Security Applications Research Articles

Multi-mode up-conversion emission of Tm@Yb@Er phosphors for color modulation and thermometry

Rare earth doped up-conversion materials play an important role in information security, colorful display and temperature sensing. In this work, the up-conversion luminescence of NaYF4:0.2Tm/20Yb@NaYF4:20Yb@NaYF4:2Er/20Yb phosphors, synthesized by typical co-precipitation method, are investigated under different excitation condition. The emission color of sample can be tuned from yellow to blue-green through changing pump laser from 1550 to 980 nm because the Tm3+ ions in the core cannot be excited by 1550 nm laser. More interesting, the value of red-to-green (R/G) and blue-to-green (B/G) can be finely manipulated by tuning the excitation pulse width and frequency. This phenomenon can be interpreted by different rising processes of the green, red and blue up-conversion emission. In addition, the core-shell structured sample displays a temperature-dependent color change, ranging from blue-green to purple. The green emissions, radiated from thermal coupled levels (2H11/24S3/2), are used in thermometry and the max relatively sensing sensitivity reaches 0.01 K−1 at 303 K. The findings indicate that the core-shell Tm@Yb@Er phosphors, with excitation-dependent and temperature-dependent up-conversion luminescence, have potential applications in information security and temperature sensing.

Aquila Optimized Fuzzy Deep Belief Network for Secure Data Transmission in WSN

A wireless sensor network (WSN) is made up of several independent sensor nodes that are able to interpret, analyze, and work with data. It is generally recognized that security and limited energy are the two challenging tasks with WSNs. To address these challenges, a novel Aquila-optimized fuzzy deep belief network (AO-FDBN) model has been proposed in this paper. The suggested AO-FDBN framework consists of three stages. Initially, the Aggregator has been selected by using the Aquila optimization algorithm. Secondly, the data from the aggregator are encrypted by using the blowfish algorithm. Finally, the optimal route has been selected by using the fuzzy-deep belief network (DBN). Packet delivery ratio (PDR), transport delay, energy usage, and network lifetime are evaluated between the suggested framework and current methods. Experimental results specify that the suggested AO-FDBN approach achieves higher performance of 22.617%, 14.22%, and 15.64% than TEEFCA, HESC, and SEPC methods. This system is more effective and secure for real-time applications.

Introducing Security Mechanisms in OpenFog-Compliant Smart Buildings

Designing smart building IoT applications is a complex task. It requires efficiently integrating a broad number of heterogeneous, low-resource devices that adopt lightweight strategies. IoT frameworks, especially if they are standard-based, may help designers to scaffold the applications. OpenFog, established as IEEE 1934 standard, promotes the use of free open source (FOS) technologies and has been identified for use in smart buildings. However, smart building systems may present vulnerabilities, which can put their integrity at risk. Adopting state-of-the-art security mechanisms in this domain is critical but not trivial. It complicates the design and operation of the applications, increasing the cost of the deployed systems. In addition, difficulties may arise in finding qualified cybersecurity personnel. OpenFog identifies the security requirements of the applications, although it does not describe clearly how to implement them. This article presents a scalable architecture, based on the OpenFog reference architecture, to provide security by design in buildings of different sizes. It adopts FOS technologies over low-cost IoT devices. Moreover, it presents guidelines to help developers create secure applications, even if they are not security experts. It also proposes a selection of technologies in different layers to achieve the security dimensions defined in the X.805 ITU-T recommendation. A proof-of-concept Indoor Environment Quality (IEQ) system, based on low-cost smart nodes, was deployed in the Faculty of Engineering of Vitoria-Gasteiz to illustrate the implementation of the presented approach. The operation of the IEQ system was analyzed using software tools frequently used to find vulnerabilities in IoT applications. The use of state-of-the-art security mechanisms such as encryption, certificates, protocol selection and network partitioning/configuration in the OpenFog-based architecture improves smart building security.

Quantum calculi and formalisms for system and network security: A bibliographic insights and synoptic review

AbstractQuantum calculi and formalisms are useful tools for ensuring security and computational capabilities in blockchain and cryptography. They aid in designing and analysing new cryptographic protocols for blockchain, determining the behaviour of quantum operations in blockchain‐based smart contracts, assessing the feasibility and security of quantum algorithms in blockchain applications, and building a quantum‐safe blockchain system. A comprehensive review of the applications of quantum calculi and formalisms in computer security and network security, along with a bibliographic analysis is presented. It is unique in that it combines bibliometric analyses with a technical review of the domain of quantum calculi and formalism. Bibliometric and biographic analysis in the field helps identify research trends, assess the influence of research, determine collaboration patterns, evaluate journals, and examine publication behaviours, among other things. It performs bibliographic and bibliometric analysis using a dataset collected from Scopus and Web of Science through different queries. The obtained results help identify important institutions, authors, organisations, collaboration networks, keywords, and more. The provided open challenges and future vision pave the way for further research in the direction of quantum calculi and formalism applications in computer security and network security.

A Comparative Study on GPS Navigation Systems: Attacks, Applications and Challenges for Security and Reliability

A Comparative Study on GPS Navigation Systems: Attacks, Applications and Challenges for Security and Reliability

Analisis Kerentanan Keamanan Website Menggunakan Metode PTES (Penetration Testing Execution And Standart)

With rapid advances in Information Technology (IT), the need for an IT-based learning framework and mechanism has become an unavoidable necessity. This requires solid security in a system, using the Penetration Testing Execution and Standard (PTES) method. This research method is quantitative and descriptive, used to illuminate website security and provide input for improvement. The research involved Tenable Nessus Professional tools that emit vulnerabilities, such as Browseable Web Directory and Potentially Clickjacking Web. The PTES stages covered include Planning, Data Collection, Scanning, Access Rights, Security, and Web Application Firewall (WAF) configuration. The hope is that this method will provide an in-depth understanding of potential threats and vulnerabilities in government information systems, as well as present solutions. The research results are expected to provide education and insight into website security. This allows related parties to take preventive and corrective steps, improve security, and protect the integrity and confidentiality of data entities. As a result, information systems are expected to be more secure and resistant to potential cyber threats.

Machine Learning-Driven Detection of Cross-Site Scripting Attacks

The ever-growing web application landscape, fueled by technological advancements, introduces new vulnerabilities to cyberattacks. Cross-site scripting (XSS) attacks pose a significant threat, exploiting the difficulty of distinguishing between benign and malicious scripts within web applications. Traditional detection methods struggle with high false-positive (FP) and false-negative (FN) rates. This research proposes a novel machine learning (ML)-based approach for robust XSS attack detection. We evaluate various models including Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVMs), Decision Trees (DTs), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and ensemble learning. The models are trained on a real-world dataset categorized into benign and malicious traffic, incorporating feature selection methods like Information Gain (IG) and Analysis of Variance (ANOVA) for optimal performance. Our findings reveal exceptional accuracy, with the RF model achieving 99.78% and ensemble models exceeding 99.64%. These results surpass existing methods, demonstrating the effectiveness of the proposed approach in securing web applications while minimizing FPs and FNs. This research offers a significant contribution to the field of web application security by providing a highly accurate and robust ML-based solution for XSS attack detection.

Genetic fuzzy rules and hybrid QDCNN-F-DSAE for detecting attacker behavior with tuning of firewall

ABSTRACT The application of computer technology is increasing quickly as the field of information technology develops over time. Because of the growth of computer networks, there is a rise in network attacks. Moreover, the majority of transactions are conducted via the Internet, and then the web application security is becoming increasingly important. A network firewall shields the web servers from unauthorised and dangerous traffic. In this work, the hybrid Quantum Dilated Convolutional Neural Network fused Deep-Stacked Auto Encoder (QDCNN-F-DSAE) model with Genetic Fuzzy System (GFS) is developed for network protection. With the utilisation of genetic fuzzy system (GFS) and firewall tuning, the QDCNN-F-DSAE identified the attack, ransomware activity, and prompt decision-making. The data offered by the simulated environment is used to make decisions, and the decisions should be made quickly within a few seconds using the proposed QDCNN-F-DSAE with GFS. Furthermore, it identifies the attacks and alerts the users to store the data from the supplied source. The malicious function and protected private Local Area Network (LAN) are discovered and removed by the firewall after the formation of genetic fuzzy rules. Moreover, this model is estimated using the accuracy, sensitivity, and specificity measures that offered the finest values of 0.915, 0.908, and 0.920.

Iterative active learning strategies for subgraph matching

The subgraph matching problem arises in a variety of domains including pattern recognition for segmented images, meshes of 3D objects, biochemical reactions, and security applications. Large and complex solution spaces are common for this graph-based problem, especially when the world graph contains many more nodes and edges in comparison to the template graph. Researchers have focused on the task of finding one match or many matches, however a real use-case scenario can necessitate identifying specific matches from a combinatorially complex solution space. Our work directly addresses this challenge. We propose to introduce additional queries to the subgraph that iteratively reduce the size of the solution space, and consider the optimal strategy for doing so. We formalize this problem and demonstrate that it is NP-complete. We compare different quantitative criteria for choosing nodes to query. We introduce a new method based on a spanning tree that outperforms other graph-based criteria for the multichannel datasets. Finally, we present numerical experiments for single channel and multichannel subgraph matching problems created from both synthetic and real world datasets.

Design and Analysis of a Novel Fractional-Order System with Hidden Dynamics, Hyperchaotic Behavior and Multi-Scroll Attractors

The design of chaotic systems with complex dynamic behaviors has always been a key aspect of chaos theory in engineering applications. This study introduces a novel fractional-order system characterized by hidden dynamics, hyperchaotic behavior, and multi-scroll attractors. By employing fractional calculus, the system’s order is extended beyond integer values, providing a richer dynamic behavior. The system’s hidden dynamics are revealed through detailed numerical simulations and theoretical analysis, demonstrating complex attractors and bifurcations. The hyperchaotic nature of the system is verified through Lyapunov exponents and phase portraits, showing multiple positive exponents that indicate a higher degree of unpredictability and complexity. Additionally, the system’s multi-scroll attractors are analyzed, showcasing their potential for secure communication and encryption applications. The fractional-order approach enhances the system’s flexibility and adaptability, making it suitable for a wide range of practical uses, including secure data transmission, image encryption, and complex signal processing. Finally, based on the proposed fractional-order system, we designed a simple and efficient medical image encryption scheme and analyzed its security performance. Experimental results validate the theoretical findings, confirming the system’s robustness and effectiveness in generating complex chaotic behaviors.

Integrating fully homomorphic encryption to enhance the security of blockchain applications

Blockchain has been widely used for secure transactions among untrusted parties, but the current design of blockchain does not provide sufficient privacy and security for the data on the chain, limiting its application in sensitive information scenarios. To address this problem, we propose integrating fully homomorphic encryption (FHE) to enhance the security of blockchain applications, which can extend the application scope of blockchain and improve the privacy and security of blockchain by the features of FHE. Our scheme classifies FHE into those supporting polynomial and non-polynomial operations and introduces the concept of ciphertext computation conversion into Ethereum, enabling conversion between different ciphertext computation types. Moreover, we analyse the security and correctness to explain the feasibility and availability of the scheme. We carry out comparative experiments using different open-source libraries for fully homomorphic encryption and the time performance evaluation of the ciphertext computation conversion under different thread counts. The experiment results demonstrate the efficiency and usability of our scheme.

Terahertz optical pattern recognition with rotation and scaling enhanced by a 3D-printed diffractive deep neural network

Optical pattern recognition (OPR) has the potential to be a valuable tool in the field of terahertz (THz) imaging, with the advantage of being capable of image recognition with single-point detection, which reduces the overall system costs. However, this application is limited in the traditional OPR that rotation and scaling of the input image will bring about an offset of the recognition spot. Here we demonstrate a full-diffractive method to maintain the recognition spot at a fixed position, even when the input image is rotated or scaled, by using an all-optical diffractive deep neural network. The network is composed of two layers of diffractive optical elements (DOEs) without a 4f-system, and 3D-printed all-in-one. Experimental results show that our device can achieve a stable recognition of the input image regardless of its rotation (from 0° to 360°) or scaling (with a ratio from 1 to 1/1.9). This work is expected to provide enhanced functionality for compact THz systems in imaging and security applications.

Blockchain‐driven IoT solutions in agriculture

AbstractThe Internet of Things (IoT) constitutes a network of physical objects, including devices and systems, capable of intercommunication both among themselves and with external systems via the Internet. Its applications span across various sectors such as manufacturing, medicine, commerce, security, forestry and agriculture. The application of IoT in agriculture involves gathering data from diverse sensors, offering timely and precise solutions. This technology facilitates the reduction of manual labour involved in tasks like animal monitoring, greenhouse management and field surveillance. Integrated sensors within the IoT framework enable the collection of data pertaining to soil, air, water, animal tracking, plant growth and other relevant metrics. Nevertheless, the swift evolution of IoT presents a susceptibility to unauthorized access and compromises in data security. To address this concern, the utilization of blockchain technology has been recommended. This paper proposes a novel IoT architecture integrated with blockchain technology to enhance data security in agricultural applications. A detailed schematic has been devised and implemented in software to illustrate this approach.

Retraction Note: Adaptive computing-based biometric security for intelligent medical applications

Retraction Note: Adaptive computing-based biometric security for intelligent medical applications

Timing Side-Channel Mitigation via Automated Program Repair

Side-channel vulnerability detection has gained prominence recently due to Spectre and Meltdown attacks. Techniques for side-channel detection range from fuzz testing to program analysis and program composition. Existing side-channel mitigation techniques repair the vulnerability at the IR/binary level or use runtime monitoring solutions. In both cases, the source code itself is not modified, can evolve while keeping the vulnerability, and the developer would get no feedback on how to develop secure applications in the first place. Thus, these solutions do not help the developer understand the side-channel risks in her code and do not provide guidance to avoid code patterns with side-channel risks. In this paper, we present Pendulum , the first approach for automatically locating and repairing side-channel vulnerabilities in the source code, specifically for timing side channels. Our approach uses a quantitative estimation of found vulnerabilities to guide the fix localization, which goes hand-in-hand with a pattern-guided repair. Our evaluation shows that Pendulum can repair a large number of side-channel vulnerabilities in real-world applications. Overall, our approach integrates vulnerability detection, quantization, localization, and repair into one unified process. This also enhances the possibility of our side-channel mitigation approach being adopted into programming environments.

Evaluating CNN Models for Gait Recognition: A Study on the CASIA-B Dataset

Gait recognition, a form of biometric identification, has garnered considerable interest for its ability to identify individuals from a distance. Yet, its effectiveness depends significantly on the precision of the underlying categorization models. This study thoroughly evaluates many Convolutional Neural Network (CNN) models to determine their effectiveness in properly identifying and categorizing gait patterns. We train and test several CNN architectures using advanced deep learning techniques on the widely known CASIA-B dataset, which is a benchmark dataset for gait recognition research. The analyses involve models like as VGG16, VGG19, NASNetLarge, NASNetMobile, EfficientNetB0, and Xception, each well-known for their effectiveness in picture categorization tasks. We evaluate the performance of these models by conducting thorough experiments and detailed analysis, focusing on accuracy, validation loss, and other pertinent metrics. The Xception model demonstrated the highest accuracy of 97.17% of the models assessed. This model frequently surpasses similar models, demonstrating its strength and effectiveness in precisely recognizing gait patterns. On the other hand, the accuracy of other models varied from 9.78% to 93.85%, demonstrating the variable levels of efficacy among various designs. The findings have significant implications for the creation and implementation of gait recognition systems. The high precision demonstrated by the Xception model highlights the promise of CNN-based methods in progressing gait identification technology. Our work highlights the significance of choosing suitable model architectures for achieving the best performance in gait detection tasks. In the future, research may focus on using larger datasets and exploring other CNN architectures to improve the accuracy and reliability of gait detection systems. Our goal is to enhance gait recognition technology to improve biometric identification for security, surveillance, and healthcare applications. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 17-26

Comparative analysis of FinTech adoption among Islamic and conventional banking users with moderating effect of education level: A UTAUT2 perspective

This study aims to examine the consumer behavior with regards to the FinTech services in the context of Pakistan and identify the consumers’ driver and detractors to adopt FinTech firm. Tapping into an area yet un-explored, it seeks to propose a conceptual framework based on the UTAUT2 model and extend the FinTech adoption model by determining the effect of performance expectancy and effort expectancy based on educational segmentation. In a similar vein, it aims at examining the gaps in the adoption of FinTech services in both Islamic banking as well as the conventional competing banks of Pakistan. This paper employed a quantitative research design; data were collected from 382 subjects in Karachi, Pakistan Convenience sampling and random number generation were employed to minimize biased. This study sought participants’ responses to the survey questionnaire of 22 items, distinguishing the seven constructs of the UTAUT2 and a new one, education level, on a five-point Likert scale. Concerning the sample size and the research methods that were used, the rationale was given in line with the available guidelines and the results of empirical research in the field of behavioral sciences. As the research hypothesis postulated, the study revealed performance expectancy, effort expectancy, social influence, and facilitating conditions as independent factors influencing behavioral intention to use FinTech, although the moderating effect of education level only affected the relationship between the performance expectancy and behavioral intention to use FinTech influenced by it. They also found differences between Islamic and conventional banking users, and the enhancing/attenuating impacts of the constructs on the behavioral intention alertly. This research underscores the essential factors that influence the behavioral intention of the users towards FinTech services – performance expectancy, effort expectancy, social influence, and facilitating conditions – with implications for promoting positive service experiences. Financial technology firms must heed these insights to create simple and secure applications to increase the adoption of FinTech innovations that are sensitive to the anglophone African cultures. Besides, the focus on deploying rather diverse local initiatives for comprehending and constantly improving the FinTech services based on people’s feedback can increase consumers’ trust and expectations.

Two-stage re-parameterization and sample disentanglement network for surveillance vehicle detection

Abstract Detecting vehicles from a surveillance viewpoint is essential, as it has wide applications in community security and traffic control. However, existing methods completely overlook the high memory access costs (MAC) and low degree of parallelism inherent in multi-branch topologies, resulting in significant latency during inference. Additionally, existing methods share the same positive/negative sample set between the classification and localization branches, leading to sample misalignment, and rely solely on intersection-over-union (IoU) for sample assignment, thereby causing a decrease in detection performance. To tackle these issues, this paper introduces a two-stage re-parameterization and sample disentanglement network (TRSD-Net). It is based on two-stage depthwise to pointwise re-parameterization (RepTDP) and task-aligned sample disentanglement (TSD). RepTDP employs structural re-parameterization to decouple the multi-branch topology during training and the plain architecture during inference, thus achieving low latency. By employing different sample assignment strategies, TSD can adaptively select the most suitable positive/negative sample sets for classification and localization tasks, thereby enhancing detection performance. Additionally, TSD considers three important factors influencing sample assignment. TRSD-Net is evaluated on both the UA-DETRAC and COCO datasets. On the UA-DETRAC dataset, compared to state-of-the-art (SOTA) methods, TRSD-Net improves the detection accuracy from 58.8% to 59.7%. Additionally, it reduces the parameter count by 87%, the computational complexity by 85%, and the latency by 39%, while increasing the detection speed by 65%. Similar performance improvement trends are observed on the COCO dataset.

Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We?

Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We?

Review and Analysis of RGBT Single Object Tracking Methods: A Fusion Perspective

Visual tracking is a fundamental task in computer vision with significant practical applications in various domains, including surveillance, security, robotics, and human-computer interaction. However, it may face limitations in visible light data, such as low-light environments, occlusion, and camouflage, which can significantly reduce its accuracy. To cope with these challenges, researchers have explored the potential of combining the visible and infrared modalities to improve tracking performance. By leveraging the complementary strengths of visible and infrared data, RGB-infrared fusion tracking has emerged as a promising approach to address these limitations and improve tracking accuracy in challenging scenarios. In this article, we present a review on RGB-infrared fusion tracking. Specifically, we categorize existing RGBT tracking methods into four categories based on their underlying architectures, feature representations, and fusion strategies, namely feature decoupling based method, feature selecting based method, collaborative graph tracking method, and traditional fusion method. Furthermore, we provide a critical analysis of their strengths, limitations, representative methods, and future research directions. To further demonstrate the advantages and disadvantages of these methods, we present a review of publicly available RGBT tracking datasets and analyze the main results on public datasets. Moreover, we discuss some limitations in RGBT tracking at present and provide some opportunities and future directions for RGBT visual tracking, such as dataset diversity, unsupervised and weakly supervised applications. In conclusion, our survey aims to serve as a useful resource for researchers and practitioners interested in the emerging field of RGBT tracking, and to promote further progress and innovation in this area.