

INTRODUCTION: Insider threats are a major issue for cyber security. In contrast to external attackers, insiders have more privileges and authorized access to data and resources, which can cause an organization great harm. To completely understand an insider's activities throughout the organization, a more sophisticated method is needed. OBJECTIVES: Based on an organization's login activity, this study proposes a novel conceptual method for insider threat detection. Behavioural activities such as HTTP, Email and Login details are collected to create a dataset which is further processed for pre-processing using data transformation and Trimmed Score Regression (TSR). METHODS: These pre-data are given to the feature extraction process using Deep Feature Synthesis (DFS) extraction. The extracted data are fed to Physics Informed Neural Networks (PINN) for insider threat detection. RESULTS: The prediction process of PINN was improved through optimally choosing parameters such as learning rate and weight using Hunter-prey Optimization (HPO). The proposed model offers 68% detection rate, 98.4% accuracy, 5% FDR, 95% F1_score and 0.7005 sec execution time. CONCLUSION: Observed outcomes are compared to other traditional approaches of validation. The contrast with traditional approaches shows that the proposed model provides better outcomes than in traditional models and is therefore a good fit for real-time threat prediction.

Flexibility of a large blindly synthetized avatar database for occupational research: Example from the CONSTANCES cohort for stroke and knee pain.

Though the rise of big data in the field of occupational health offers new opportunities especially for cross-cutting research, they raise the issue of privacy and security of data, especially when linking sensitive data from the field of insurance, occupational health or compensation claims. We aimed to validate a large, blinded synthesized database developed from the CONSTANCES cohort by comparing associations between three independently selected outcomes, and various exposures. From the CONSTANCES cohort, a large synthetic dataset was constructed using the avatar method (Octopize) that is agnostic to the data primary or secondary data uses. Three main analyses of interest were chosen to compare associations between the raw and avatar dataset: risk of stroke (any stroke, and subtypes of stroke), risk of knee pain and limitations associated with knee pain. Logistic models were computed, and a qualitative comparison of paired odds ratio (OR) was made. Both raw and avatar datasets included 162,434 observations and 19 relevant variables. On the 172 paired raw/avatar OR that were computed, including stratified analyses on sex, more than 77% of the comparisons had a OR difference ≤0.5 and less than 7% had a discrepancy in the statistical significance of the associations, with a Cohen's Kappa coefficient of 0.80. This study shows the flexibility and the multiple usage of a synthetic database created with the avatar method in the particular field of occupational health, which can be shared in open access without risking re-identification and privacy issues and help bring new insights for complex phenomenon like return to work.

Synergistic evaluation of energy security and environmental sustainability in BRICS geo-political entities: An integrated index framework

Research background: The increasing demand for energy, driven by economic growth and population expansion, is a critical driver of societal progress. However, the predominant reliance on fossil fuels to meet this demand presents significant challenges, particularly in the rapidly developing BRICS nations (Brazil, Russia, India, China, and South Africa). These countries are faced with a complex interplay of energy security and environmental sustainability issues, stemming from their substantial fossil fuel reserves and the associated environmental consequences. The challenges manifest as inequalities in access to clean energy, environmental degradation, and heightened vulnerability to the impacts of climate change. Addressing these multifaceted issues requires a comprehensive approach. Metrics-based strategies, which employ aggregated indices derived from a diverse set of energy and environmental indicators, have the potential to provide valuable insights into these complexities. However, the development of a universally applicable energy sustainability index is complicated by the heterogeneity of metrics, disparities between countries, and methodological challenges, emphasizing the need for an innovative and holistic analytical framework. Purpose of the article: This study aims to develop a tailored Energy Security and Environmental Sustainability Index for BRICS economies to evaluate the robustness of their energy systems and the viability of their ecological practices. The index serves as an instrument to assess the progress of these nations in the Energy and Environment domain and identify areas that require targeted interventions and improvements. Methods: The construction of the composite ESESI involves the selection of relevant parameters and the application of a Multi-Criteria Decision Analysis (MCDA) framework in conjunction with the Weighted Product Method (WPM). To ensure objectivity in the determination of optimal and least favorable weights for each indicator, the study employs the Multiplicative Data Envelopment Analysis (MDEA) model. Findings & value added: The ESESI analysis reveals disparities in the progress made by BRICS nations in enhancing energy security, promoting renewable energy deployment, and mitigating environmental impacts. While some countries demonstrate substantial advancements, others face challenges in improving energy efficiency and reducing carbon emissions. The study underscores the necessity for tailored policies and targeted infrastructural enhancements that align with the unique challenges and strengths of each nation. Harnessing the abundant renewable energy potential through advanced energy trade mechanisms and fostering cross-border investments are identified as crucial strategies for ensuring environmental sustainability and long-term energy in the BRICS region. The ESESI provides a valuable tool for policymakers and researchers to evaluate the progress of BRICS nations in achieving sustainable energy goals and to inform evidence-based decision-making processes. By offering a comprehensive and scientifically rigorous assessment framework, this study contributes to the ongoing discourse on sustainable energy transitions and environmental stewardship in the context of rapidly developing economies.

Review on the Use of Data Analysis for Customer Segmentation and Personalization in Marketing Strategies

This paper studies the application of data analysis in the field of customer segmentation and personalization. With the advent of the era of big data, enterprises and organizations have rich data resources that contain important information about customer behavior, preferences, and purchasing habits. Through data analytics, companies can better understand customer needs and provide personalized products and services, thereby improving customer satisfaction and loyalty. This study aims to explore the application of data analytics in the field of customer segmentation and personalization, and provides some common data analysis methods. Data analysis methods include probabilistic methods and Bayesian methods, among others. Through case studies, the application of data analysis in the field of e-commerce is explained. The research results show that data analytics has important value and potential applications in customer segmentation and personalized marketing. However, data analysis still faces some challenges and limitations in practice, including data security and quality issues. Future research can continue to explore methods and techniques for data analysis, solve problems such as data privacy and security, and apply data analysis in more fields.

Data tampering detection and recovery scheme based on multi-branch target extraction for internet of vehicles

With the rapid development of new networks such as 5 G/6 G, the Internet of Vehicles has been given features such as hyper-connectivity and hyper-intelligence, promoting the implementation of new scenarios for autonomous vehicles. However, on the Internet of vehicles, vehicles use many cameras, radars and other sensors to sense the environment and execute instructions, facing security issues such as road data tampering and hijacking. Consequently, this paper presents a data tampering detection and recovery scheme based on multi-branch target extraction. Specifically, for road images collected by sensors, this paper presents a target extraction method based on multi-branch spatial feature pyramid blocks to obtain road salient targets. Then, a tamper detection and recovery algorithm based on interval mapping is presented. Once the image road target is tampered with, this method can quickly detect the tampering traces and restore them to achieve the authenticity and integrity of the road image on the Internet of Vehicles. The availability of the proposed scheme is verified through comparative experiments, and the performance is improved satisfactorily compared with other works.

Extensive Review of Security and Privacy Issues in Heterogeneous Networks

This paper studies advanced network security, privacy, and performance issues for Heterogeneous Networks (HetNets), which combine multiple types of cells to enhance wireless communication coverage and capacity. The coexistence of heterogeneous network elements inherently leads to considerable issues in terms of security, privacy, and performance. Previous research has primarily addressed security issues using common cryptographic techniques including data encryption with AES, secure key exchange with RSA, and handoff security with mutual authentication. Data anonymization methods and regulatory compliance have been used to address privacy concerns. Allocating resources and managing interference are some of the tactics that have been used in performance optimization. Although the current solutions offer a solid base, they frequently have issues with computational overhead, scalability, and continuous maintenance needs. Performance optimization solutions might not sufficiently handle dynamic network conditions, and privacy protections might not be sufficient to mitigate sophisticated data harvesting operations. This study evaluates existing HetNets solutions using a thorough evaluation and analytical technique. It assesses how well they handle issues with security, privacy, and performance, points out any shortcomings, and suggests areas for further investigation. Our analysis emphasizes the need for improved security mechanisms, including quantum-resistant cryptography, AI-driven threat detection, and technologies that improve privacy, such as differential privacy and homomorphic encryption. Innovative resource management and optimization strategies catered to HetNets' dynamic nature are needed to address performance issues. The necessity of developing new security, privacy, and performance solutions to guarantee the stability and dependability of HetNets is emphasized by this study. Stakeholders can promote the broad adoption and smooth integration of HetNet technology by tackling these obstacles. To sum up, this study offers a thorough analysis of the HetNets' architectural, security, privacy, and performance issues. It lists existing remedies, analyses their drawbacks, and suggests new lines of inquiry for future development to advance the area and improve HetNets' operational capabilities.

Survey of Cloud Computing Security and Privacy Issues

The world of computing and data management has completely changed as a result of cloud computing, which offers both consumers and enterprises scalable resources, on-demand services, and considerable cost savings. The demand for more adaptable and effective computer resources has prompted this paradigm change, and cloud computing provides it by enabling users to access and utilize computing resources over the internet. But using cloud computing also raises a number of security and privacy issues that must be properly handled. In order to assess the present status of cloud computing security and privacy problems, this article will examine key terms, the development of the technology, its significance, current research gaps, and the urgent need for more in-depth analysis.

A comprehensive systematic review of privacy and security issues in Satellite Networks

The recent advancements in satellite networks have garnered significant interest due to their extensive geographic coverage and flexible deployment capabilities, offering a promising solution for global communications and transforming traditional communication methods. Despite these advancements, current satellite systems face challenges such as high propagation delays and inadequate coverage of high-latitude regions, particularly in Geostationary (GEO) systems. Low Earth Orbit (LEO) systems, which can address these issues, are primarily used for voice services, as seen in the Iridium system, but have encountered financial difficulties. This study aims to address the security issues in satellite networks, a critical concern as these networks increasingly rely on IP protocols and hybrid configurations of terrestrial nodes and satellite links. Previous works have identified various potential security attacks on satellite networks and proposed different solutions, but these solutions often lack comprehensive effectiveness and robustness. Our methodology involves analyzing the security vulnerabilities in satellite networks similar to the Iridium system, which includes inter-satellite links (ISL) and routers on each satellite. We review and evaluate existing security measures and propose enhancements to improve their effectiveness. Our results indicate significant vulnerabilities in current systems, but also show that with targeted improvements, security can be substantially enhanced. The implications of this study are profound, suggesting that more secure satellite networks can better support critical global communications and services, including broadband Internet and data services, thereby enhancing their reliability and user trust

Software-Defined Wide Area Networks (SD-WANs): A Survey

SD-WANs are an innovative software-defined network (SDN) technology used to reinvent networks, services, and applications in wide area network (WANs). The development of SD-WANs ranges from network optimization in the past to service provision platforms at present and distributed computing systems in the future. The existing surveys on SD-WANs are fragmented, covering specific problems only, and are not comprehensive with detailed research directions. This paper seeks to provide a systematic survey on SD-WANs by introducing major research directions and stating specific problems. Therefore, four major research directions related to traffic engineering, network optimization and systems, service orchestration, and the security issues of SD-WANs are sequentially introduced, along with detailed statements relating to specific problems and the classification of state-of-the-art research. Finally, the trends and challenges regarding SD-WANs are summarized and our future work is described.

Cybersecurity Implications of Edge Computing in Data Engineering

Edge computing, as an extension of cloud computing, brings computation and data storage closer to the data source. This shift offers significant advantages in terms of latency reduction, bandwidth optimization, and real-time processing capabilities. By minimizing the distance that data needs to travel, edge computing enhances the performance of applications that require rapid data processing and immediate response times. This is particularly beneficial for Internet of Things (IoT) devices, autonomous vehicles, smart grids, and other applications that demand low-latency interactions. However, the decentralization inherent in edge computing introduces a unique set of cybersecurity challenges that are distinct from those faced in traditional centralized cloud environments. The distributed architecture of edge computing creates numerous points of vulnerability, each of which can be exploited by cyber attackers. Additionally, edge devices often operate with limited computational resources and power, which complicates the implementation of robust security measures. This paper explores the cybersecurity implications of edge computing in the context of data engineering. It begins with a comprehensive review of existing literature to establish the current understanding of edge computing security issues. The review identifies the primary vulnerabilities associated with edge computing, including those related to distributed architecture, data transmission, and resource constraints. Following the literature review, the paper delves into specific security threats that edge computing environments face. These include man-in-the-middle (MitM) attacks, distributed denial-of-service (DDoS) attacks, malware and ransomware, and insider threats. Each threat is analyzed to understand its potential impact on edge computing systems and the data they process.

DFier: A directed vulnerability verifier for Ethereum smart contracts

Smart contracts are self-executing digital agreements that automatically enforce the terms between parties, playing a crucial role in blockchain systems. However, due to the potential losses of digital assets caused by vulnerabilities, the security issues of Ethereum smart contracts have garnered widespread attention. To address this, researchers have developed various techniques to detect vulnerabilities in smart contracts, with fuzzing techniques achieving promising results. Nonetheless, current fuzzers are unable to effectively exercise suspicious targets because they overlook two key factors: comprehensively exploring all paths to the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), which elaborates effective transaction sequences with directed inputs for the fuzzer. This focuses on exploring target paths and automatically validating whether the specified locations are vulnerable. Specifically, DFier employs static analysis to help locate target paths, facilitating their comprehensive exploration. Additionally, we devise three heuristic strategies to enable our fuzzing technique to generate directed inputs that effectively validate the targets. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius), while the performance losses are in an acceptable range.

The diversification of energy resources and equipment imports in the European Union

Efforts to reduce dependence on energy imports and increase domestic energy generation are often linked to imports of energy-generation equipment, which may lead to security issues. This study analyses the European Union's imports of energy and energy technologies in 2013–2023 using a common methodology based on a set of indicators. The analysis encompasses both well-established import diversification indicators and an extended version of the Herfindahl–Hirschman index (HHI), which considers the political stability of import sources. The results indicate that, while the diversification of energy imports has increased in recent years (the extended HHI for imports of the generalised group of energy and fuel products decreased from 0.16 in 2017 to 0.1 in 2022), imports of energy equipment are becoming increasingly concentrated (the extended HHI for energy technology imports into the EU increased from 0.16 in 2017 to 0.55 in 2022). This trend is particularly evident in the case of solar and energy storage technologies, which represent a significant vulnerability in the context of technology imports. Import dependency may have adverse consequences for the EU's ambitious energy transformation agenda.

Fit for purpose? Climate change, security and IR

As the contributions to this special issue suggest, IR has had a problematic relationship with environmental issues. Indeed it is difficult to avoid the conclusion that IR has treated environmental change almost as a distraction from important concerns of global politics, and gives us few significant resources for understanding these challenges or addressing them effectively. This is perhaps most starkly evident in the subfield of security studies, despite increasing recognition that environmental change warrants consideration as a security issue. This paper examines this engagement with a particular focus on climate change. Ultimately, the paper advances two arguments. First, examinations of the climate change–security relationship located in traditional security studies struggle to come to terms with the nature of the Anthropocene challenge and more specifically with the questions of who needs securing; what the nature of the threat posed is; and who is capable of or responsible for addressing this threat. Second, however, we can see progressive potential in engagement with the security implications of climate change in IR where such scholarship parts ways with traditional accounts of security; does not allow existing configurations of power to define the conditions for thinking about agency and sites of politics; and reflexively and self-consciously draws on insights from beyond the IR discipline. The increasing volume of work consistent with this more critical engagement is grounds for hope for this field of study in engaging productively even with a challenge as complex and significant as climate change.

What Do News Editorials Point to in the Framing of Nigerian National Security? Does Media Content Project Public Lens or Elites Power Hierarchies?

Growing cases of the role of media in national security tend to suggest that news is reflective of on the spot assessment. Yet, a corpus of literature shows that media influence is highly dominated by elites’ power, where media framing projects more of these power and influence. This paper therefore expounds on the extent to which news media editorials are reflective of public concerns or portray more of elites’ power. Succinctly, the aim is to explore what media editorials point to in the framing of national security issues. In view of the precarity of insecurity and mounting challenges of Nigeria’s national security, it is pertinent to unravel what constantly preoccupies media establishments, mostly, their editorial contents. And what trajectory and path do they tilt towards in the broader mediatization of the nation’s national issues occasioned by terrorism, militancy, banditry, kidnapping and other crimes. Overall, the paper provides a clue about how news editorials showcase and often pander to hierarchal power within Nigeria’s national security architecture.

GNSS Receiver Fingerprinting Based on Time Skew of Embedded CSAC Clock.

GNSS spoofing has become a significant security vulnerability threatening remote sensing systems. Hardware fingerprint-based GNSS receiver identification is one of the solutions to address this security issue. However, existing research has not provided a solution for distinguishing GNSS receivers of the same specification. This paper first theoretically proves that the CSACs (Chip-Scale Atomic Clocks) used in GNSS receivers have unique hardware noise and then proposes a fingerprinting scheme based on this hardware noise. Experiments based on the neural network method demonstrate that this fingerprint achieved an identification accuracy of 94.60% for commercial GNSS receivers of the same specification and performed excellently in anomaly detection, confirming the robustness of the fingerprinting method. This method shows a new real-time GNSS security monitoring method based on CSACs and can be easily used with any commercial GNSS receivers.

Towards intrusion detection in fog environments using generative adversarial network and long short-term memory network

Recently, fog computing has been developed to complement cloud computing, which can provide cloud services at the edge of the network with real-time processing. However, the computational power of fog nodes is limited and this leads to security issues. On the other hand, cyber-attacks have become common with the exponential growth of Internet of Things (IoT) connected devices. This fact necessitates the development of Intrusion Detection Systems (IDSs) in fog environments with the aim of detecting attacks. In this paper, we develop an IDS named GAN-LSTM for fog environments that uses Generative Adversarial Networks (GANs) and Long Short-Term Memory Networks (LSTMs). GAN-LSTM is used to identify anomalies in network traffic to specific types of attacks or non-attacks. In general, GAN-LSTM consists of three components: data preprocessing, generation of real traffic patterns, and sequence analysis of real traffic data. Data preprocessing ensures data quality by removing noise and irrelevant features. The pre-processed data is fed to the GAN to generate real traffic as a baseline for normal behavior. Finally, the LSTM component is applied to detect anomalous anomalies in fog computing. The proposed algorithm was evaluated on public databases and experimental results showed that GAN-LSTM improves the accuracy of attack detection compared to equivalent approaches.

Distributed control for DC microgrids with cyber attacks and constraints: A fault-tolerant model predictive controller

Communication security issues pose severe challenges to stability of microgrid system. This paper designs a cooperative control method based on fault-tolerant distributed model predictive control (DMPC) for DC microgrid against possible false information injection (FDI) attacks and communication constraints problems. First, a state variable is defined based on the voltage and current information, thereby deriving a system model of the microgrid under the cyber-physical framework. Then, since a combined uncertainty term consisting of attacks and disturbances exists in the constructed system model, an expanded state observer (ESO) is proposed to observe the uncertainty term. Further, based on the constructed system model and the designed ESO, a fault-tolerant distributed model predictive controller is designed. In the controller, DMPC is utilized to constrain the communication information and ensure the boundedness of the communication state and physical state. Simulation results indicate that the proposed DMPC-based distributed control method can achieve stable voltage regulation and accurate current sharing, and maintain smooth operation even under FDI attack, which verifies the feasibility for the proposed method.

Exploring GreenIoT's technical landscape: Sustainable development, deployment, use case and research challenges

The Internet of Things (IoT) has experienced exponential expansion, raising questions about its sustainability and environmental impact. In response, the developing discipline of Green Internet of Things(GreenIoT) seeks to address these problems by encouraging energy-saving and environmentally friendly IoT deployment practices. The technological environment of the GreenIoT is thoroughly explored in this article, including enabling technologies, simulators and platforms, smart applications, open projects, information on certificates and compliance, and open research challenges. The article focuses on intelligent applications that use GreenIoT concepts to meet sustainability objectives in a variety of industries, including smart buildings, transportation, agriculture, and healthcare. It demonstrates the revolutionary potential of GreenIoT in creating sustainable and environmentally friendly urban settings by outlining the fundamental characteristics, advantages, and actual implementations of these applications. The paper also highlights the value of open projects and initiatives in the GreenIoT space, looking at teamwork and research projects that promote innovation and advance energy-efficient IoT deployments, particularly in the context of India. The paper also discusses the significance of certificate and compliance information in ensuring that environmental standards and laws are followed in GreenIoT implementations. The study concludes by outlining the open research challenges in the GreenIoT, including creating specific GreenIoT architectures, controlling infrastructure complexity, resolving environmental security issues, and investigating energy-efficient communication protocols. These difficulties offer chances for more research, invention, and development in the area. It serves as a valuable resource for researchers, practitioners, and policymakers curious about the crosswords of IoT and sustainability.

Stochastic switching and analog-state programmable memristor and its utilization for homomorphic encryption hardware

Homomorphic encryption performs computations on encrypted data without decrypting, thereby eliminating security issues during the data communication between clouds and edges. As a result, there is a growing need for homomorphic encryption hardware (HE-HW) for the edges, where low power consumption and a compact form factor are desired. Here, a Pt/Ta2O5/Mo metallic cluster-type memristors (Mo-MCM) characterized by the Mo as a mobile species, and its utilization for the HE-HW via a 1-trasistor-1-memristor (1T1M) array as a prototype HE-HW is proposed. The Mo-MCM exhibits inherent stochastic set-switching behavior, which can be utilized for generating the random numbers required for encryption key generation. Furthermore, the device can accurately store analog conductance states after set-switching, which can be used as an analog non-volatile memristor. By simultaneously leveraging these two characteristics, encryption key generation, data encryption, and decryption are possible within a single device through an in-memory computing manner.