Security Checks Research Articles

Capsule Attention Based Detection of Contraband in X-Ray Images

Aiming at the problem of low detection accuracy caused by the different postures, different sizes, complex backgrounds and overlapping occlusions of contraband in the security checking process, a Matrix capsule network based on attention mechanism (MCAM) is designed by introducing attention mechanism into the capsule network. Firstly, a multi-feature-extraction (MFE) module is designed to solve the difficulty of detecting contraband with different sizes and complex backgrounds; then the Conv2d with Attention for ConvCap (CACC) is constructed in the convolutional layer of the capsule, and the weight information is computed in the channel dimensions of the feature map to give the contraband regions higher coefficients to enhance the contraband detection ability when the poses are different and the occlusion is severe; finally, a new capsule detection layer is designed by utilizing the pose matrix of the capsule to give full play to the detection ability of the matrix capsule network. The mAPs of the proposed model on SIXray, SIXray10, and SIXray100 are 85.10%, 64.05%, and 53.67%, respectively, which are 42.79%, 19.73%, and 9.41% higher than those of the original network, higher than those of the current mainstream detection networks, and the number of parameters of the network and the amount of computation are also lower.

Improving millimeter-wave imaging quality using the vortex phase method

This paper investigates a new vortex wave imaging approach to improve the imaging quality of small metal targets of size less than 1.5 mm. Antennas with different spiral phase plates are designed to efficiently transmit vortex beams with orbital angular momentums (OAMs). By analyzing the OAM spectrum of the target, it was discovered that the predominant reflection contains a particular OAM mode that carries abundant azimuthal information. This can be explained by the OAM selectivity of the target and the guidance of the vortex transmitting beam. A simple reflection vortex imaging system was designed to capture the phase information. Measurement results show that the high image contrast reaches 14.9%, which is twice as high as that of the imaging without OAM. Both of simulations and experiments demonstrate that the vortex phase imaging approach proposed in this paper can effectively improve the imaging quality at 80 GHz. This approach is suitable for other millimeter wave imaging systems and is helpful to improve the resolution in anti-terrorism security checks.

On the inadequacy of open-source application logs for digital forensics

This study explores the challenges with utilizing application logs for incident response or forensic analysis. Application logs have the potential to significantly enhance security analysis as sometimes they provide information regarding user actions, error messages, and performance metrics of the application. Although these logs can offer vital information about user activities, errors, and application performance, their use for security needs better understanding. We looked at the current logging implementation of 60 open-source applications. We checked the logs to see if they could help with five key security tasks: making timelines, linking events, separating different actions, spotting misuse, and detecting attacks. By examining source code, extracting log statements, and evaluating them for security relevance, we found many logs lacked essential elements. Specifically, 29 applications omitted timestamps, crucial for identifying the timing of actions. Furthermore, logs frequently missed unique identifiers (UIDs) for event correlation, with 23 not noting UIDs for new activities. Inconsistent logging of user activities and an absence of logs detailing successful attacks indicate current application logs need significant enhancements to be effective for security checks. The findings of our research suggest that current application logs are inadequately equipped for in-depth security analysis. Enhancements are imperative for their optimal utility. This investigation underscores the inherent challenges in leveraging logs for security and emphasizes the pressing need for refining logging methodologies.

Experience of Individual Northern European Countries in Providing Protection of Justice (Criminological Aspect and Criminal Law Aspect)

The purpose of the article is to describes experience of Denmark, Finland, Norway and Sweden in providing criminological protection of justice. . This article is the qualitative research with a historical and comparative approach. Thus, as of 01 January, 2023, employees of the Court Security Service in Ukraine ensured public order and security in 590 appellate and local courts and bodies of the justice system, which is more than 80% of their total number , and as of 01 January 2024 security was ensured in 639 (87 %) objects of the justice system. To a large extent, this is related to introduction of restrictions in Ukraine provided for by martial law as a result of the armed aggression of the Russian Federation.The results of the study concluded that the it has been established that today in some countries of Northern Europe, in contrast to Ukraine, permanent security checks for all visitors are implemented only in those courts where such checks are needed for security reasons, and implementation of such a criminological function is entrusted to security inspectors represented by police officers, court employees, as well as security companies. Based on the results of the research, promising directions for improving activities of the Court Security Service of Ukraine were determined, and expediency of strategic planning practice in the sphere of ensuring protection of justice in Ukraine was also emphasized.

Securing the Cloud: Understanding Threats and Countermeasures

Abstract: Cloud computing has completely transformed how businesses and individuals store, manage, and retrieve data. Nevertheless, despite its many benefits, there are inherent security challenges. This research investigates the different risks encountered by cloud computing, which consist of data breaches, insider threats, and denial of service attacks. It also covers successful prevention methods like encryption, multi-factor authentication, routine security checks, and staff education. By comprehending these dangers and putting in place strong security measures, organizations can improve the security of their cloud environments and guarantee the confidentiality, integrity, and availability of their data. Cloud computing is now being extensively researched and utilized in both academic and industrial sectors. Both cloud service providers (CSPs) and consumers benefit from cloud computing. The security issues related to cloud computing have been extensively researched in various studies.

Donor-acceptor covalent organic polymers as real-time luminescent sensors for detection and removal of nitro-aromatics from an aqueous solution

Creating suitable monitoring probes that exhibit strong fluorescence properties for sensitive detection and effective removal of nitro-aromatics remains a challenge. Herein, triazine-based N-rich donor–acceptor covalent organic polymers (TBN-COPs) with unique electron-rich channels, tunable pore size distribution, and outstanding specific surface areas were rational designed and synthesized for the highly selective detection and effective removal of nitro-aromatics. The sensing properties for electron-deficient benzene derivatives were explored. Compared with other types of TBN-COP candidates, TBN-1 showed a better luminescence response to nitro-aromatics, especially for picric acid (PA). The 0.15 ppm detection limits and 2.89*105 as Ksv could be realized for PA detection by TBN-1. Besides, there were no additional fluorescence responses to the electron rich benzene derivatives, demonstrating the specific selectivity of nitro-aromatics by TBN-1. The energy and charge transfer mechanisms for the hyper nitro-aromatics sensing properties of TBN-1 were further elucidated according to the energy level simulation, and to both the spectroscopic and electrochemical analysis. The merits of TBN-1 are unreservedly manifested when further testing the adsorption performance of TBN-1 for nitro-aromatics removal. The mechanism for the excellent nitro-aromatics adsorption performance of TBN-1 is revealed by coupling model fitting and chemical computation, which indicates the π-π stacking interaction and high electron density of the material to facilitate its combination with nitro-aromatics. Therefore, TBN-1 is a promising candidate to selectively detect and effectively remove nitro-aromatics, and can hence be applied in security checking and environmental conservation.

Structural Engineering Enables Suppressed Ion Migration and Enhanced Hard X‐Ray Detection Performance in Novel Oxide Single Crystals

AbstractX‐ray detectors have obtained great attention in medical diagnostics, security checks, and space exploration. However, ion migration of X‐ray detectors results in dark current drift, low signal‐to‐noise ratio, and degradation of detection performance. Here, ion migration properties of Li2ZrTeO6 and ZrTe3O8 are comprehensively investigated by theoretical analysis and experimental results. ZrTe3O8 single crystal exhibits a high migration barrier of O2− ion (1.89 eV), a high ion activation energy (992.6 meV), and an ultralow dark current drift (3.16 × 10−9 nA cm−1 s−1 V−1) due to the absence of Li+ cations and the disconnecting of adjacent ZrO6 octahedra, which is better than Li2ZrTeO6 single crystal. These results show that the absence of low atomic number elements and the disconnecting of adjacent octahedra in crystal structure can inhibit ion migration essentially. Owing to a high resistivity, a high mobility lifetime product, suppressed ion migration, and the lone‐pair electrons enhancement effect of detection performance for Te4+ cations, ZrTe3O8 X‐ray detector shows a high sensitivity of 348 µC Gy−1 cm−2 and an ultralow detection limit of 15.4 nGyair s−1. This work proposes an effective strategy to inhibit ion migration and proves the enhancement effect of detection performance for the cations containing lone‐pair electrons.

Life fits home: Exploring people's experience with a COVID-19 tracing app in Turkey through a qualitative study

Mobile apps have been developed to manage COVID-19 in many countries. However, for these apps to be truly effective, they need to be widely adopted by society. To date, there has been less qualitative research on user experiences and perspectives on these apps. The goal of this study was to explore how users perceive and use different features of a COVID-19 tracing app provided in Turkey. Semi-structured interviews ( n = 15) were conducted over the phone, audio recorded, and then transcribed verbatim. The transcriptions were analyzed through thematic analysis. The analysis began by categorizing each transcript at the sentence level through open codes, which then grouped into broader themes. In total, 8 male and 7 female participants from 10 different cities took part in the interviews. On average, participants were 32 years ( SD = 6.8) old, and their app usage experience was 9.8 months ( SD = 3.1). Thematic analysis revealed five key themes: long-term adoption and engagement, perception and desired features, privacy concerns, reliability, and emotional and behavioral impact. The main reasons for users to download and use the app were to check the COVID-19 density map in their region and to access personal health codes for security checks. The COVID-19 density map caused many users to change where they usually travel and shop. Most participants felt comfortable sharing their personal data to collectively manage the pandemic. The majority found the app useful, stating that the app allows them to take precautions against the virus and therefore helps them feel good emotionally. Future contact tracing apps need to provide indicators to enable users to evaluate whether app use is making a difference in the pandemic.

Advancements in GitHub Automation and Workflow: A Comprehensive Exploration

The landscape of software development has been significantly transformed by the integration of automation and streamlined workflows, with GitHub emerging as a pivotal platform in this evolution. This research paper delves into the multifaceted realm of GitHub automation and workflow, investigating key aspects that contribute to enhanced efficiency, collaboration, and code quality in modern development practices. The paper begins by exploring the utilization of GitHub Actions for establishing robust Continuous Integration and Continuous Deployment (CI/CD) pipelines. Best practices for designing reliable workflows are discussed, along with insights into customizing workflows to suit diverse project requirements through the use of custom actions and templates. The automation of code reviews is scrutinized, with a focus on tools and practices that facilitate objective and efficient evaluation of code quality. The paper also investigates GitHub's role in security automation, covering strategies for vulnerability detection and integration of security checks into the development lifecycle. Collaborative workflows on GitHub, including branch management, pull requests, and code reviews, are explored as pivotal components fostering teamwork. The integration of GitHub into DevOps practices is analyzed, showcasing its synergy with popular DevOps tools and platforms. As a forward-looking endeavor, the research paper concludes by examining emerging trends and technologies in GitHub automation. By encapsulating a comprehensive overview of the current state and future trajectories of GitHub automation and workflow, this paper aims to contribute to the collective understanding of best practices, challenges, and opportunities in contemporary software development.

The Influence of Check Point Unit Aviation Security (AVSEC) Security Services on Passenger Satisfaction at Yogyakarta International Airport

Licensed aviation security officers, known as Aviation Security, are responsible for managing and providing security services at airports. One of the main roles of Aviation Security (AVSEC) is to ensure the quality of security services at the Yogyakarta International Airport Security Check Point (SCP) for passengers. In research conducted in the November-December 2022 period at YIA Airport, it was found that a number of Avsec officers were still not friendly enough in providing services to consumers. The aim of this research is to understand the impact of the quality of Security Check Point security services by AVSEC officers on the level of passenger satisfaction at Yogyakarta International Airport. Apart from that, this research also aims to assess the extent to which the quality of Security Check Point security services by AVSEC officers influences passenger satisfaction at Yogyakarta International Airport. This research adopted a quantitative approach and was carried out at Yogyakarta International Airport. Respondents in this study consisted of fifty aviation security officers. To collect data, researchers used observation, questionnaires and documentation methods. Data management is carried out through simple linear regression analysis

Assessing the risks and opportunities posed by AI-enhanced influence operations on social media

AbstractLarge language models (LLMs) like GPT-4 have the potential to dramatically change the landscape of influence operations. They can generate persuasive, tailored content at scale, making campaigns using falsified content, such as disinformation and fake accounts, easier to produce. Advances in self-hosted open-source models have meant that adversaries can evade content moderation and security checks built into large commercial models such as those commercialised by Anthropic, Google, and OpenAI. New multi-lingual models make it easier than ever for foreign adversaries to pose as local actors. This article examines the heightened threats posed by synthetic media, as well as the potential that these tools hold for creating effective countermeasures. It begins with assessing the challenges posed by a toxic combination of automated bots, human-controlled troll accounts, and more targeted social engineering operations. However, the second part of the article assesses the potential for these same tools to improve detection. Promising countermeasures include running internal generative models to bolster training data for internal classifiers, detecting statistical anomalies, identifying output from common prompts, and building specialised classifiers optimised for specific monitoring needs.

PUF-based dynamic secret-key strategy with hierarchical blockchain for UAV swarm authentication

The evolving UAV swarm flight environment exacerbates the difficulty of secure message delivery within the swarm. Physical Unclonable Functions (PUFs) have the potential to provide lightweight physical identities to UAV nodes. Nevertheless, the explicit storage of a private key within each UAV device invariably results in privacy leakage arising from physical hijacking. Therefore, to accomplish unified management and decentralized storage of identity informations for UAVs, as well as to simultaneously realize the dynamic updating of private key, we propose a PUF-based Dynamic Secret-key strategy with Hierarchical Blockchain (PDSHB) for UAV swarm authentication. Specifically, the master blockchain ensures that all UAVs’ registration information is tamper-proof, while the dynamic sub-blockchain synchronizes the UAV information within the swarm. Meanwhile, we have developed a more reliable and efficient PUF-based dynamic secret-key strategy that eliminates the need to store any secret information within UAVs, effectively mitigating the risk of key leakage resulting from physical attacks. Protocol security is demonstrated through formal security proofs, informal security analysis, and AVISPA security checking. Performance experiments based on Raspberry Pi 4B and FPGA-based F450 UAVs also show that the proposed protocol can deal with various security threats of dynamic swarms of UAVs and reduce computational and communication overheads during authentication.

Security-oriented Face Detection Technology Utilizing Deep Learning Techniques Along with the CASIA Datasets

Recently, face recognition technology has become increasingly important for safety purposes. Masks are now required in most countries and are increasingly used. Public health professionals advise people to conceal their facial features outdoors to reduce COVID-19 transmission by 65%. Detecting people without masks on their faces is crucial. This has become widely used as face recognition outperforms PINs, passwords, fingerprints, and other safety verification methods. Sunglasses, scarves, caps, and makeup have made facial identification harder in recent decades. Thus, such masks impact facial recognition performance. Face masks also make traditional technology for facial recognition ineffective for face authorization, security checks, school monitoring, and cellphone and laptop opening. Thus, we proposed Masked Facial Recognition (MFR) to recognize veiled and exposed-face people so they don't need to remove their masks to verify themselves. This deep computing model was trained with Inception Res Network V1. CASIA is responsible for preparing pictures and using LFW to validate models. Dlib creates masked datasets utilizing vision algorithms. About 96% accuracy was achieved using our three models that were trained. Thus, covered and uncovered recognition of faces and detection techniques in security and safety verification might easily be used. These systems can be used in various settings, such as airports, train stations, and other public places, to enhance security and prevent crime. Overall, deep learning within face recognition technology has significant potential for improving safety and security in various settings.

Development of Ward Visitor Management System in the COVID-19 Epidemic.

The purpose of this study was to design, develop, and deploy a visitor management system (VMS) to effectively manage visitors during COVID-19. The VMS was designed using the User-Centered Design (UCD) methodology. The iterative process of UCD includes 3 interviews and 5 usability tests and cognitive walkthrough cycles. This system comprised six parts: the WEB server provides visit scheduling service; the database server stores visit data and provides visit data services; the mobile application server provides security checks and scanning services; the electronic medical record (EMR) server provides ward data service; the Internet application gateway provides health code data service and exchanges health code data with the Sichuan Tianfu Health Code platform, and the service bus enables the centralized exchange and integration of visit data. The visit management system optimizes the workflow of ward visitors, improves staff productivity, and reduces the risk of infection transmission.

Pengaruh Pengetahuan Penumpang tentang Prohibited Items terhadap Kepatuhan pada Security Check Point di Bandar Udara Internasional Adi Soemarmo Boyolali Jawa Tengah

In accordance with Law No. 1 of 2009 the purpose of the implementation of flights is to realize the operation of flights that are safe, secure, fast, smooth, orderly, orderly and comfortable. Passenger concern can be in the form of knowledge and compliance with aviation security and safety procedures, especially during security checks at the Security Check Point. Therefore, this study aims to determine the effect of passenger knowledge on compliance. This study uses three independen variabels, namely Analytical Thinking Knowledge (X1), Conceptual Thinking (X2) and Expertise (X3) for the dependen variabel, namely Compliance (Y) using a quantitative method that uses a questionnaire to collect data that is distributed directly to 100 passengers at Adi Soemarmo airport using a non-probability sampling technique which was then assisted by using the SPSS data processing application Version 20. This research was conducted at Adi Soemarmo International Airport Boyolali, Central Java on December 5 2022 to January 5 2023. The results of this study indicate that the Analytical Thinking (X1) and Expertise (X3) variabels have an influence on the Compliance variabel (Y) while the Conceptual Thinking variabel (X2) has no effect on the Compliance variabel (Y) as evidenced by the t test. The f test shows that the effect of passenger knowledge on compliance is 40.2%. Keywords: Knowledge, Analytical Thinking, Conceptual Thinking, Expertise, Compliance

Multi‐Scale Feature Attention‐DEtection TRansformer: Multi‐Scale Feature Attention for security check object detection

AbstractX‐ray security checks aim to detect contraband in luggage; however, the detection accuracy is hindered by the overlapping and significant size differences of objects in X‐ray images. To address these challenges, the authors introduce a novel network model named Multi‐Scale Feature Attention (MSFA)‐DEtection TRansformer (DETR). Firstly, the pyramid feature extraction structure is embedded into the self‐attention module, referred to as the MSFA. Leveraging the MSFA module, MSFA‐DETR extracts multi‐scale feature information and amalgamates them into high‐level semantic features. Subsequently, these features are synergised through attention mechanisms to capture correlations between global information and multi‐scale features. MSFA significantly bolsters the model's robustness across different sizes, thereby enhancing detection accuracy. Simultaneously, A new initialisation method for object queries is proposed. The authors’ foreground sequence extraction (FSE) module extracts key feature sequences from feature maps, serving as prior knowledge for object queries. FSE expedites the convergence of the DETR model and elevates detection accuracy. Extensive experimentation validates that this proposed model surpasses state‐of‐the‐art methods on the CLCXray and PIDray datasets.

Attention‐based prohibited item detection in X‐ray images during security checking

AbstractThis paper focuses on the intelligent detection of prohibited items in X‐ray images during the security checking process. An intelligent semantic segmentation model of prohibited items in X‐ray images is proposed based on the attention‐based object localization method. Based on the pre‐trained CNN classification framework, the attention mechanism can map the high‐layer semantic information of objects into the input space, while generating energy saliency maps to locate the prohibited items. In order to make the obtained attention maps discriminative, the lateral and contrastive inhibition strategies are introduced and combined together which can highlight the responses of activated neurons. Under the guidance of attention responses, two traditional image segmentation algorithms are employed to achieve the semantic segmentation results for the prohibited items detection in X‐ray images. The proposed semantic segmentation model relies on weakly supervised learning mechanism, and only depends on the category labels of prohibited items, which greatly avoids the work cost of data semantic annotation. The experimental results based on the public SIXray baseline and the self‐built X‐ray image database demonstrate the proposed method can achieve about 65% IoU localization precise averagely. In addition, comparison experiments were carried out with the state‐of‐the‐arts and ablation experiments to verify the effectiveness of the proposed model.

Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm

Path planning technology is the basis of autonomous driving of unmanned vehicles. However, there are some problems in the traditional path planning technology. For example, high-quality global paths can't be generated quickly; Lacking of security verification ability; The performance of dynamic obstacle avoidance is poor. Therefore, this paper proposes a path planning method of unmanned vehicles based on adaptive particle swarm optimization algorithm (APSO). Firstly, a map simplification strategy (MSS) is proposed. The grid map is preprocessed by map simplification strategy to reduce the search space and time of path planning algorithm; Secondly, an APSO algorithm is proposed. The algorithm coordinates the search of particles through three adaptive factors and Levy flight strategy. Then, a security checking strategy is proposed. Security checking strategy can be used to verify the safety of global path; Finally, a dynamic obstacle avoidance strategy based on behavior is proposed. Vehicles can independently analyze the types of collision and adopt corresponding obstacle avoidance strategies. The simulation results show that MSS-APSO algorithm and APSO algorithm surpass original algorithms and comparison algorithms; MSS-APSO algorithm has strong applicability in real map environment; The obstacle avoidance strategy has great obstacle avoidance ability and real-time performance; The map simplification strategy can improve iterations of the algorithm and quality of the global path.

Research on Contraband Recognition Algorithm of Intelligent Millimeter Wave Security Equipment Based on R-CNN Algorithm under Civil Aviation Domain

Abstract In recent years, the way of air travel is gradually popularized, and the security check system in the airport is facing corresponding pressure. In order to guarantee the security problems in the aviation field, this study investigates the security check system in the civil aviation field. After investigating the security checking process in civil aviation, the Faster R-CNN has been improved based on both multilayer feature fusion and loss function to adapt to the target detection of millimeter wave images. Then, the detection and radiation characteristics of millimeter waves are introduced, and the target detection algorithm based on edge extraction is constructed. On this basis, artificial intelligence, big data, augmented reality glasses, and other technologies are incorporated to establish a security check system in civil aviation and analyze it experimentally. In terms of the overall recognition performance of mAP, this paper’s method improves by an average of 3.098-6.504% and 3.740-8.706%, respectively, with the highest FPS of 45 and 36, and its generalization ability is better compared to other methods. In addition, in the practice of contraband detection in unmixed and mixed backgrounds, the devices based on the security system of this paper have higher than 90% detection in all seven types of contraband, which is able to improve the accuracy of contraband detection, while still maintaining good performance in the case of overlapping targets. This study can assist security personnel in completing the task of detecting contraband and improving the efficiency of security checks.

SC-Lite: An Efficient Lightweight Model for Real-Time X-Ray Security Check

SC-Lite: An Efficient Lightweight Model for Real-Time X-Ray Security Check