Security Tasks Research Articles

Design and Simulation of a Muon Detector Using Wavelength-Shifting Fiber Readouts for Border Security

Cosmic ray muon tomography is a promising method for the non-invasive inspection of shipping containers and trucks. It leverages the highly penetrating cosmic muons and their interactions with various materials to generate three-dimensional images of large and dense objects, such as inter-modal shipping containers, which are typically opaque to conventional X-ray radiography techniques. One of the key tasks of customs and border security is verifying shipping container declarations to prevent illegal trafficking, and muon tomography offers a viable solution for this purpose. Common imaging methods using muons rely on data analysis of either muon scattering or absorption–transmission. We design a compact muon tomography system with dimensions of 3 × 3 × 3 m3, consisting of 2D position-sensitive detectors. These detectors include plastic scintillators, wavelength-shifting (WLS) fibers, and SiPMs. Through light transport modeling with GEANT4, we demonstrate that the proposed detector design—featuring 1 m × 1 m scintillator plates with 2 mm2 square-shaped WLS fibers—can achieve a spatial resolution of approximately 0.7–1.0 mm. Through Monte Carlo simulations, we demonstrate that combining muon scattering and absorption data enables the rapid and accurate identification of cargo materials. In a smuggling scenario where tobacco is falsely declared as paper towel rolls, this combined analysis distinguishes the two with 3 σ confidence at a spatial resolution of 1 mm (FWHM) for the muon detector, achieving results within a scanning time of 40 s for a 20-foot shipping container.

DEVELOPMENT OF TECHNOLOGY OF COTTAGE CHEESE AND WHEY DRINK WITH THE USE OF SEA BUCKTHORN

One of the most important tasks of food security is to provide the population with dairy products, particularly, with a high protein content. Among such products, cottage cheese is distinguished. At the same time, the industrial processing of milk, which is based on the principles of waste-free production of dairy products with increased quality and safety indicators, is relevant.The work aims to develop the technology of cottage cheese and whey drink using sea buckthorn.The object of the study was samples of cottage cheese and whey drink produced using different amounts (10 %, 15 % and 20 %) of sea buckthorn mixture (sea buckthorn + sugar). The cottage cheese produced using a 15 % sea buckthorn mixture was characterized by high organoleptic quality indicators, particularly taste properties, compared to the samples obtained using a 10 % and 20 % sea buckthorn mixture. it was established that cottage cheese using 15 % sea buckthorn mixture is characterized by an increased mass fraction of protein by 1.2 % compared to the control. An increase in the amount (from 10 % to 20 %) of the sea buckthorn mixture leads to an increase in vitamin C content in the produced cottage cheese – by 30–33%. The dependence of the mass fraction of protein in the separated whey on the amount of sea buckthorn mixture during the production of cottage cheese was established. Thus, the protein content of whey is 1.2% after using a 10 % sea buckthorn mixture, 0.9 %–15 % sea buckthorn mixture, and 0.45 % when using a 20 % sea buckthorn mixture. Whey drink produced from separated whey during the production of cottage cheese with the use of 15 % sea buckthorn mixture is characterized by a high content of vitamin C – 121 mg/100 g and a more yellowish colour, a pleasant sweet taste compared to the control. A technological block scheme for the production of cottage cheese and whey drink using a 15% sea buckthorn mixture has been developed. The proposed technology will make it possible to expand the range of new types of cottage cheese and whey drinks.

The security function of the community protection unit in the Teling Atas Urban Village, Manado City

This research aims to find out, analyze and describe 1) The Security Function of the Community Protection Unit (Satlinmas) in Teling Atas urban village, Manado City has not run optimally, and 2) Determinant factors of the Security Function of the Community Protection Unit in Teling Atas urban village, Manado City. This research used a qualitative research design with a contextual research that makes humans as instruments and is adapted to a natural situation in relation to data collection which is generally qualitative in nature. The results showed that 1) in terms of Member Capacity, so far Satlinmas members in Teling Atas urban village, Manado City have never participated in education and training; 2) in terms of Facilities and Infrastructure, in carrying out security tasks, Satlinmas has not been supported by adequate facilities and infrastructure; 3) in terms of Coordination with the provincial government, district / city, sub-district to the village / urban village level is quite good but not optimal. While coordination with local residents is quite well established; 4) in terms of Budget, there is no budget organized in the Manado City Regional Budget (APBD) through Teling Atas Urban Village for operational costs of carrying out security patrol tasks, procurement of uniforms, supporting equipment for Satlinmas members as well as monthly allowances/honors; 5) in terms of Community Participation, Community participation is very limited.

PENETRATION TESTING APPROACHES EMPLOYING THE OPENVAS VULNERABILITY MANAGEMENT UTILITY

Currently, issues of security of information systems of critical information infrastructure facilities are becoming relevant. At the same time, the current tasks of an information security (IS) audit of critical information infrastructure objects, as a rule, come down to checking them for compliance with IS requirements. However, with this approach to auditing, the resistance of these objects to real attacks by intruders often remains unclear. To test this resistance, objects are subjected to a testing procedure, namely penetration testing. The goal is a comparative analysis of existing foreign and domestic penetration testing methods and standards. The elements of novelty of the work are the identified features, advantages, disadvantages and scope of applicability of existing standards and methods of penetration testing. This article will review the OpenVAS vulnerability scanner. Readers will become familiar with the basic and advanced functions of the program; its settings depend on the functions and capabilities.

Distributed Optimization of Multi-Role UAV Functionality Switching and Trajectory for Security Task Offloading in UAV-Assisted MEC

Distributed Optimization of Multi-Role UAV Functionality Switching and Trajectory for Security Task Offloading in UAV-Assisted MEC

Security tasks of the Military Police in the international components of the Military Police on United Nations and NATO missions from 1995 to 2010.

Security tasks of the Military Police in the international components of the Military Police on United Nations and NATO missions from 1995 to 2010.

The energy sector of the economy of the Arctic zone of the Russian Federation from the perspective of national security: legal aspects

Russia’s Arctic zone plays a key role in the development of energy resources on the world stage. At the same time, the development of the energy sector of the economy is associated with solving the tasks of the national security of the Russian Federation. The effective solution of these tasks is possible due to the more active use of the tools offered by the “green” economy and alternative energy, which requires a significant modification of legal regulation. However, at the moment, a complete replacement of the raw hydrocarbon sector in the world, according to researchers, seems impossible. Today, the main engine of the development of the Russian Arctic is precisely the raw energy sector, and the global economy is based mainly on energy, which is produced from mineral resources. The development of energy in the Arctic zone depends on the level of industrial safety. Industrial safety can be understood as the state of protection of personal interests (vital and property), as well as public interests (society and the state) due to the system of industrial safety management provided for by legislation. The industrial safety management system and the Institute of Civil Liability Insurance for owners of hazardous facilities is a comprehensive mechanism for preventing accidents and neutralizing their negative consequences at hazardous facilities, which is especially important for the Arctic zone of the Russian Federation. The main problems of the implementation of this mechanism and the ways to solve them are outlined.

Long duration forecasting and its performance capability for seasonal variation modelling of residual chlorine concentrations: A comparative evaluation of two small-scale water distribution systems in Japan

Chlorine dosing control is a critical task for health security, complying with drinking water quality standards while achieving customer satisfaction. In Japan, this became more difficult due to decreasing number of technical personnel as a result of declining population and huge retirement of veterans. As experienced-based dosing control still exists and is considered inefficient due to risks of inaccurate dosing and need of experienced manpower, streamlining of operations is needed. This study aims to address these concerns through the development and evaluation of a deep learning model for residual chlorine concentrations capable of forecasting long durations. In this paper, the model is investigated in seasonal timeframe analyzing trends and variations. Two water distributions systems of varying network—simple and complex, are also compared to further analyze model performance and versatility. The model utilizes the past 24 h of flow rate, chlorine level at treatment plant, water temperature, and residual chlorine at private homes as input to predict the 12 h ahead of residual chlorine at private homes evaluated at hourly training lengths of 0.5, 1, 1.5, 2 years. Results revealed that the model achieved high accuracy in predicting hourly residual chlorine with general increasing model error from winter, spring, autumn, and summer due to the progressing instabilities in chlorine concentrations from low to high temperatures. Moreover, smaller system tends to be more unstable incurring lower model performance relative to larger system. In terms of optimal training length, ≥1Y training models are found to have lesser chance of data drift occurrence prospectively reducing model retraining frequency in the future.

Transformer Based Ship Detector: An Improvement on Feature Map and Tiny Training Set

The exponential increment of commodity exchange has raised the need for maritime border security in recent years. One of the most critical tasks for naval border security is ship detection inside and outside the territorial sea. Conventionally, the task requires a substantial human workload. Fortunately, with the rapid growth of the digital camera and deep-learning technique, computer programs can handle object detection tasks well enough to replace human labor. Therefore, this paper studies how to apply recent state-of-the-art deep-learning networks to the ship detection task. We found that with a suitable number of object queries, the Deformable-DETR method will improve the performance compared to the state-of-the-art ship detector. Moreover, comprehensive experiments on different scale datasets prove that the technique can significantly improve the results when the training sample is limited. Last but not least, feature maps given by the method will focus well on key objects in the image.

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.

Pitfalls in Machine Learning for Computer Security

With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas. This development has influenced computer security, spawning a series of work on learning-based security systems, such as for malware detection, vulnerability discovery, and binary code analysis. Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance and render learning-based systems potentially unsuitable for security tasks and practical deployment. In this paper, we look at this problem with critical eyes. First, we identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We conduct a study of 30 papers from top-tier security conferences within the past 10 years, confirming that these pitfalls are widespread in the current security literature. In an empirical analysis, we further demonstrate how individual pitfalls can lead to unrealistic performance and interpretations, obstructing the understanding of the security problem at hand. As a remedy, we propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible. Furthermore, we identify open problems when applying machine learning in security and provide directions for further research.

Rapid cargo verification with cosmic ray muon scattering and absorption tomography

Cosmic ray muon tomography is considered a promising method for the non-invasive inspection of shipping containers and trucks. It utilizes highly penetrating cosmic-ray muons and their interactions with various materials to generate three-dimensional images of large and dense materials, like inter-modal shipping containers, typically not transparent with conventional X-ray radiography techniques. The commonly used methods for imaging with muons are based on muon scattering or absorption-transmission data analysis. Due to large thickness of cargo material in shipping container substantial scattering and absorption occur when muons are passing through cargo. One of the key tasks of customs and border security is to verify shipping container declarations to prevent illegal trafficking, and muon tomography could be a viable choice for this task. In this paper, we demonstrate through Monte Carlo simulations using the GEANT4 toolkit that a combined analysis of muon scattering and absorption data can improve the identification of cargo materials compared to using scattering or absorption data alone. The statistical differences in scattering and absorption data for several cargo materials are quantified. For a particular smuggling scenario where tobacco declared as paper towel rolls, it is demonstrated that the combined analysis can accurately distinguish between tobacco and paper towel rolls with 5.5σ accuracy for detector spatial resolution (FWHM) of 0.235 mm, 4.5σ for 1.175 mm resolution (FWHM), and 3.9σ accuracy for 2.35 mm spatial resolution (FWHM), in a short scanning time of 10 seconds. This rapid detection capability has significant implications for anti-smuggling efforts and cargo inspection.

Internet of things as a tool for ensuring material security of Military Units and Institutions

Abstract: Material security of military units and institutions is one of the determinants of the ability to carry out combat tasks. It will be possible to provide it, among other Things, by obtaining information on the existing material needs and the possibility of meeting them. This information can be obtained through the use of technologically advanced technologies such as the Internet of Things. The aim of the article is to publish the results of research indicating whether and to what extent the Internet of Things will determine the logistical security of military units and institutions. In order to achieve this goal, a general research problem has been identified in the form of a question: "How will the Internet of Things affect the acquisition of information necessary to make decisions regarding the satisfaction of the material needs of military units and institutions"? The solution to the general research problem was achieved by refining it with four specific research problems. Theoretical methods (analysis, synthesis) supplemented by an empirical method – participant observation – were used to solve them. An important item in the research process were the results of surveys conducted with persons performing tasks related to ensuring material security in military units and institutions. The results obtained in the course of the research clearly indicate the importance of information for decision-making related to meeting the material needs of military units and institutions. On the other hand, the ability to provide material resources will depend not only on the resources at hand, but mainly on the time of obtaining information on the occurrence of needs in a specific place and time. This requirement can be met through the use of modern technologies such as the Internet of Things. In addition to the undoubted benefits, the results of the study also pointed to the possibility of threats during the use of the Internet of Things. Therefore, it is necessary to balance the benefits and threats and make a decision on the possibility of implementing the Internet of Things for solutions supporting the implementation of material security tasks for military units and institutions.

DewROS2: A platform for informed Dew Robotics in ROS

With the shift from Cloud to Fog and Dew Robotics a lot of emphasis of the research community has been devoted to task offloading. Effective and efficient resource monitoring is however necessary for such offloading and it is also fundamental for other important safety and security tasks. Despite this, robot monitoring has received little attention in general and also for Robot Operating System (ROS) the most employed framework in robotics. In this paper DewROS2 is presented, a platform for Dew Robotics that comprises entities to monitor the system status and to share it with interested applications. The design and implementation of the platform is presented together with the monitoring entities created. DewROS2 has been deployed on different real devices, including an unmanned aerial vehicle and an industrial router, to move from theory to practice and to analyze the impact of monitoring on robot resources. DewROS2 has also been tested in a search and rescue use case where robots are used to collect and transmit videos to spot signs of humans in trouble. Results in controlled and uncontrolled conditions show that the monitoring nodes do not have a significant impact on the performance while providing important and measurable benefits to the applications. Accurately monitoring of robot resources, for example, allows the search and rescue application to almost double the utilization of the network, therefore collecting video at a much higher resolution.

Crude Oil Theft, Illegal Bunkering and Pipeline Vandalism in Nigeria’s Niger Delta: Effects and National Responses to Curb the Menace.

Against the backdrop of the fact that Nigeria lost large scale of petroleum resources valued as huge financial losses to the nation, individuals, and other institutions, this paper examined crude oil theft, illegal bunkering, and pipeline vandalism in the Niger Delta region of Nigeria; effects and national responses. The findings show that crude oil theft, illegal bunkering, and pipeline vandalism have caused adverse effects such as socio-economic losses to Nigeria, environmental pollution and degradation, a threat to national and international security, and caused huge losses to national and international oil companies and individuals. To ensure the smooth operation of the country’s oil industry, various administrations have carried out various national responses to curb the menace. These include enactment of laws, arrest, and prosecution of perpetrators, strengthening security arrangements, the establishment of the Inter-Agency Maritime Security Task Force (IAMSTF) and the Nigerian Extractive Industries Transparency Initiative (NEITI), granting of amnesty to ex-militants and collaboration with regional and international partners to curb the menace of crude oil theft, illegal bunkering and pipeline vandalism in the Niger Delta. Therefore, the paper concluded that it is due to faulty implementation of such national responses that the phenomenon is unabated.

Legislative Ratio of Strengthening State Cyber and Cryptography Agencies in Law Enforcement: A Perspective of Legal System Theory

This research aims to address cyber threats and crimes in Indonesia. Currently, the responsibility for the implementation of cyber security tasks and functions is handled by the State Cyber and Cryptography Agency (BSSN). This situation makes it very difficult for BSSN to operate if its duties, functions, and authorities in cybercrime law enforcement are only regulated through a Presidential Regulation. This study uses a normative juridical method with a legislative approach that examines norms related to the authority of investigative institutions in law enforcement against cyber crimes. These findings highlight that there were 24.6 million cyberattacks recorded from January 1 to September 7, 2022, which caused losses to the community. The existence of BSSN was formed by the President through Presidential Regulation Number 28 of 2021 concerning the State Cyber and Cryptography Agency in carrying out its duties and authority to overcome cybercrime in the face of obstacles. This requires a comprehensive legal due diligence strategy that can be adjusted to legal compliance and potential legal problems of cybercrime. The results of the study show that to ensure security, justice, and legal certainty in the enforcement of cybercrime law can be carried out properly, it is necessary to have special laws and regulations that regulate cybercrime, with the existence of institutions that will implement laws and regulations in special law enforcement to deal with cybercrime, by containing legal compliance based on the applicable national legal system.

Psychological status of medical security teams in Winter Olympic Games and Paralympics under COVID-19.

Medical security work for the 2022 Beijing Winter Olympic and Paralympics faced enormous challenges under COVID-19. This study aimed to investigate the mental status of those medical team members to provide a reference for scientifically implementing medical security services for large-scale events. In this prospective cohort study, the Patient Health Questionnaire-9, Self-rating Anxiety Scale, General Self-Efficacy Scale, and Psychological Questionnaire for Emergent Events of Public Health (PQEEPH) were administered to 145 members of the medical team. A generalized mixed linear model was used to analyze the impact of work duration, position, on/off rotation, and gender on psychological status. Work duration significantly impacted depression, anxiety, self-efficacy, and all dimensions of PQEEPH. Women scored higher than men in the PQEEPH dimensions of depression, neurasthenia, fear, and total score. Working status affected the dimensions of depression, neurasthenia, and total score. Deterioration in emotional state became apparent in the fourth week and recovered 1 week after the task concluded, while self-efficacy decreased from beginning to end. All the medical team members experienced emotional deterioration and decreased self-efficacy in medical security tasks. To maintain a medical team's psychological wellbeing during large-scale activities, rotation times should be set reasonably, and adequate mental health services should be provided.

Efficient malware detection using hybrid approach of transfer learning and generative adversarial examples with image representation

AbstractIdentifying malicious intent within a program, also known as malware, is a critical security task. Many detection systems remain ineffective due to the persistent emergence of zero‐day variants, despite the pervasive use of antivirus tools for malware detection. The application of generative AI in the realm of malware visualization, particularly when binaries are depicted as colour visuals, represents a significant advancement over traditional machine‐learning approaches. Generative AI generates various samples, minimizing the need for specialized knowledge and time‐consuming analysis, hence boosting zero‐day attack detection and mitigation. This paper introduces the Deep Convolutional Generative Adversarial Network for Zero‐Shot Learning (DCGAN‐ZSL), leveraging transfer learning and generative adversarial examples for efficient malware classification. First, a normalization method is proposed, resizing malicious images to 128 × 128 or 300 × 300 for standardized input, enhancing feature transformation for improved malware pattern recognition. Second, greyscale representations are converted into colour images to augment feature extraction, providing a richer input for enhanced model performance in malware classification. Third, a novel DCGAN with progressive training improves model stability, mode collapse, and image quality, thus advancing generative model training. We apply the Attention ResNet‐based transfer learning method to extract texture features from generated samples, which increases security evaluation performance. Finally, the ZSL for zero‐day malware presents a novel method for identifying previously unknown threats, indicating a significant advancement in cybersecurity. The proposed approach is evaluated using two standard datasets, namely dumpware and malimg, achieving malware classification accuracies of 96.21% and 98.91%, respectively.

VIRTUAL SIMULATOR FOR PROCESSING CONFIDENTIAL INFORMATION ON PHYSICAL MEDIA

Acquiring digital competencies is an important area in modern education. Using and applying digital tools and technologies allows to increase the level of students' competencies. A modern digital solution based on virtual reality technology is proposed. The virtual simulator is a set of interactive models in a three-dimensional virtual space, allowing to implement scenarios of interaction between users and special software for the practical application of information security tasks. An overview of existing virtual simulators and justification for the choice of software and processes for processing confidential information on physical media are provided. Digital competencies are an important area in modern education. A modern digital solution based on virtual reality technology is proposed. The virtual simulator is designed and represents a set of interactive models in a three-dimensional virtual space, allowing to implement scenarios of interaction between users and special software for the practical application of information security tasks. Experimental studies were conducted on working with the virtual simulator. Testing of the simulator confirmed its operability. The virtual simulator can be used by both practitioners in the field of information security software operation. In the future, it is planned to refine the simulator in terms of developing augmented reality scenarios and modeling information security incidents. The implementation of the virtual simulator is described. Statistical data on the results of the experiment are provided. On average, the training result using the simulator increased by 35,8 %.

Towards robust log parsing using self-supervised learning for system security analysis

Logs play an important role in anomaly detection, fault diagnosis, and trace checking of software and network systems. Log parsing, which converts each raw log line to a constant template and a variable parameter list, is a prerequisite for system security analysis. Traditional parsing methods utilizing specific rules can only parse logs of specific formats, and most parsing methods based on deep learning require labels. However, the existing parsing methods are not applicable to logs of inconsistent formats and insufficient labels. To address these issues, we propose a robust Log parsing method based on Self-supervised Learning (LogSL), which can extract templates from logs of different formats. The essential idea of LogSL is modeling log parsing as a multi-token prediction task, which makes the multi-token prediction model learn the distribution of tokens belonging to the template in raw log lines by self-supervision mode. Furthermore, to accurately predict the tokens of the template without labeled data, we construct a Multi-token Prediction Model (MPM) combining the pre-trained XLNet module, the n-layer stacked Long Short-Term Memory Net module, and the Self-attention module. We validate LogSL on 12 benchmark log datasets, resulting in the average parsing accuracy of our parser being 3.9% higher than that of the best baseline method. Experimental results show that LogSL has superiority in terms of robustness and accuracy. In addition, a case study of anomaly detection is conducted to demonstrate the support of the proposed MPM to system security tasks based on logs.