Security Applications Research Articles

Learned phase mask to protect camera under laser irradiation

The electro-optical imaging system works under focus conditions for clear imaging. However, under unexpected laser irradiation, the focused light with extremely high intensity can easily damage the imaging sensor, resulting in permanent degradation of its perceptual capabilities. With the escalating prevalence of compact high-performance lasers, safeguarding cameras from laser damage presents a formidable challenge. Here, we report an end-to-end method to construct the wavefront coding (WFC) imaging systems with simultaneous superior laser protection and imaging performance. In the optical coding part, we employ four types of phase mask parameterization methods: pixel-wise, concentric rings, linear combinations of Zernike bases, and odd-order polynomial bases, with parameters that are learnable. In the algorithm decoding part, a method combined of deconvolution module and residual-Unet is proposed to furthest restore the phase-mask-induced image blurring. The optical and algorithm parts are jointly optimized within the end-to-end framework to determine the performance boundary. The governing rule of the laser protection capability versus imaging quality is revealed by tuning the optimization loss function, and the system database is established for various working conditions. Numerical simulations and experimental validations both demonstrate that the proposed laser-protection WFC imaging system can reduce the peak single-pixel laser power by 99.4% while maintaining high-quality imaging with peak signal-to-noise ratio more than 22 dB. This work pioneers what we believe to be a new path for the design of laser protection imaging systems, with promising applications in security and autonomous driving.

Near-complete extraction of maximum stored energy from large-core fibers using coherent pulse stacking amplification of femtosecond pulses

High field science relies on ultrashort pulse lasers with multi-joule pulse energies for studying light–matter interactions under extreme conditions and for driving particle accelerators and secondary radiation sources of x rays, gamma rays, neutrons, positrons, muons, and protons. Next-generation laser drivers will require a 103−104 times increase in pulse repetition rates, producing multi-joule energies at multi-kilowatt average powers to enable practical applications in nuclear engineering, advanced materials, medicine, biology, homeland security, and high-energy physics. Spatially coherently combined femtosecond fiber lasers are recognized as a pathway to these next-generation drivers, with significant practical advantages including high efficiency and the possibility of compact integration. However, chirped pulse amplification in fibers is capable of extracting only a small fraction (usually ∼1%) of the maximum stored energy. Here we demonstrate near-complete maximum stored energy extraction with low accumulated nonlinearity from a large-core fiber amplifier using coherent pulse stacking amplification. We have amplified a 81-pulse stacking burst in a 85 µm core chirally coupled core Yb-doped fiber, extracting up to 9.5 mJ (∼90% of stored energy) with <4.5 radians of accumulated nonlinear phase, temporally combined this burst into a single pulse, and achieved 4.2 mJ pulses of 313 fs bandwidth-limited duration after compression. This represents, to our knowledge, the highest energy extracted and compressed into a femtosecond pulse from a single fiber amplifier, enabling approximately two orders of magnitude size reduction of future high-energy coherently spatially combined fiber laser arrays.

Enhancing Structured Query Language Injection Detection with Trustworthy Ensemble Learning and Boosting Models Using Local Explanation Techniques

This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gain unauthorized access, and manipulate data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including decision tree, random forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that decision tree, random forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAPs) and Local Interpretable Model-agnostic Explanations (LIMEs) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach to detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications.

Systematic Approach to Prevent Code Vulnerabilities using CI/CD Pipelines

This paper discusses a systematic approach to integrating Static Application Security Testing (SAST), Software Composition Analysis (SCA), Code Coverage, and Code Quality Checks into Continuous Integration/Continuous Delivery (CI/CD) pipelines. Modern CI/CD pipelines accelerate software delivery but introduce significant security and quality challenges. By incorporating SAST and SCA for security testing, along with code coverage and quality checks, organizations can prevent code vulnerabilities and ensure the maintainability and reliability of their applications. This approach helps development teams shift security and quality controls left, catching issues early in the development lifecycle. Keywords — CI/CD, DevSecOps, Static Application Security Testing (SAST), Software Composition Analysis (SCA), Code Coverage, Code Quality, Vulnerability

Analysis of Personal Privacy Leakage under the Background of Digital Twin

With the development and application of digital twin technology, the problem of privacy leakage is becoming increasingly prominent. Privacy leakage issues caused by data abuse and weak awareness have attracted high attention. In this regard, this paper analyzes the causes of privacy leakage in the digital twin, analyzes the difficulties of privacy protection under the background of the digital twin, and puts forward the “legal co-governance model.” Through multi-party cooperation, improving the system, and improving literacy, people can effectively protect private information, ensure the security application of technology, law, and ethics, and provide a guarantee for the sustainable development and social application of digital twin technology.

Temporal-Incremental Learning for Android Malware Detection

Malware classification is a specific and refined task within the broader malware detection problem. Effective classification aids in understanding attack techniques and developing robust defenses, ensuring application security and timely mitigation of software vulnerabilities. The dynamic nature of malware demands adaptive classification techniques that can handle the continuous emergence of new families. Traditionally, this is done by retraining models on all historical samples, which requires significant resources in terms of time and storage. An alternative approach is Class-Incremental Learning (CIL), which focuses on progressively learning new classes (malware families) while preserving knowledge from previous training steps. However, CIL assumes that each class appears only once in training and is not revisited, an assumption that does not hold for malware families, which often persist across multiple time intervals. This leads to shifts in the data distribution for the same family over time, a challenge that is not addressed by traditional CIL methods. We formulate this problem as Temporal-Incremental Malware Learning (TIML), which adapts to these shifts and effectively classifies new variants. To support this, we organize the MalNet dataset, consisting of over a million entries of Android malware data collected over a decade, in chronological order. We first adapt state-of-the-art CIL approaches to meet TIML's requirements, serving as baseline methods. Then, we propose a novel multimodal TIML approach that leverages multiple malware modalities for improved performance. Extensive evaluations show that our TIML approaches outperform traditional CIL methods and demonstrate the feasibility of periodically updating malware classifiers at a low cost. This process is efficient and requires minimal storage and computational resources, with only a slight dip in performance compared to full retraining with historical data.

Development of Mathematical Algorithm for Detecting XSS Attacks on Web Applications

The widespread usage of web applications has led to an increase in security threats, with Cross-Site Scripting (XSS) attacks being one of the most prevalent and damaging. Detecting and mitigating XSS attacks is crucial to ensure the integrity and confidentiality of sensitive user data. This article presents the mathematical algorithm and a way to identify XSS attacks using a bounded function from below, which depends on the input string, and highlights its potential impact in bolstering web application security. To construct this function, we used special characters and keywords that are frequently found in the construction of XSS attacks.

Watch Out for the Inherent Vulnerabilities in Developing Multi-tenant Cloud-FPGA: Communication Protocols

As FPGAs are being deployed in the cloud infrastructure for acceleration, the technology of multi-tenant FPGA has emerged as a topic of interest. This development has drawn considerable attention to its security issues. While previous research primarily focused on the security of applications, there has been limited exploration of the vulnerabilities inherent in FPGA IPs. In our work, we examine the vulnerabilities of two widely used data transmission protocols in modern FPGAs: the Advanced eXtensible Interface (AXI) and Peripheral Component Interconnect Express (PCIe). Our experiments, conducted with commercial FPGA development kits, launched fault injection attacks through the shared power distribution network (PDN). Through non-invasive electromagnetic (EM) trace measurement, we characterize the voltage fluctuation across various attack patterns. Subsequently, we simulate real-world data transfers using two crafted datasets with different statistical characteristics. The experimental results demonstrate the unique security vulnerabilities of the current AXI and PCIe protocols in the context of a multi-tenant cloud-FPGA. In response to such vulnerability, we further propose two defense strategies: InChAXI that utilizes integrity checking for AXI-based data, and FCPCIe that employs frequency scaling for PCIe-based data. The performance evaluation demonstrates that our proposed defenses can significantly reduce the fault injections on the AXI-based data transmission by 705 times with small overheads – 0.5% in hardware footprint and 7.9% in latency, respectively. On the other hand, FCPCIe effectively prevents the fault injection attack during the PCIe-based data transmission by reducing the user clock frequency, while incurring a 10.13% overhead on data throughput.

Enhancing the SRAM PUF with an XOR Gate

This study focuses on designing enhanced Physically Unclonable Functions (PUFs) based on SRAM devices and improving the security of cryptographic systems. Most SRAM PUFs are limited in their number of CRPs, which makes them vulnerable to enrollment attacks. In this research, we present an SRAM-based PUF design that greatly increases the number of CRPs and the entropy of the generated bits by performing exclusive-or (XOR) on the responses of two SRAM devices. This was implemented using a readily available development board, SRAM devices, and a user-friendly custom circuit board for cryptographic key generation. The cryptographic protocol was implemented using both C++ and python3. The proposed SRAM PUF design was experimentally demonstrated and showed substantial improvements in the security of various cryptographic applications as a hardware authentication device. It also addresses the specific vulnerabilities of legacy designs.

Three-Level Chirality Transfer and Amplification in Liquid Crystal Supramolecular Assembly for Achieving Full-Color and White Circularly Polarized Luminescence.

Chiral liquid crystal supramolecular assembly provides an ideal strategy for constructing excellent circularly polarized luminescence (CPL) materials. However, the chirality transfer in chiral liquid crystals normally occurs at two levels from the configurational chirality to the supramolecular phase chirality. The more precise and more levels of chirality transmission are fascinating but remain challenging. The present work reports the first success of three-level chirality transfer and amplification from configurationally point chirality of small molecules to conformationally helical chirality of helical polymers and finally to supramolecular phase chirality of cholesteric liquid crystals composed of chiral nonfluorescent polymers (P46) and nematic liquid crystals. Noticeably, the helical twisting power of P46 is five-fold larger than its monomer. Full-color and white CPL with maximum luminescence dissymmetry factor up to 1.54 and photoluminescence quantum yield up to 63.8% are realized utilizing helical supramolecular assembly combined with selective reflection mechanism. Also significantly, the electrically stimuli-responsive CPL switching device as well as anti-counterfeiting security, information encryption, and chiral logic gate applications are developed. This study deepens the understanding of chirality transfer and amplification across different hierarchical levels.

Influence of carbon quantum dots as modifier and SiO2 shell coating as a protecting layer in YAlO3:Cr3+ phosphors for multimodal applications

This study focuses on the synthesis of SiO2-coated carbon dots (CDs) grafted onto YAlO3:Cr3+ (YAP:Cr3+) nanophosphors (NPs) through a combustion method. The CDs enhance the absorption cross-section of Cr3+ ions, significantly boosting the luminescence of YAP:Cr3+ by 19.29-fold. Further improvement is achieved with a SiO2 coating, leading to a 48.73-fold increase in photoluminescence intensity. The NCs are colour tunable, with emissions ranging from pinkish-red to blue, depending on the concentration of CDs and the excitation wavelength. These properties make them suitable for diverse applications, such as ratio metric fluorescence sensors for detecting Fe3+ ions within the 0–350 µM range, with high sensitivity (0.2 µM) and a low detection limit (4.93 μM). Additionally, they serve as material for white light-emitting diodes (W-LEDs), achieving CIE coordinates of (0.31, 0.30) and a rendering index of 94. The NCs also function as an optical thermometer with a sensitivity of 0.31 % K−1 at 320 K, making them ideal for temperature sensing applications. Moreover, their stability and dual-emissive nature make them highly effective in latent fingerprints (LFPs) detection, enabling high-resolution, level III fingerprint analysis. The dual emission properties of CDs and Cr3+ ions position these NCs as multifunctional materials, suitable for optical sensing, lighting, and security applications.

Elliptic curve cryptography: Theory, security, and applications in modern network security

Abstract. Due to the swift advancement of Internet and computer technology in the 21st century, the demand for network security is increasing. Classic cryptographic algorithms like Rivest-Shamir-Adleman (RSA) and Digital Signature Algorithm (DSA) are insufficient in the face of modern network environments, while elliptic curve cryptography (ECC) has become a research hotspot due to its high security and high efficiency. The purpose of this paper is to discuss the theoretical basis, security analysis, and practical application cases of elliptic curve cryptography, to provide readers with a comprehensive understanding and trigger further research and thinking. This paper analyzes the theoretical basis of ECC, including group theory, domain theory, and the definition and properties of elliptic curves, and analyzes the application of ECC in combination with practical application cases, such as the SM2 algorithm. The article first introduces the concept of groups, the definition of domains, and the basic properties of elliptic curves. Then, the security of ECC is analyzed, especially the complexity of the Elliptic Curve Discrete Logarithm Problem (ECDLP) and the selection of key length. In addition, the security of ECC in practical applications is discussed, including digital signatures, key exchange protocols, and applications in blockchain technology. The results show that ECC provides comparable security to traditional public key algorithms at a short key length, and shows strong security and efficiency in practical applications. As technology progresses and new threats arise, research in ECC will also evolve.

Laser-engraved holograms as entropy source for random number generators

Our study introduces a novel approach to true random number generation (TRNG) using speckle patterns generated by laser-engraved holograms on carbon fiber-reinforced polymer (CFRP) composite substrates. Unlike previous methods, our approach simplifies the process by generating the necessary image dataset from a single microscope image of the engraved hologram. We achieve a high extraction ratio of 76 %, demonstrating the effectiveness of our TRNG. Moreover, our method successfully passes rigorous statistical tests proposed by the National Institute of Standards and Technology (NIST), indicating its suitability for cryptographic and secure system applications. This work offers promising implications for enhancing security in various domains, from secure communication networks to IoT devices.

Multimodal biometric identification: leveraging convolutional neural network (CNN) architectures and fusion techniques with fingerprint and finger vein data

Advancements in multimodal biometrics, which integrate multiple biometric traits, promise to enhance the accuracy and robustness of identification systems. This study focuses on improving multimodal biometric identification by using fingerprint and finger vein images as the primary traits. We utilized the “NUPT-FPV” dataset, which contains a substantial number of finger vein and fingerprint images, which significantly aided our research. Convolutional neural networks (CNNs), renowned for their efficacy in computer vision tasks, are used in our model to extract distinct discriminative features. Specifically, we incorporate three popular CNN architectures: ResNet, VGGNet, and DenseNet. We explore three fusion strategies used in security applications: early fusion, late fusion, and score-level fusion. Early fusion integrates raw images at the input layer of a single CNN, combining information at the initial stages. Late fusion, in contrast, merges features after individual learning from each CNN model. Score-level fusion employs weighted aggregation to combine scores from each modality, leveraging the complementary information they provide. We also use contrast limited adaptive histogram equalization (CLAHE) to enhance fingerprint contrast and vein pattern features, improving feature visibility and extraction. Our evaluation metrics include accuracy, equal error rate (EER), and ROC curves. The fusion of CNN architectures and enhancement methods shows promising performance in identifying multimodal biometrics, aiming to increase identification accuracy. The proposed model offers a reliable authentication system using multiple biometrics to verify identity.

FINANCIAL PROVISION OF MARKETING ACTIVITIES OF MACHINE-BUILDING ENTERPRISES FOR STRENGTHENING COMPETITIVENESS IN THE CONTEXT OF A NEW REALITY

The purpose of this article is to develop a methodological approach to evaluating all possible options for the financial provision of the marketing activities of modern machine-building enterprises under conditions of full-scale war. Thus, the new conditions of reality are characteristic specifically of Ukrainian machine-building enterprises, which become the key object of the study. As a result, within this article, the optimal forms of financial provision, both external and internal, were chosen based on evaluation results, which will allow not only to "survive" in the current conditions for Ukrainian machine-building enterprises but also to strengthen their competitiveness in the face of new challenges and threats. The identification of the most optimal forms of financial provision and the conduct of evaluations were made possible through the implementation of expert analysis, using the Delphi method. The central place in the methodology of the article is occupied by the method of hierarchical multicriteria evaluation. Its thorough substantiation has better reflected the main results of the research. The developed methodological approach is distinguished by its convenience and practical value in the conditions of the new reality, that is wartime. Another feature is the consideration of not only internal resources but also external ones. Ultimately, the proper and secure application of the defined forms of internal and external financial provision of marketing activities to change their own competitiveness was also substantiated. This, in turn, allowed forming the correct trajectory for how to evaluate one’s own financial provision options for marketing activities and how to apply them to enhance one’s competitiveness. The practical significance of the obtained results lies in substantiating recommendations for establishing the optimal form of financial provision of marketing activities for machine-building enterprises.

The role of APIs in modern software development

Application Programming Interfaces (APIs) play a critical role in modern software development, enabling seamless communication between disparate systems and enhancing the scalability, modularity, and security of applications. This research investigates the impact of APIs on software architecture, focusing on their use in facilitating interoperability across distributed environments. Using a mixed-methods approach, the study combines a literature review, a survey of 50 software developers, and case studies from industry leaders such as Amazon Web Services (AWS), Google, and Facebook. The findings reveal that REST APIs are the most widely used (75%), while GraphQL is gaining popularity for its ability to optimize data retrieval in complex systems. Security challenges, particularly vulnerabilities in REST APIs, were highlighted by 35% of developers, underscoring the need for stronger authentication and encryption methods like OAuth. This study also explores how APIs foster innovation by enabling third-party integrations, driving ecosystem growth in cloud computing and other sectors. The research concludes that while APIs are vital for the future of software development, addressing security risks and improving API management practices will be essential for maximizing their benefits in emerging technologies such as IoT and AI.

Emerging New-Generation Semiconductor Single Crystals of Metal Halide Perovskites for Radiation Detection

Radiation detection uses semiconductor materials to convert high-energy photons into charge (direct detection) or low-energy photons (indirect detection), and it has a wide range of applications in nuclear physics, medical imaging, astronomical detection, homeland security, and other fields. Metal halide perovskites have the advantages of high frequency number, high carrier mobility, high defect tolerance, low defect density, adjustable band gap, and fast light response, and they have wide application prospects in the field of radiation detection. However, the research is still in its infancy stage, and it is far from meeting the requirements of industrial application. This paper focuses on the advantages of metal halide perovskite single-crystal materials in both semiconductors-based direct conversion detection and scintillator-based indirect detection as well as the latest progress in this promising field. This paper not only introduces the latest application of lead halide perovskite monocrystalline materials in high-energy electromagnetic radiation detection (X-ray and γ-rays), but it also introduces the latest development of α-particle/β-particle/neutron detection. Finally, this paper points out the challenges and future prospects of metal halide perovskite single-crystal materials in radiation detection.

Establishment of Theoretically Guided Understanding for Magneto‐Optical Interactions through Ferromagnetic Fluids in Uniform, Static Magnetic Field

Prior magneto‐optical transmission models through ferrofluids are limited by oversimplifications which hinder accuracy and generalizability. To overcome this challenge, this study models the magnetic field effect on the magneto‐optical transmission through hematite ferrofluid using the method of invariant embedding. Optical coherence tomography (OCT) is used to extract the changes in the refractive index, enabling a discovery of a magnetodielectric effect for the hematite ferrofluid. To establish a basis for control and understanding, the influence of the magnetic field on the spatial transmission profile at different wavelengths, optical propagation lengths, and the effect of variation of each size parameter on the optical transmission have been investigated. Further parametric studies allowed finding out an analogy to nonuniform, cascade, volume holographic diffraction gratings, which is crucial for a wide range of optical and bioimaging applications. Compared to the experimental results, the presented model achieves 99% accuracy over the wavelength range (300–1100) nm under uniform static magnetic field (0–6.5) mT. Besides, the conspicuous lead of evidentiary understanding of magneto‐optical interactions with magnetic fluids, the structural milestones of the model can be further utilized to model similar challenging constructs in complex media. The innumerable applications of study extend to involve communication engineering, biomedical, optical, and security applications.

Biosensing technology for detection and assessment of pathogenic microorganisms.

At present, the prevalence of infectious diseases is rising annually, making it an important risk factor for human health that should not be neglected. Consequently, infection control and prevention have become even more important. The key to determining and designing the most effective anti-infectious medication depends upon the immediate and accurate identification of the causative agent. The standard techniques used for routine infection screening and surveillance tests are shifting toward biosensors. Furthermore, biosensors are projected to be employed for microbiological detection to satisfy the higher accuracy required for clinical diagnosis. This is because of their compact size, real-time monitoring and ability to analyze large sample numbers with less sophistication and manpower requirement, which have allowed them to develop quickly with extensive uses. Biosensors have multiple applications in food safety, environmental surveillance, drug sensing and national security because they offer several advantages such as quick response, outstanding sensitivity, remarkable selectivity, high degree of accuracy and precision, ease of use and affordable price. This review highlights the performance aspects of recently developed biosensors for the detection of infectious bacteria and viruses in biological and environmental samples and emphasizes the significance of nanotechnology in signal amplification for enhanced biosensor performance and dependability.

Enhanced Data Security Using 5x5 Hill Cipher with Modular 53

This research presents an optimized approach to the Hill Cipher encryption and decryption algorithm using a 5x5 matrix and modular 53 arithmetic. The traditional Hill Cipher, a well-known symmetric key algorithm, typically utilizes smaller matrices and modular arithmetic, which may not provide sufficient security for contemporary applications. By expanding the key matrix to a 5x5 structure and adopting a larger modulus of 53, the complexity and security of the cipher are significantly enhanced. The study details the methodology for constructing and implementing the 5x5 key matrix, as well as the processes for encryption and decryption under the modular 53 system. The computational efficiency and security improvements achieved through this optimization are analyzed. Comparative assessments with the conventional Hill Cipher demonstrate that the enhanced approach offers superior resistance against cryptographic attacks while maintaining manageable computational requirements. The results of this research indicate that the proposed optimized Hill Cipher can serve as a robust encryption method suitable for securing sensitive data in various modern applications. This study contributes to the field of cryptography by providing a more secure and efficient variant of the classical Hill Cipher algorithm