Threat Analysis Research Articles

Cybercrime in the context of the digital age: analysis of threats, legal challenges and strategies

The current period of social evolution is commonly referred to as the information stage of society’s development or the digital age. The introduction of the most advanced tools significantly speeds up the process of obtaining, processing and analyzing data. Wide and prompt access to information resources increases the efficiency of their use, which becomes an integral part of managing all institutions and processes. Various forms of crime are emerging globally as science and information technology evolve. The emergence and development of cybercrime is taking place, in particular, due to the globalization of the information environment and the rapid development of computer systems around the world. Currently, information technologies accompany people in various spheres of their activities, and accordingly, their use in committing criminal offences is increasing. In connection with the abovementioned, the issues of legal support for information security occupy a specific place in the public relations spectrum. The topic of cybersecurity in the era of information technology is increasingly spreading in the world community nowadays. After all, because of technological development, the number of cybercrimes that disrupt the activities of states, their institutions and a large number of private companies is rapidly increasing, with institutions and organizations incurring significant costs and disrupting their normal operation. The purpose of the academic paper is to define the concept and to review the classifications of cybercrime, taking into account the features of its commission and counteraction to this type of abuse in the context of digital society.

Cybersecurity in the face of information warfare and cyberattacks

Cybersecurity in the face of information warfare and cyberattacks in the modern world is one of the most critical issues of our time. Driven by the rapid development of technologies and digital transformation, threats to information security are becoming even more severe. Nowadays, almost every aspect of our lives, from personal data to critical infrastructure systems, is affected by cyberattacks. Information warfare has become a complex struggle where digital battles are integral to modern conflict. Countries, corporations, and even criminals fight for control over data and influence over critical systems. All this creates a situation where a defense against cyber threats is a top priority. The article aims to provide a detailed analysis and understanding of cyber threats' dynamics in the context of information warfare. It also highlights effective strategies and measures to ensure cybersecurity in such a challenging environment. The research methodology is based on comparing and analyzing information on real cyberattacks, studying the vulnerabilities of information systems, and developing strategies and technologies to counter cyber threats. The study employs both quantitative and qualitative methods, including statistical analysis and case studies. The results of the study revealed the main features of information warfare and their impact on cybersecurity globally and within specific organizations and states. Conflicting parties can use digital means to attack opponents, destroy their critical infrastructure, obtain secret information, or disrupt their operations. Information operations may use phishing and social engineering tactics to access essential resources or information. The organizations and states that find themselves at the epicenter of information warfare must carefully ensure the cybersecurity of their information resources.

Dynamic Real-Time Analysis of Network Attacks Based on Dynamic Risk Probability Algorithm

With the rapid development of Internet technology and its application, the existence of network vulnerabilities is very common. Attackers may use the defects of software, hardware, or system security policy in the network system to access or destroy the system without authorization. How to nip in the bud and carry out a safety risk assessment and early warning is an urgent problem to be solved. Based on the overall assessment of the risk factors in the whole network, the more dangerous nodes are found and priority measures are taken. The method proposed in this paper can reflect and predict the actions of attackers, repair, and adjust the previously predicted probability. It is compared with the method that evaluates the uncertainty in the network solely by calculating the static probability. The proposed new ideas and methods better reflect the real-time changes in the actual environment of the Internet, thereby better responding to the actual situation. This method can be well applied to threat detection, threat analysis, and risk assessment of monitoring system networks, enabling monitoring network managers to evaluate and protect the security of real-time power grids. It is of great significance to effectively defend against network attacks, ensure system security, and study the resistance of control systems under network attacks.

PREDICTING FRAUDULENT CRYPTOCURRENCY TRANSACTIONS USING LOGISTIC REGRESSION

The article conducts a SWOT analysis of cyber threats, identifies the strengths and weaknesses of cryptocurrencies. It is determined that the development of cryptocurrencies forms a new class of digital assets, which is attracting increasing attention from the economic and financial communities and information technology. The issue of detecting fraudulent transactions with cryptocurrency is highlighted. To solve the problems of detecting fraudulent transactions, the authors propose to use new technologies based on data analysis methods, in particular, the development of logistic regression models. The following algorithm for classifying fraudulent transactions with cryptocurrency is proposed, which is reduced to the classical data classification scheme. The following steps are highlighted: data loading into the dataset and primary analysis, data preparation for analysis, division into training and test samples, application of the classification algorithm on the training sample, evaluation of the model accuracy on the test sample, model optimization if necessary, and conclusion, where, if the model accuracy is high, it can be used to classify fraudulent transactions. The main task of the presented stages is to detect suspicious cryptocurrency transactions with as few false positives as possible. The classification of cryptocurrency transactions is proposed to be carried out with the Ethereum cryptocurrency. The R language and its integrated processing environment R Studio are chosen as tools. A logistic regression model has been developed to detect fraudulent transactions with cryptoassets. The model checks a new transaction for fraud. The model's high accuracy of 98 percent demonstrates its effectiveness. The model can be improved to take into account new types of fraudulent schemes and applied to analyze transactions with different assets, making it promising for use in financial institutions and cryptocurrency exchanges.

Piper agroforestry in the Indian Himalayas: indigenous peoples’ practices, policies and incentives

BackgroundIndigenous populations across the world play a significant role in sustainable land management and conservation of biodiversity. However, indigenous agricultural practices are rarely studied in depth and remain poorly documented in many regions of the world. Documenting such practices and identifying policies and incentives that affect them can unlock their potential for better land management and biodiversity conservation.MethodsWe undertook household surveys and focus group discussions to document indigenous practices and the values of trees in Piper (Piper betle) agroforestry practiced by the ethnic Khasi (Pnar) community in the Indian Eastern Himalayas. We also undertook an in-depth vegetation sampling to quantify variations in phytosociology and tree diversity with stand age in Piper agroforestry and nearby native forests. In addition, we undertook strengths, weaknesses, opportunities and threats (SWOT) analysis and a desk review to identify policies, market support structures and incentives affecting cultivation of Piper, a cash crop of global importance.ResultsUnlike in shifting cultivation, indigenous people do not fell trees in the Piper agroforestry; instead, they allow trees to regenerate naturally and also enrich by planting tree seedlings in the gaps. Depending on the stand age, 30–49 tree species were recorded in the Piper agroforestry compared to 39 in nearby natural forests. While tree density was higher in the natural forests, greater species richness, diversity and basal area was recorded in > 25 years old Piper agroforestry stands. However, landholders do not have legally transferable or heritable rights to the land or trees.ConclusionIt is concluded that the Piper agroforestry provides a pathway for averting land degradation due to shifting cultivation, biodiversity conservation and improving livelihoods of the indigenous community. Although existing policies are supportive, Piper agroforestry is not currently benefiting from incentives and market support structures. We recommend implementation of policies, market support structures, incentives and payment for ecosystem services so that indigenous communities can benefit from the global ecosystem services they provide.

Threat Analysis of Nipah Virus as a New Pandemic from the Perspective of Medical Intelligence

According to World Health Organization (WHO), Nipah virus is a type of infectious disease from 10 types of infectious diseases that can pose a risk to the public health. Besides, it has the potential to become a new pandemic. The State Intelligence Agency has the authority, mandate, and responsibility to actively engage in early detection of a disease that could potentially become an epidemic and cause a threat to national security and interests. The formulation of this research problem is " Threat Analysis of Nipah Virus as a New Pandemic from the Perspective of a Medical Intelligence." The authors used a qualitative descriptive method approach through interviews and literature studies related to Nipah virus research. The results of the research indicated that the Nipah virus has the potential to become a new pandemic that can threaten the Indonesian people health and even the world. Thus, early detection of the Nipah virus spreading in Indonesia is necessary, by collaborating across ministries in implementing preventive measures using a one health concept approach.

Exploration of The Potential of Suwangi Tourism Village Through Swot Analysis in East Lombok Regency, Indonesia

Suwangi Village is one of the villages located in Sakra District, East Lombok Regency. Suwangi Village is trying to pioneer a tourism village through natural, cultural, artificial, and special interest tourism. Therefore, to develop the potential of Suwangi village, a strategy is needed to make a tourism village that can continue to grow and attract the attention of tourists to visit. This research uses qualitative descriptive methods and uses Strenght, Weakness, Opportunity and Threat Analysis so that the strengths and weaknesses of the village can be recognized and to see the opportunities and threats faced to pioneer Suwangi Village into a tourist village. Based on the results obtained, several aspects that become the strength of Suwangi village's tourist attraction include natural beauty that is still natural, a fairly large area, has relics that are considered sacred and preserved until now. In addition, public awareness and understanding of the management and development of tourist destinations is still limited, becoming a weakness of the management of this tourist village. It can also be known opportunities and threats such as natural disasters, public perceptions that have not been educated about tourism tend to think negatively to be an obstacle to tourism development, and security is still not good. Although it still has some shortcomings, the potential tourist attraction of Suwangi village is quite adequate as a competitive tourist village.

Chronologie de la certification ISO 9001:2015 et enquête de satisfaction au sein du centre d’investigations cliniques plurithématique (CIC-P) de l’historique hôpital Saint-Louis en période de pandémie du COVID-19

The main objectives of multidisciplinary clinical investigation center (CIC-P) are to facilitate the availability of new drugs for patients, to enhance the visibility and attractiveness of French clinical research, to improve the quality of early phase trials, and to enhance the value of academic research by evaluating molecules in rare diseases. Since 2017, the CIC-P has been committed to a quality approach process, launching in 2018 its first satisfaction survey on patient care and clinical trial management of all its employees. A second satisfaction survey targeted by profession type was to be launched in 2020, in view of the requirements of the ISO 9001:2015 standard, but the process was interrupted following the coronavirus diseases 2019 (COVID-19) pandemic. The successful reorganization of the CIC-P activity during the first containment of the COVID-19 pandemic was driven by the implementation of a quality management system that promotes continuous improvement through the organization and involvement of all the staff. This voluntary and participative approach motivated the CIC-P to apply for the organizational sesame. The ISO 9001:2015 certification of CIC-P aims at increasing its performance, to satisfying its customers and to fully integrate its activities in a continuous improvement process, according to the requirements of this international standard, through the deployment of quality tools such as The Deming wheel (PDCA), an indispensable tool for transformation and reorganization; the analysis of the environment by the strengths, weakness, opportunities, threats (SWOT) analysis tool; the analysis and management of risks by the FMEA method, and all with performance indicators (SMART) and precise objectives at each stage of a project/process. The implementation of satisfaction questionnaires remains the essential tool for evaluating the expectations and needs of interested parties, but also for improving the quality of CIC-P activities and services. All these tools put in place have allowed us to continuously improve the means of production and to constantly improve our organization.

GENICS: A Framework for Generating Attack Scenarios for Cybersecurity Exercises on Industrial Control Systems

Due to the nature of the industrial control systems (ICS) environment, where process continuity is essential, intentionally initiating a cyberattack to check security controls can cause severe financial and human damage to the organization. Therefore, most organizations operating ICS environments check their level of security through simulated cybersecurity exercises. For these exercises to be effective, high-quality cyberattack scenarios that are likely to occur in the ICS environment must be assumed. Unfortunately, many organizations use limited attack scenarios targeting essential digital assets, leading to ineffective response preparedness. To derive high-quality scenarios, there is a need for relevant attack and vulnerability information, and standardized methods for creating and evaluating attack scenarios in the ICS context. To meet these challenges, we propose GENICS, an attack scenario generation framework for cybersecurity training in ICS. GENICS consists of five phases: threat analysis, attack information identification, modeling cyberattack scenarios, quantifying cyberattacks, and generating scenarios. The validity of GENICS was verified through a qualitative study and case studies on current attack scenario-generating methods. GENICS ensures a systematic approach to generate quantified, realistic attack scenarios, thereby significantly enhancing cybersecurity training in ICS environments.

Targeted placement for people with disabilities in Italy: a perspective from Lombardian companies

PurposeThe United Nations Convention on the Rights of Persons with Disabilities (2006) recognizes the right of disabled people to access work. Against this legislative backdrop, this study explores the strengths and weaknesses of the Italian system of targeted placement for disabled people, based on Law 68/1999, which delegates to regional authorities the management of the labor market. The examination centers on the perspective of companies, the primary stakeholders in the inclusion of persons with disabilities within organizational structures.Design/methodology/approachThe article discusses the results of focus groups conducted with 28 managers of large, medium and small enterprises in Lombardy (Italy). Qualitative analysis was employed, and the results were structured using a simplified strengths, weaknesses, opportunities and threats (SWOT) analysis, incorporating practical recommendations.FindingsThe analysis leads to practical suggestions to improve the entire targeted placement process at the regional level, from selection and accompaniment to evaluation, such as improving the networking of local stakeholders who deal with the inclusion of disabled people, homogeneity of the procedures in different regions, making all employees aware of diversity management, etc. The territorial network and the welfare environment are particularly important in achieving a successful targeted placement and to promote an inclusive corporate culture.Research limitations/implicationsThis study is not representative of Italy as a whole, as it remains a qualitative investigation focused on a single region.Originality/valueThis contribution accomplishes an in-depth study of the law of labor inclusion of people with disabilities observed from the point of view of companies, which are still usually reluctant to integrate people with disabilities into their organizations or encounter difficulties in doing so.

Machine Learning Applications in Cyber Threat Intelligence: A Comprehensive Review

Contemporary institutions are consistently confronted with fraudulent activities that exploit weaknesses in interconnected systems. Securing critical data against unauthorized access by hackers and other cybercriminals requires the application of robust cybersecurity protocols. As the number and complexity of cyber threats continue to grow, innovative prevention strategies are required. The objective of this study is to investigate the correlation between machine learning (ML) and cyber threat intelligence (CTI) to improve cybersecurity strategies. For the detection of anomalies, the analysis of malware, and the prediction of threats, ML techniques are indispensable in industries including retail, finance, healthcare, and cybersecurity. By employing critical threat information (CTI), security teams can gain a comprehensive understanding of adversary strategies and bolster defensive measures; thus, they play a pivotal role in proactive defense. Integration of ML and CTI facilitates exhaustive analysis by automating the acquisition, processing, and categorization of data. However, obstacles arise when confronted with issues such as risk assessment, the requirement for precise data, and the initial stages of machine learning implementation in business intelligence. In this paper, we present an extensive examination of the current literature concerning the visualization of Cyber Threat Intelligence (CTI) and the utilization of Machine Learning (ML. Therefore, the report concludes with an analysis of emergent threats, potential future applications of AI and ML in the field of cyber threat intelligence, and the critical contribution of machine learning to the improvement of cybersecurity.

A framework for co‐designing decision‐support systems for policy implementation: The LANDSUPPORT experience

AbstractThis work proposes a framework for co‐designing decision‐support tools for sustainable land management and soil protection at multiple scales. Geospatial dashboards, due to their key capabilities in the use of spatial or geospatial information, are quickly gaining traction for planning and policymaking. Developing the decision‐support system (DSS) as a transversal system capable of capturing trends in land and soil properties at the local, regional, national, and EU levels has been co‐designed with policy stakeholders. This work seeks to link (i) the main goal of the Soil Mission and the UN Sustainable Development Goals (SDGs), to raise awareness and knowledge on soil conditions (ii) and the LANDSUPPORT (LS) project cross‐evaluation on how the spatial decision‐support system (SDSS) can support policy‐related stakeholders and help them to take evidence‐based decisions. To achieve this objective, we present the user engagement process to ensure broad testing and evaluation of the LS SDSS's ability to support selected EU policies and soil‐related SDGs by testing the LS platform's European scale tools, including an analysis and conformity check of the data delivered by the LS tools and a critical review of results. The indicators were assessed via direct contact with end users, such as semi‐structured interviews (SSI) and 184 questionnaires. Results of the test series have been analyzed by the spatial scale per respective tool and performance indicators. We present a unique, integrated, science‐based approach to co‐create data‐driven decision‐making with the stakeholders to promote sustainable land management practices. This methodology strives to involve many stakeholders in scientific research, empowering them to participate in the decisions on topics that directly affect them. Public bodies responsible for land policy implementation, environmental stakeholders, spatial planners, and other users have engaged in the process to ensure broad testing of the LS platform from 2020 to 2022. A strengths, weaknesses, opportunities, and threats (SWOT) analysis provided a synthesis of the performance of the LS tools. The testing phase proved the utmost importance of usability, underlining that the mixed method of testing allowing quantitative and qualitative analyses based on the same key indicators proved essential for co‐designing SDSS tools to be used by a wide range of stakeholders.

The Analysis of Extreme Climate Threats in Maryland’s Lower Region Along the US Mid Atlantic

The Analysis of Extreme Climate Threats in Maryland’s Lower Region Along the US Mid Atlantic

Performance optimization of fourth-generation biorefinery departments: A novel mixed macroergonomics-sustainability framework

Today, crises caused by environmental pollution such as climate change have emerged as fundamental challenges with far-reaching and sometimes irreparable effects on the environment. Fourth-generation biorefineries (FGBs) have been recognized as a primary solution to these crises. There is a need to establish a comprehensive framework that can evaluate and enhance the performance of various departments in FGBs. This study introduces an integrated framework for evaluating and enhancing the performance of FGB departments, focusing on macroergonomics and sustainability indicators. First, the relative importance of each indicator was calculated using the best-worst method. The necessary data were collected using a validated questionnaire, and factor analysis was employed to assess its validity. The efficiency score of each decision-making unit was then computed using data envelopment analysis (DEA). Principal component analysis (PCA) was performed to validate the DEA results. In the next step, the positive or negative effects of each indicator on the performance of the refinery departments were investigated using sensitivity analysis and statistical tests such as Kolmogorov-Smirnov, Levene, paired t-test, and Wilcoxon. The results of sensitivity analysis indicated that the FGB had the highest performance in reducing injury rates but the lowest in reducing lost workdays caused by injuries or illnesses. In addition, appropriate strategies were developed using Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis to improve the performance of the refinery's departments. To the best of our knowledge, this research is the first attempt to assess the qualitative and quantitative performance of FGB departments on the basis of sustainability and macroergonomics indicators.

Examination of the role of the rotating nursing department in the training of nursing staff based on SWOT analysis.

The implementation of the rotation system in the Chinese medical industry has achieved significant results. The present study aims to 1) explore the strengths, weaknesses, opportunities and challenges of rotational nursing department implementation and 2) provide references for developing nursing staff's competencies in leadership, performance evaluation, quality of care, communication in relationships and human resources. A total of 16 rotational nursing department staff members from a tertiary tuberculosis specialist hospital in Beijing were interviewed, and the interview data were analysed using a strengths, weaknesses, opportunities and threats analysis and class analysis. The advantages of the rotational nursing department included: (1) stimulating the nursing staff's enthusiasm and creativity; (2) strengthening the communication and collaboration between departments; (3) improving the detailed management of nursing quality; and (4) enhancing the nursing staff's comprehensive abilities. The disadvantages included: (1) the design of the rotation programme focusing on practice; (2) a lack of personalisation; and (3) imperfect performance assessment of the rotating staff. Opportunities included: (1) deepening the connotation of nursing job management and (2) developing the construction of nursing discipline and the need for personal career development and value realisation. Threats included the lack of a sound rotation management model to draw on. A rotational nursing department is conducive to enhancing the competence of nursing staff in management positions and providing new ideas for hospitals to select and train nursing management talents. By taking full advantage of the benefits of vertical nursing management, designing personalised rotation training programmes, building a diversified learning and training platform and developing a positive performance incentive mechanism is recommended to fully engage the role of rotation in nursing management talent training.

Integrating Quantum Computing and NLP for Advanced Cyber Threat Detection

The exponential growth of digital data and the increasing sophistication of cyber threats demand more advanced methods for threat analysis. This paper explores the integration of quantum computing and natural language processing (NLP) to enhance cyber threat analysis. Traditional computing methods struggle to keep up with the scale and complexity of modern cyber threats, but quantum computing offers a promising avenue for accelerated data processing, while NLP provides sophisticated tools for interpreting and understanding human language, crucial for analysing threat intelligence. Our proposed framework leverages quantum algorithms for rapid anomaly detection and advanced NLP techniques for precise threat identification and analysis. The methodology includes data collection from diverse sources, pre-processing for normalization, quantum-assisted data processing using Grover's search and Quantum Approximate Optimization Algorithm (QAOA), NLP analysis with transformers and BERT-based models, and integration of findings to build comprehensive threat profiles. Experimental results demonstrate significant improvements: quantum algorithms reduced data processing time by up to 50%, NLP models achieved 92% accuracy in threat identification, and the false positive rate was reduced by 30%. These findings indicate a promising direction for next-generation cybersecurity solutions, enabling more proactive and efficient threat mitigation. Future work will focus on refining quantum algorithms, enhancing NLP models, and expanding the framework for real-time threat detection capabilities.

Applying threat analysis approach in a small forest urban park (Northern Italy): local expert-based assessment to prioritize the management actions

Abstract To overcome the human-induced threats impacting on ecosystems, managers should focus on priorities. Here, we applied the expert-based Threat Analysis (TAN) in a forest urban park (Northern Italy), involving experts which ranked local threats, from the more to less impacting and following the IUCN classification. We also evaluated the level of knowledge of operators about these threats. Experts identified five priority target-specific threats: Roads and Railroads; Invasive-Non Native/Alien species; Other Ecosystem modifications; Recreational Activities, and Storms and Flooding. Storms and Flooding and Invasive-Non Native/Alien species appeared the threats with significant highest magnitude. Knowledge of threats is comparable without significant difference among them. However, Storms and Flooding and Roads and Railroads are the threats having both the highest level of knowledge by experts and the highest magnitude. At the opposite, Mowing was the less known threat regarding its regime and showed the lowest magnitude. TAN approach should be routinely used to build conceptual frameworks, ranking threats from the more to less impacting, therefore optimizing the management effort and developing local projects.

ЗРОСТАННЯ РОЛІ РЕГТЕХ ЯК СКЛАДОВОЇ БІЗНЕС МОДЕЛІ ФІНАНСОВОЇ УСТАНОВИ У ПЕРІОД ЦИФРОВИХ ТРАНСФОРМАЦІЙ

The article analyzes the reasons for the emergence of Regtech, in particular, the complexity and variability of legislation, the growth of regulatory costs of both financial institutions and regulators, the need for new technologies that would optimize operations to comply with regulatory requirements. Objectives, tools and main characteristics of RegTech analyzed. The division of RegTech technological processes into customer onboarding, constant monitoring, risk identification, and comprehensive reporting is defined. The key features of RegTech are analyzed, namely blockchain, data standardization, high costs, high compliance risks, risk management, importance. RegTech has the potential for a wide range of applications from financial regulation to monitoring corporations for compliance with environmental legislation. Therefore, RegTech gradually outgrows the boundaries of the FinTech sector and becomes a relatively independent direction.The driving forces of the "explosive" growth of the RegTech market are identified, which include the aggravation of the need for post-crisis regulatory changes and the increase in data flows from controlled organizations; growing complexity of regulatory requirements; minimization of growing costs for ensuring compliance with regulatory requirements and standards; technological progress and rapid implementation of advanced technologies; real-time transaction monitoring, threat analysis and secure identity verification; increasing the effectiveness of supervisory tools in order to support financial stability. Innovative trends in the development of RegTech are analyzed, which include the focus on natural language processing, the development of AML and the metauniverse, the spread of cryptocurrencies, distributed ledger technology, voice recognition technology, and geographic information systems. The growing emphasis on business efficiency is increasing the popularity of RegTech among financial institutions, which allows them to increase productivity by reducing regulatory costs, protecting digital data and meeting regulatory requirements.

The Utility of Head-Mounted Eye Gaze Tracking for Vision-in-Action Assessments to Enhance Skill Acquisition and Sport Performance: A Strengths, Weaknesses, Opportunities, and Threats Analysis

Mobile eye tracking technology enables the objective evaluation of what, when, and how athletes use visual information from the environment to control action in service of skilled performance. This review highlights the strengths, weaknesses, opportunities, and threats (SWOT) to consider before adopting this technology to evaluate visual–motor control for enhanced skill acquisition and performance in sport. Strengths include increased ecological validity by recording the athlete within their natural performance environment. Weaknesses include diminished tracking fidelity based on the eye’s morphology or squinting, eye tracker movement, and lens fogging. Opportunities consist of novel assessments around research themes such as combatting the negative effects of pressure and performance enhancement through personalized training. The formfitting technologies also introduce recording opportunities during standard practices. Data processing time, synchronization of video streams, and hand coding of variables comprise the threats in addition to the hardware cost and the fees associated with crowdsourcing data coding. Currently, it is also not feasible to provide coaches with a report of their athletes’ data in a timeline suitable to create player-specific drills. Evaluating portable eye tracking technology with a SWOT framework helps translate scientific knowledge to practitioners considering implementing this technology for athlete assessment and development.

DETECTION, ANALYSIS AND PROTECTION OF CONFIDENTIAL DATA USING AMAZON MACIE MACHINE LEARNING TECHNOLOGY

Over the past decades, the field of data storage and processing has undergone significant changes and expansion, especially with the advent of cloud technologies and computing. Cloud services enable organizations to store and access large amounts of data through distributed systems. However, along with these new opportunities come new challenges, particularly in the area of protecting confidential data. Protecting sensitive data is an extremely important task for today's organizations, especially in the face of a growing number of digital threats and security breaches. In order to ensure reliable protection of valuable and sensitive information, developers and researchers are actively working on the development of new technologies and tools. One of the powerful tools used to identify, analyze and protect confidential data is the machine learning technology of the Amazon Macie service. Amazon Macie is an AWS cloud computing service that uses artificial intelligence and machine learning algorithms to automate data analysis and identify potential data security threats. The main purpose of this work is the detection, analysis and protection of confidential data using Amazon Macie machine learning technology. Amazon Macie is an innovative service developed by Amazon Web Services (AWS) that uses advanced machine learning algorithms for automated discovery and analysis of sensitive data. As part of the work, an analysis of the main machine learning algorithms, principles of data storage systems and methods of protecting confidential information was carried out. The working principles and capabilities of Amazon Macie, which uses advanced machine learning algorithms for automated data analysis and detection of potential threats to data security, were investigated.