Security Tasks Research Articles

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.

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 hours of flow rate, chlorine level at treatment plant, water temperature, and residual chlorine at private homes as input to predict the 12 hours 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.

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.

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 %.

Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization

Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the data plane. SDN represents a paradigm shift towards agile, efficient, and secure network infrastructures, moving away from traditional, hardware-centric models to embrace dynamic, software-driven paradigms. SDN is compliant also with the virtualization architecture defined in the Network Function Virtualization framework. However, SDN should cooperate seamlessly for some years with the distributed TCP/IP control developed during the years all over the world. Among others, the traditional tasks of routing, forwarding, load balancing, QoS assurance, security, and privacy should be solved. The SDN native centralization brings also some new challenges and problems which are different from the traditional distributed control IP networks. The algorithms and protocols usable in SDN should meet requirements like scalability, convergence, redundancy assurance, sustainability, and good real-time response, and allow orchestrated automation in enhancing network resilience and adaptability. This work presents a theoretical review of state-of-the-art SDN optimization techniques, offering a critical and comparative discussion of various algorithms having tasks such as routing (including dynamic ones), forwarding, load balancing and traffic optimization, and forwarding delay minimization. Attention is pointed to general algorithms which can offer pragmatic solutions for large systems or multiple metric routing.

Violence against minors: concept and types

Protecting children from violent criminal offenses that infringe on their life, health, morality and sexual inviolability is a priority national security task that unites the efforts of the family, the state and civil society. Domestic violence against minors has characteristic features related to the specifics of the mechanism of committing criminal offenses, the causes and conditions that determine it, the personality of the perpetrators, and the peculiarities of the victim’s behavior, which must be taken into account when developing special prevention measures. The analysis of legislation and specialized literature leads to the conclusion that today there is No. single approach to the definition of a number of the most important concepts related to the topic of this study. This primarily concerns the concepts of “family” and “violence”. The concept of “violence” is now widely used not only in everyday life, but is also included in the terminology of various sciences. Philosophy, psychology, criminal law, criminology, forensics and other sciences approach the problem of defining the concept of “violence” independently, without using existing definitions in related fields. The definition of violence may vary depending on the purpose of its use. This is due to the fact that violence itself has a rather “broad format”. It is a social and legal phenomenon that is not limited to the criminal law. In other words, violence can include criminal offenses, administrative offenses, as well as other forms of behavior that are not formally torts, but contradict generally accepted and approved norms of behavior, forming the prerequisites for violent crime as such. In this regard, it is necessary to clarify that in the context of this study, only the criminal law concept of violence will be used, assessed from the standpoint of criminal law as an offense and, accordingly, possessing all its features. Such a concept is necessary for a clear definition of the subject of research and its use to distinguish related issues in the process of studying empirical material.

Secure Task Offloading and Resource Allocation Strategies in Mobile Applications Using Probit Mish-Gated Recurrent Unit and an Enhanced-Searching-Based Serval Optimization Algorithm

Today, with the presence of 5G communication systems, including Internet of Things (IoT) technology, there is a high demand for mobile devices (especially smartphones, tablets, wearable technology, and so on). Regarding this proliferation and high demand, the massive adoption of mobile devices (MDs) has led to an exponential increase in network latency; the heavy demand for cloud servers causes the degradation of data traffic, which considerably impacts the real-time communication and computing aspects of mobile devices. As a result, mobile edge computing (MEC), an efficient framework capable of enhancing processing, optimizing energy usage, and offloading computation tasks, is considered a promising solution. In current research, numerous models have been implemented to achieve resource allocation and task offloading. However, these techniques are ineffective due to privacy issues and a lack of sufficient resources. Hence, this study proposes secure task offloading and resource allocation strategies in mobile devices using the Probit Mish–Gated Recurrent Unit (PM-GRU) and Entropic Linear Interpolation-Serval Optimization Algorithm (ELI-SOA). Primarily, the tasks to be offloaded and their attributes are gathered from mobile users and passed to a local computing model to identify the edge server. Here, the task attributes and the server attributes are compared with a cache table using the Sorensen–Dice coefficient. If the attributes match, then details about the appropriate edge server are produced. If the attributes do not match, then they are inputted into a global scheme that analyzes the attributes and predicts the edge server based on the Probit Mish-Gated Recurrent Unit (PM-GRU). Then, the server information is preserved and updated in the cache table in the local scheme. Further, the attributes, along with the predicted edge server, are inputted into a system for privacy-preserving smart contract creation by using Exponential Earth Mover’s Distance Matrix-Based K-Anonymity (EEMDM-KA) to develop a secure smart contract. Subsequently, the traffic attributes in the smart contract are extracted, and the request load is balanced by using HCD-KM. Load-balanced requests are assigned to the edge server, and the optimal resources are allocated in the cloud server by using the Entropic Linear Interpolation-Serval Optimization Algorithm (ELI-SOA). Finally, the created smart contract is hashed based on KECCAK-512 and stored in the blockchain. With a high accuracy of 99.84%, the evaluation results showed that the proposed approach framework performed better than those used in previous efforts.

Fine-YOLO: A Simplified X-ray Prohibited Object Detection Network Based on Feature Aggregation and Normalized Wasserstein Distance.

X-ray images typically contain complex background information and abundant small objects, posing significant challenges for object detection in security tasks. Most existing object detection methods rely on complex networks and high computational costs, which poses a challenge to implement lightweight models. This article proposes Fine-YOLO to achieve rapid and accurate detection in the security domain. First, a low-parameter feature aggregation (LPFA) structure is designed for the backbone feature network of YOLOv7 to enhance its ability to learn more information with a lighter structure. Second, a high-density feature aggregation (HDFA) structure is proposed to solve the problem of loss of local details and deep location information caused by the necked feature fusion network in YOLOv7-Tiny-SiLU, connecting cross-level features through max-pooling. Third, the Normalized Wasserstein Distance (NWD) method is employed to alleviate the convergence complexity resulting from the extreme sensitivity of bounding box regression to small objects. The proposed Fine-YOLO model is evaluated on the EDS dataset, achieving a detection accuracy of 58.3% with only 16.1 M parameters. In addition, an auxiliary validation is performed on the NEU-DET dataset, the detection accuracy reaches 73.1%. Experimental results show that Fine-YOLO is not only suitable for security, but can also be extended to other inspection areas.

Artificial Intelligence as an Innovative Element of Support in Policing

Currently, the public security sector is faced with an increasing administrative burden that limits the ability of police officers to focus on core security tasks. This paper focuses on the possibility of using large-scale language models (LSMs) as an innovative tool to address this challenge. Based on a careful literature review and analysis of current trends in artificial intelligence, the author team develops a concept for integrating GPTs into police practice, with an emphasis on the potential for reducing administrative burden and supporting efficient processing of relevant information.As part of this research, we have identified key areas of policing where AI could bring significant value, including data analysis and document production assistance. However, it should be emphasized that this technology is still in its early stages of development and its implementation would require a carefully considered approach involving interdisciplinary collaboration and further research to test the theoretical assumptions presented in this study.Thus, this paper contributes to a deeper understanding of the potential benefits and challenges of integrating GPT into policing practice and outlines a path towards future innovative solutions in the field of public safety.

A Survey on Network Security Traffic Analysis and Anomaly Detection Techniques

With the increasingly severe network security situation, advanced network traffic anomaly detection techniques are urgently needed. This paper provides a comprehensive survey of the research status and latest progress in the field of network anomaly detection. Firstly, we introduce the basic concepts, common methods, and challenges of network traffic analysis, which lays the foundation for anomaly detection. Then, we systematically summarize the mainstream techniques in the anomaly detection field, including statistical methods, machine learning methods, deep learning methods, and behavior analysis methods, analyzing their basic principles, representative works, advantages and disadvantages, and applicable scenarios. Next, we focus on discussing the hybrid methods in the anomaly detection field, elaborating on the motivations, common strategies, and representative works of hybrid methods, and pointing out that hybrid methods are an important development direction for anomaly detection. In addition, the paper also summarizes the application effects of several types of methods in practical network security tasks and makes a quantitative comparison in tabular form. Finally, we prospect the future development trends of network anomaly detection techniques, proposing goals such as intelligentization, automation, federalization, and interpretability, while analyzing the challenges faced by anomaly detection, including data heterogeneity, complexity of security threats, model robustness, privacy protection, and interpretability. We argue that network anomaly detection requires interdisciplinary integration, strengthening of security big data governance, and a shift from passive defense to active immunity. As the strategic position of cyberspace security becomes increasingly prominent, driven by disruptive technologies such as big data, artificial intelligence, and blockchain, network anomaly detection will surely usher in new development opportunities and challenges.

Scoping Review: Digitalisation in the organisational culture of safety and security agencies

Digitalisation has a significant impact on most aspects of society. State agencies with safety and security tasks have to develop effective strategies to manage the opportunities and risks associated with technological innova-tions and increased networking. Emerging digital technologies have the potential to affect work practices and thus the organisational culture of such agencies. To examine the extent to which scientific research has addressed the impact of digitalisation on organisational culture so far, a systematic scoping review was conducted. Preliminary results show that the overall research topic is still in an exploratory phase and studies are primarily from the global north. The analyses show that the majority of studies focuses on artefacts around information data leading to respective work practices around data work.

Victimology of Digital Crime: Research Direction Concept

The need to protect society from crime, taking into account the peculiarities of its victimization, as well as transformation and adaptation to existing realities, poses the task of victimological security in the context of the development of digitalization not only to the state, but also to the scientific community of criminologists and victimologists . The use of the achievements of digital technologies for criminal purposes gives rise to new types of victimization of society . This situation causes the need for large-scale, more serious victimization research in this area and requires new forms and methods of countering crime .On the pages of the journal “Victimology” many times discussed the problems associated with victimization of society in the context of digital crime and various types of its manifestations . I believe that there is a need to introduce a new section devoted to the victimization aspects of digital crime and the digitalization of modern society

STATE OF ECONOMIC SECURITY AND DIRECTIONS OF RESTORATION SOCIO-ECONOMIC DEVELOPMENT AND FOOD SECURITY IN THE CONDITIONS OF WAR

The purpose of the article is to determine the state of economic security, ways to ensure its level, and ways to restore socio-economic development under the cumulative impact of threats. In order to realize this goal, an expert assessment of the components of economic security provided and the need to take into account the component of military-economic security indicated. It indicated that the task of neutralizing threats to military and economic security opens up new windows of opportunity, in particular opportunities in ensuring socio-economic development. Prerequisites for the use of opportunity windows are defined. The dynamic nature of threats of war that require institutional measures for permanent monitoring of dynamic threats, their operational analytical assessment and the introduction of tactics to neutralize threats and reduce their long-term negative impact, are indicated. An analysis of the state economic security provided, directions for improving this state by components of economic security indicated. The main factors on which the well-being of the country's population depends are determined, the effects of crises on the indicators of the levels of income and expenditure of the population analyzed, and the drivers of the deterioration of well-being are established. It noted that the deterioration of the socio-economic situation of households led to the narrowing of the domestic market, which had a negative impact on the functioning of SMEs. It indicated that this created a negative inverse multiplier effect on the well-being of the population. The negative effects of the crisis, which led to a reduction in production volumes in the agricultural sector, were identified. It indicated that this significantly limited the ability of the branch to fulfil food security tasks. Proposed ways to reduce the negative impact of war on agricultural production to ensure food security. The directions and recommendations for economic and food security and restoring socio-economic development are specified.