Security Solutions Research Articles

Enhancing IOT Security: Investigating Lightweight Encryption Algorithms for Resource-Constrained Devices

Abstract: In this work, we examine three LWCs: namely AES-128, SPECK, and ASCON, considering their presentation on resource-constrained IoT devices. With the large-scale deployment of IoT in all areas of industry, ensuring the security and efficiency of cryptographic implementations has become highly critical because typical IoT devices are usually characterized by limited memory, processing power, and energy resources. The study ascertains the memory usage, throughput, energy efficiency, and strength of security in the chosen algorithms by highly popular resource-constrained boards such as Arduino Nano and Arduino Micro. The benchmarks have shown that SPECK has the highest throughput, which is much efficient for constrained environments. One of the significant features of ASCON is that it has minimized memory usage and high energy efficiency. Therefore, it suits for long-term usage in IoT. AES-128 is secure but resource-intensive, so its usage may be irrelevant in very constrained environments. The results are of great value for IoT developers as a contribution to the selection of cryptographic algorithms based on the conditions and resource restrictions of the IoT devices involved so that optimized security solutions are achieved.

Comprehensive survey on security challenges and solutions in cognitive radio networks

Comprehensive survey on security challenges and solutions in cognitive radio networks

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

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

Machine learning techniques for enhancing security in financial technology systems

The financial technology (fintech) industry has transformed the way financial services are delivered, offering enhanced convenience, accessibility, and efficiency. However, this rapid digitization has also increased the sector’s exposure to a wide array of security risks, including cyberattacks, fraudulent activities, data breaches, and insider threats. As traditional security measures struggle to keep pace with the growing sophistication of these threats, machine learning (ML) techniques have emerged as promising tools for reinforcing security in fintech systems. Machine learning models can analyze vast amounts of data, detect anomalous behavior, predict potential risks, and respond to security threats in real-time. This paper explores the potential of machine learning to enhance security in financial technology by addressing key challenges such as anomaly detection, fraud detection, intrusion detection systems (IDS), and risk management. We provide an overview of common machine learning algorithms—such as decision trees, neural networks, support vector machines (SVM), and clustering methods—that are applied to these security tasks. Additionally, we discuss the evaluation metrics used to measure the accuracy, precision, recall, and overall effectiveness of these models. Through real-world case studies, we highlight successful implementations of machine learning in fintech security, offering insights into best practices and lessons learned. Despite the many benefits, there are also significant challenges and limitations associated with the adoption of machine learning in fintech, including concerns over data privacy, the accuracy and reliability of models, and the computational resources required for large-scale deployment. Looking ahead, this paper identifies emerging trends and potential innovations that could further enhance security in fintech, from advancements in deep learning to the integration of artificial intelligence with blockchain technology. Finally, we propose areas for future research to address unresolved issues and support the continued development of machine learning-driven security solutions in the fintech industry.

Urban Canopy Parameters’ Computation and Evaluation in an Indian Context Using Multi-Platform Remote Sensing Data

Urban Canopy Parameters (UCPs) are crucial for urban microclimate modeling; however, the scarce availability of precise UCP data in developing regions limits their application for urban climates. This study investigated the use of multi-platform remote sensing data viz. very high-resolution satellite (VHRS) optical stereo and Unmanned Aerial Vehicle (UAV) datasets for the computation of UCPs in high-density urban scenarios in India, with varied development characteristics. The results demonstrated high accuracy in terms of building height and footprint extraction from both datasets, key inputs for UCP computation. However, UCPs from UAV data have displayed relatively high accuracy for building footprints (86%), building height (RMSE ~ 0.05 m), and land use/land cover classification (90%). Performance evaluation of computed UCPs against a 3D reference geodatabase showed high prediction accuracy for most UCPs, with overall biases, mean absolute error, and root-mean-square error values significantly better than 1 m, with strong correlation (0.8–0.9). It was concluded that VHRS optical stereo and UAV datasets offer a secure, reliable, and accurate solution for UCP computation in urban areas, particularly in developing regions. These findings have significant implications for urban climate research and the sustainable development of rapidly urbanizing areas facing resource and policy constraints.

Do mercenaries perform better than states? Evaluating the Wagner group’s impact on Central African Republic

ABSTRACT Do mercenaries perform better than democratic state armies? This study examines the efficacy of mercenary groups compared to state military interventions, using the Wagner Group’s operations in the Central African Republic as a case study. By comparing the French Operation Sangaris (2013–2016) with Wagner’s involvement (2021–2023), the research evaluates their relative effectiveness across counterinsurgency measures, military success, and popular responses. Utilizing data from the Armed Conflict Location & Event Data Project, the analysis reveals that the Wagner Group outperformed the French intervention in most metrics, including territorial gains, geographical spread of operations, and reduction of civil unrest. These findings challenge prevailing assumptions about mercenaries’ ineffectiveness. The study suggests that the Wagner Group’s perceived effectiveness, despite its controversial reputation and association to human rights violations, explains its appeal to African governments seeking security solutions. This research contributes to ongoing debates about the role of private military companies in conflict zones and has implications for understanding the changing dynamics of security provision in Africa and beyond.

Performance Evaluation and Economic Sustainability of the PAU Model Batch Type Paddy Straw Biogas Plant

This study evaluates the performance and economic viability of the Punjab Agricultural University (PAU) Model Batch Type Paddy Straw Biogas Plant in rural India, addressing critical challenges in agricultural residue management and sustainable energy production. Data collected from two plants over four months showed an average daily biogas production of 1.46 to 3.13 m3 per day, with a total gas production of 284.8 m3 in a period of four months. Plant performance correlated moderately with ambient temperature. Economic analysis revealed annual savings of Rs. 47,600 through reduced LPG use and biogas slurry sales. The study concludes that the PAU Model is technically efficient and economically viable, offering a sustainable solution for paddy straw management and rural energy security. These findings have significant implications for policymakers and rural development practitioners in promoting sustainable agriculture and renewable energy adoption in developing regions.

Hardware assurance with silicon photonic physical unclonable functions

In the modern landscape of optical communication networks, ensuring robust security is increasingly critical, particularly for applications requiring seamless integration and minimal attack surfaces. Photonic Physical Unclonable Functions (PUFs) leverage the response from the photonic devices that are prone to inherent physical variations to generate unique and unpredictable signature identifiers which are then utilized by an authentication system for identification or encryption purposes. These photonic PUFs can be cohesively integrated into systems that use optical communication, whereas using electronic PUFs would introduce additional vulnerabilities due to the need for signal-domain conversions between optical and electronic signals. In this paper, we present the design, fabrication, and experimental evaluation of advanced silicon-photonic-based PUFs utilizing Contra-Directional Coupler (CDC) structures. These structures offer a complex design space and are intrinsically sensitive to fabrication-process variations, making them ideal for creating unique and secure responses. We introduce several innovative design enhancements, including randomized corrugation functions, perforated designs, and ring-assisted CDCs, to increase the complexity and unpredictability of the CDC response. Measurement results from the fabricated CDCs demonstrate their capability to achieve an average Hamming distance threshold of over 0.2, effectively distinguishing between legitimate devices and their copies. We rigorously tested these fabricated designs against three different machine-learning-based attack scenarios. The results showed a Hamming distance of over 0.4 with a standard deviation of less than 0.01 at a quantization level of three, using 10,000 samples of challenge-response pairs. These findings underscore the potential of silicon photonic PUFs in enhancing security for optical communication systems of different scales. The integration of such photonic PUFs offers robust and reliable security solutions for applications where traditional electronic methods introduce additional attack surfaces and fail to provide adequate protection.

Coupled hydro-mechanical model for underground hydrogen storage undergoing cyclic injection and production

ABSTRACT Excess renewable energy can be used to generate and store hydrogen underground, offering a secure and econimical solution to the intermittency challenges of renewable energy sources. The success of underground storage relies on understanding and controlling the geo-mechanical integrity of storage sites during cyclic loadings. While geological deformation during natural gas and carbon dioxide storage is well studied, few have investigated pore pressure and in-situ stress variations during cyclic loading, which is unique to underground hydrogen storage sites. This paper presents a predictive analytical model for the pore pressure and stress changes during cyclic loading. The model was derived based on continuous point source injection, a basic hydro-mechanical model while applying the superposition to consider the cyclic loading. The developed model was evaluated for hydrogen and natural gas subjected to cyclic injection and production. Comparing hydrogen and natural gas storage cases can offer valuable insights into the behavior of hydrogen since the deformation of natural gas storage is more studied. Finally, analytical solutions for both storage cases were validated by numerical models with discrepancies between the models amounting to less than 1%. Comparative analysis of the results from 3 to 99 cycles of injection and production for both hydrogen and methane demonstrates that rise in cycle numbers leads to a corresponding rise in stress accumulation in the system. Neglecting the stress accumulation during these cycles can lead to significant geomechanical issues, potentially compromising the storage system’s safety and integrity.

Federated learning system on autonomous vehicles for lane segmentation

Autonomous Vehicles (AV) is one of the most evolving industries in the last decade. However, one of the bottlenecks of this evolution is providing data that contains different scenarios and scenes to improve the models without exposing the privacy and security of the edge vehicles. The authors of this research propose a secure and efficient novel solution for lane segmentation in AVs through the use of Federated Learning (FL). FedLane involves initial training of U-Net, ResUNet, and ResUNet++ models, followed by real-time inference in edge devices and the application of FL to update the server model using clients’ data. The study found that FL has enhanced the performance of lane segmentation significantly over baseline, enabling decentralized privacy-preserving collaborative optimization with increased dice coef from 0.9429 to 0.9794 for U-Net, from 0.9291 to 0.9854 for ResUNet and from 0.9079 to 0.9675 for ResUNet++. Additionally, the models show increased stability over the training iterations, highlighting the potential of FL to play a significant role in the future of automation in the AV industry.

Quantum Physical Unclonable Function Based on Multidimensional Fingerprint Features of Single Photon Emitters in Random AlN Nanocrystals

AbstractPhysical unclonable function (PUF) has emerged as a unique physical'fingerprint' that is inherently difficult to replicate. It shows tremendous application value in various hardware security areas such as identity authentication, chip anticounterfeiting, communication encryption, blockchain, etc. However, with the rapid development of 3D nanoprinting, classical PUFs constructed with disordered micro‐nanostructures face tremendous threats from physical cloning attacks. Herein, this study proposes and demonstrates the utilization of room‐temperature single‐photon emitters derived from atomic defects in randomly distributed pyramidal aluminum nitride (AlN) nanocrystals as a novel quantum PUF to resist physical cloning attacks. The fabrication of this quantum PUF on silicon (Si) wafers enables seamless integration with silicon photonic integrated circuits. The multidimensional fingerprint features of the single‐photon emitters are highly sensitive to the lattice parameters of the uneven AlN nanocrystals. Furthermore, each single photon emitter can work as a quantum random number generator to ensure the fundamental unpredictability of PUFs. The subatomic precision requirement coupled with unpredictable quantum emission behavior makes it practically impossible to attack the proposed quantum PUF, providing a promising solution for information security in the post‐quantum era.

Deep learning models security: A systematic review

Deep learning models and the digital records they generate have remarkably increased their adoption of many practical applications. While the success of deep learning in multimedia applications, especially images, helps tackle some of the most challenging problems, one of its copyright violations, ownership conflict, poses a grave concern for many potential applications. Many works on intellectual property protection for such models have proposed to verify ownership. Therefore, it is necessary to conduct a comprehensive study on the security of deep learning models to evaluate their strong background, state-of-the-art solutions, possible attacks, current limitations and notable improvements. This survey attempts to systematically discuss and summarise the recent advanced security solutions for deep learning models through watermarking, encryption and fingerprinting. Our study explores the recent applications, possible attacks, current limitations and notable suggestions regarding deep learning. It also comprehensively evaluates the recent research gaps and opportunities in detail to empower researchers and practitioners to provide additional secure solutions for deep learning models. This extensive survey is the first to consider model security through several notable techniques.

A Novel Detection Transformer Framework for Ship Detection in Synthetic Aperture Radar Imagery Using Advanced Feature Fusion and Polarimetric Techniques

Ship detection in synthetic aperture radar (SAR) imagery faces significant challenges due to the limitations of traditional methods, such as convolutional neural network (CNN) and anchor-based matching approaches, which struggle with accurately detecting smaller targets as well as adapting to varying environmental conditions. These methods, relying on either intensity values or single-target characteristics, often fail to enhance the signal-to-clutter ratio (SCR) and are prone to false detections due to environmental factors. To address these issues, a novel framework is introduced that leverages the detection transformer (DETR) model along with advanced feature fusion techniques to enhance ship detection. This feature enhancement DETR (FEDETR) module manages clutter and improves feature extraction through preprocessing techniques such as filtering, denoising, and applying maximum and median pooling with various kernel sizes. Furthermore, it combines metrics like the line spread function (LSF), peak signal-to-noise ratio (PSNR), and F1 score to predict optimal pooling configurations and thus enhance edge sharpness, image fidelity, and detection accuracy. Complementing this, the weighted feature fusion (WFF) module integrates polarimetric SAR (PolSAR) methods such as Pauli decomposition, coherence matrix analysis, and feature volume and helix scattering (Fvh) components decomposition, along with FEDETR attention maps, to provide detailed radar scattering insights that enhance ship response characterization. Finally, by integrating wave polarization properties, the ability to distinguish and characterize targets is augmented, thereby improving SCR and facilitating the detection of weakly scattered targets in SAR imagery. Overall, this new framework significantly boosts DETR’s performance, offering a robust solution for maritime surveillance and security.

СУ ҚАУІПСІЗДІГІ: ФОРСАЙТ-ЗЕРТТЕУЛЕР ПРИЗМАСЫ АРҚЫЛЫ НЕГІЗГІ АСПЕКТІЛЕР МЕН ПЕРСПЕКТИВАЛАР

In the modern world, water security issues are becoming increasingly relevant and are key to ensuring the sustainable development of society. The world community faces serious challenges related to the threats of pollution of water sources, climate change, ecosystem instability and inefficient management of water resources. Solving these problems requires not only immediate measures, but also long-term strategies that can ensure the preservation of water security for the future. The research work is an analytical review of current problems in the field of water security and possible strategic solutions to solve them using foresight research methods. The article highlights the current threats and challenges facing water resources in the modern world, and discusses the advantages and effectiveness of applying foresight research methods in this area. Within the framework of the conducted scientific research, the key areas of strategic planning for ensuring water security in the future are highlighted through the highlighted through the analysis of the current state and forecasting potential changes in the water sector.

Elliptic curves cryptography for lightweight devices in IoT system

The Internet of Things (IoT) is revolutionizing industries by connecting billions of devices, ranging from home appliances to industrial machines, to the internet. This transformation enables unprecedented automation, data collection, and process optimization. However, the widespread adoption of IoT also introduces significant security challenges, especially for lightweight devices with limited computational power and energy resources. Ensuring secure communication and data integrity in such constrained environments is critical. This paper explores and compares the performance of implemented algorithms within IoT devices. A comparison between Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC) implementations is made to identify a lightweight, secure, and efficient solution for IoT environments. Our results indicate that ECC outperforms RSA in terms of memory requirements, overall energy consumption, key sizes, signature generation time, key generation and execution time, and decryption time in a resource-constrained environment. However, RSA performs better in signature verification and encryption.

Towards Scalable and Secure Blockchain in Internet of Things: A Preference-Driven Committee Member Auction Consensus Approach

Blockchain technology is acclaimed for eliminating the need for a central authority while ensuring stability, security, and immutability. However, its integration into Internet of Things (IoT) environments is hampered by the limited computational resources of IoT devices. Consensus algorithms, vital for blockchain safety and efficiency, often require substantial computational power and face challenges related to security, scalability, and resource demands. To address these critical issues, we propose a novel model that significantly enhances the security and performance of blockchain in IoT environments. Our model introduces three key innovations: (1) a bidirectional-linked blockchain system that strengthens security against long-range attacks by exploiting dual reference points for block validation; (2) the integration of user preferences into the Committee Member Auction (CMA) consensus algorithm, optimizing miner selection to balance resource efficiency with security; and (3) a comprehensive performance and frequency analysis that demonstrates the system’s resilience against double-spend, long-range, and eclipse attacks. The proposed model not only reduces block validation delays but also enhances overall system performance, as evidenced by simulations comparing its effectiveness with existing CMA algorithms. These advancements have the potential to significantly impact the deployment of blockchain in resource-constrained IoT environments, offering a more secure and efficient solution.

Autonomous Vehicle Diagnostics and Support: A Framework for API-Driven Microservices

The rapid evolution of autonomous vehicles (AVs) demands robust and scalable diagnostic and support systems to ensure seamless operations, safety, and performance. This paper presents a framework for API-driven microservices tailored to address the unique diagnostic and support requirements of AVs. Traditional monolithic systems are insufficient for managing the complex, real-time data generated by AVs, necessitating a shift toward microservices architecture. By leveraging API-driven microservices, the proposed framework ensures modularity, scalability, and real-time communication between various vehicle components and external support systems. The framework is designed to support continuous vehicle monitoring, predictive maintenance, fault detection, and real-time decision-making, all while minimizing latency. APIs play a critical role in enabling interaction between different microservices, allowing for seamless data exchange across diverse platforms. This interoperability facilitates integration with third-party services, such as cloud-based diagnostics, real-time traffic information, and software updates. Additionally, the microservices architecture ensures fault isolation, meaning that failures in one service do not compromise the entire system. The framework also emphasizes the importance of cybersecurity and data privacy. Given the sensitive nature of AV data, API security protocols such as OAuth2 and TLS encryption are incorporated to safeguard communication between microservices and external systems. Furthermore, the design is scalable, allowing for the integration of future AV technologies and third-party service enhancements without disrupting existing functionalities. In conclusion, API-driven microservices provide an efficient, flexible, and secure solution for autonomous vehicle diagnostics and support. This framework has the potential to improve AV reliability, reduce downtime, and enhance safety by enabling real-time, data-driven decision-making and proactive maintenance. Future work may explore the integration of machine learning algorithms to further optimize diagnostics and predictive maintenance capabilities. Keywords: Autonomous Vehicles, Microservices, API-Driven Architecture, Diagnostics, Predictive Maintenance, Real-Time Data, Cybersecurity, Vehicle Support Systems, Modularity, Scalability.

Modelling Decision and Reaction Against Detected Intrusions in IoT System Basing on Security Policy Rules

In recent years, The Internet of Things (IoT) is considered as one of the main technological revolutions, it connects heterogeneous devices, peoples, and services in order to exchange information and to improve existing deployments in different sectors. So far, existing security solutions are not adapted to this development, considering that IoT resources are exposed to different intrusions, which impact security management efficiency and the need for human interventions to increase. Although, it is too hard to depend on manual approaches that require the deep involvement of security managers to deliver the aimed security level. Therefore, a new solution is needed, that will facilitate the decision against detected intrusions and according to their magnitudes and their intentions to put the necessary reaction in the right place. In this context, this paper proposes a model which specify how the decision and correlation scenario will be carried out when a critical alert comes from the intrusion detector basing on security policy rules. This model simplifies and facilitates the decision and reaction against detected intrusions by enriching it with the rules defined in security policy to ensure the protection of IoT resources and help security administrators to make the right decisions in other tasks.

Data Mining Approach for Evil Twin Attack Identification in Wi-Fi Networks

Recent cyber security solutions for wireless networks during internet open access have become critically important for personal data security. The newest WPA3 network security protocol has been used to maximize this protection; however, attackers can use an Evil Twin attack to replace a legitimate access point. The article is devoted to solving the problem of intrusion detection at the OSI model’s physical layers. To solve this, a hardware–software complex has been developed to collect information about the signal strength from Wi-Fi access points using wireless sensor networks. The collected data were supplemented with a generative algorithm considering all possible combinations of signal strength. The k-nearest neighbor model was trained on the obtained data to distinguish the signal strength of legitimate from illegitimate access points. To verify the authenticity of the data, an Evil Twin attack was physically simulated, and a machine learning model analyzed the data from the sensors. As a result, the Evil Twin attack was successfully identified based on the signal strength in the radio spectrum. The proposed model can be used in open access points as well as in large corporate and home Wi-Fi networks to detect intrusions aimed at substituting devices in the radio spectrum where IEEE 802.11 networking equipment operates.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).