Security Monitoring Research Articles

Implementing Hybrid Cloud Solutions Using Azure Arc to Integrate On-Premises and Cloud Environments.

The following research paper has provided the importance of implementing a hybrid cloud solution using Azzue Arc and integrating it into on-premises and cloud environments. At the same time, the key benefits which have been observed are minimising the overall costs, robust compliance and security, centralised management and being scalable and flexible. Furthermore, challenges have been identified as keeping track and location of the resources and controlling the costs have been mitigated by the augmentation of strategies such as the application of robust security and control and monitoring and troubleshooting in hybrid cloud environments has navigated the path for the organisations to stay sustainable in the upcoming days. Keywords: Azure Arc, Hybrid Cloud Solutions, On- Premises, Cloud Environments, DevOps

WoodAD: A New Dataset and a Comparison of Deep Learning Approaches for Wood Anomaly Detection

ABSTRACTAnomaly detection is a crucial task in computer vision, with applications ranging from quality control to security monitoring, among many others. Recent technological advancements have enabled near‐perfect solutions on benchmark datasets like MVTec, raising the need for novel datasets that pose new challenges for this modelling task. This work presents a novel Wood Anomaly Detection (WoodAD) dataset, which includes defects in wooden pieces that result in challenges for the most advanced techniques applied to other established datasets. This article evaluates such challenges posed by WoodAD with one‐class and few‐shot supervised learning approaches. Our experiments herein reveal that EfficientAD, a state‐of‐the‐art method previously excelling on the MVTec dataset, outperforms all other one‐class learning approaches. Nevertheless, there is room for improvement, as EfficientAD achieves a 0.535 pixel/segmentation average precision (AP) over the complete test set. UNet, a well‐known pixel‐level classification architecture, leveraged few‐shot supervised learning to enhance the pixel AP score, achieving 0.862 pixel/segmentation AP over the entire test set. Our WoodAD dataset represents a valuable contribution to the field of anomaly detection, offering complex image textures and challenging defects. Researchers and practitioners are encouraged to leverage this dataset to push the boundaries of anomaly detection and develop more robust and effective solutions for more complex real‐world applications. The WoodAD dataset has been made publicly available in Kaggle (https://www.kaggle.com/datasets/itiresearch/wood‐anomaly‐detection‐one‐class‐classification).

A comprehensive and systematic literature review on intrusion detection systems in the internet of medical things: current status, challenges, and opportunities

The increasing number of medical devices in the Internet of Medical Things (IoMT) environment has raised significant cybersecurity concerns. These devices often have weak security features, poor design, and insufficient authentication protocols, making them vulnerable to cyberattacks and intrusions. To mitigate these threats, robust security measures are essential. This includes implementing strong security protocols, ensuring continuous security monitoring, enforcing regular updates, and maintaining a constant response plan. Additionally, designing an effective Intrusion Detection System (IDS) is crucial to safeguard patient data and devices. This paper systematically studies the current state of the literature and the essential methods for intrusion detection in the IoMT. Employing a selection process, the paper identifies 28 critical studies published between 2018 and April 2024. The intrusion detection mechanisms in the IoMT are divided into five categories: IDS based on artificial intelligence models, datasets used in IoMT for IDS, fundamental security requirements, intrusion detection processes, and evaluation metrics. This paper dissects the various mechanisms within each category in a meticulous and comprehensive analysis. Finally, the paper examines the challenges and open issues in developing IDSs in IoMT. By offering a roadmap for researchers to enhance IDSs in the IoMT, this paper has the potential to significantly impact the fields of computer engineering, cybersecurity, and healthcare, thereby contributing to the advancement of these crucial fields.

Empowering Database Administrators: The Essential Role of Automation in Modern Practices

Managing large servers and databases while maintaining the uninterrupted functioning of critical applications presents significant challenges for database administrators. Automating database administration has become a crucial component in enhancing high availability, reliability, efficiency, and scalability. To reduce mistakes, minimize manual interventions, and improve database process performance, automation is essential. Important components include disaster recovery plans, security monitoring, performance review, and automatic backups, all of which are intended to prevent data loss. This study explores how businesses use automated database administration and contemporary automation techniques to maintain a competitive edge in an increasingly data-centric environment. Keywords: Jenkins, data, ansible, automation, mistakes, CICD, access, backups, and consistency.

Terahertz programmable metasurface based on solid-state plasma.

Terahertz modulation technology based on programmable metasurfaces can respond in real time to external signals or environmental changes, enabling flexible and adaptive control of terahertz waves. This technology demonstrates significant potential and importance across various fields, including communication, imaging, scientific research, security monitoring, and industrial applications. This paper proposes a terahertz programmable metasurface based on solid-state plasma, utilizing solid-state plasma devices to achieve dynamic control of properties such as the amplitude and phase of terahertz waves. Compared to traditional silicon-based devices, GaAs/AlGaAs heterostructures enhance both the forward current and solid-state plasma concentration of the metasurface devices by two orders of magnitude, with carrier concentrations exceeding 1019 cm-3. Additionally, we designed a solid-state plasma metasurface containing 12×12 supercells, achieving dynamic modulation of terahertz waves in the elevation direction through different coding schemes. Furthermore, this paper explores the stealth control mechanism of solid-state plasma metasurfaces, achieving effective cloaking of detection targets through specific phase compensation.

Integrating Natural Language Processing with AdaBoost, Random Forest, and Logistic Regression for an Advanced Ensemble-Based Network Intrusion Detection Model

Higher numbers and more complicated traffic data passing over your network requires that you have some advanced ways to properly identify security threats. In this paper, we design an ensemble intrusion detection system that unites different machine learning strategies such as due to Natural Language Processing (NLP), AdaBoost, Random Forest and Logistic Regression to identify several kinds of network intrusions. Problem: To deal with more and more stronger attacks, detect rate of the traditional Intrusion Detection Systems remains feckless. Based on the results of this analysis, the important features are extracted from network traffic logs by applying NLP and feed it to espresso classifier in the ensemble approach. Because AdaBoost increases the performance of weak learners, Random Forest provides robustness and Logistic Regression provides interpretability in final decision making. It is trained and tested on benchmark datasets like the NSL-KDD, and CICIDS2017. Overall, in the case results ensemble models were better performance than all individual classifiers regarding accuracy, precision and recall especially in rare case of attack types. Using NLP for feature extraction has enabled the detection of sophisticated attack signatures, showing that this model performs well for real-time security monitoring in high throughput networks like corporate or cloud environments.

Power system security monitoring: quantum computing aided anomaly detection and early warning system

Power system security monitoring: quantum computing aided anomaly detection and early warning system

Evaluasi Kinerja Sensor Penginderaan Jauh dalam Pemantauan Perubahan Iklim dan Dampaknya terhadap Keamanan Nasional

The use of remote sensing techniques has been increased in Indonesia, given that this technique can obtain data and information without interacting directly with related objects, regions, and phenomena. This study then aims to analyze the performance of remote sensing sensors in Indonesia, especially in the monitoring of climate change and national security. The method used is a literature review method that focuses on the use of secondary data that includes trusted and credible textual materials. The study’s result shows that remote sensing has a crucial role in obtaining accurate data on climate change, thus disaster mitigation can be improved. Meteorological and climatological data provided by remote sensing sensors include cloud cover and movement, water vapor analysis, wind field analysis, surface radiation, atmospheric temperature structure, weather analysis and forecasting, and extreme weather estimation. Not only that, but remote sensing also plays an important role in supporting the realization of national security because of its ability to provide data on threats and security which is very useful in territorial guidance, military observation, and reconaissance activities.

Methodology for Determining Means of Monitoring Information Security by the Method of Expert Assessment

The article examines and analyzes the numerous advantages of using information technologies to ensure the information security of organizations in connection with the wide spread of the number of modern methods of cyber attacks. It is established that effective cyber protection requires an information security management system with a set of modern event monitoring tools depending on the specifics of each organization. To select an appropriate system and evaluate the effectiveness of its tools, the method of expert evaluation is used in the work. In order to improve the determination of the weight coefficient of each tool of the system, a composite indicator is proposed, based on the sum of the products of individual indicators of the system tools and their priority coefficients. The features of the modern widely used solutions considered in the study confirmed the feasibility of the proposed methodology for determining effective tools of the information security monitoring system. The resulting data allows us to help organizations make an evidence-based decision about the optimal composition of the information security monitoring system.

Transmuting Detached Patient Consideration through Secure and Private Healthcare Monitoring Systems

Making use of the approach called SecureConnect, the article titled “Revolutionizing Remote Patient Care with Secure and Private IoT-Based Healthcare Monitoring Systems” describes how it functions. Thanks to the usage of modern encryption methods in its Internet of Things substrate, SecureConnect safeguards patient information and data from falling into the wrong hands as a result of the modern industry it was built for – digital health. The procedures used involve a methodical development and issuance of SecureConnect followed by it being subjected to controlled experimentation, replicating the edifice of the actual healthcare setting for validation. After analyzing the security feature of SecureConnect, we show that it outperforms comparable approaches, namely, SecureMed, iGuardian, and MedGuard by benchmarking SecureConnect’s security architecture. It was also evidenced that there is a highly significant difference between the two systems which supports the idea of how SecureConnect could help to transform the era of remote patient care. The accuracy of SecureConnect to detect all potential threats is 94%, while for SecureMed, iGuardian and MedGuardian; it is 88%, 91% respectively. Sensitivity, one of the measures applied in tracking healthcare, shows SecureConnect’s proficiency at 96 percent, surpassing competitors. The comparison with SecureMed, iGuardian and MedGuardian as for specificity proves its advantage as well: 92% opposed to 89%, 92% and 88% correspondingly. These two numerical outcomes substantiate SecureConnect’s position as an effective new concept in managing remote patient care since consistent out-performing of the assessment indices has been achieved.

A CRISPR/Cas12a-based competitive aptasensor for ochratoxin A detection.

The serious contamination of ochratoxin A (OTA) in agricultural products has promoted the development of rapid, sensitive, and selective analytical methods for OTA monitoring. We demonstrated a competitive aptasensor for OTA detection using CRISPR/Cas12a as an effective signal amplifier. OTA competes with complementary DNA of the aptamer on the microplate to bind to the aptamer. Streptavidin bridges the biotinylated aptamer and biotinylated activator to convert the OTA input into Cas12a activation, which cleaves fluorescent DNA reporters. Under optimized experimental conditions, the aptasensor was demonstrated to work well for sensitive detection of OTA, with a linear range from 0.5 nM to 62.5 nM and a detection limit of 0.5 nM. Moreover, our method not only exhibits high selectivity, but also has satisfactory anti-interference ability against complex sample matrices. Taken together, the CRISPR/Cas12a-based competitive aptasensor offers a simple and sensitive platform for OTA detection, and it holds great promise for food security monitoring.

Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system.

Centrifugal compressors are widely used in the oil and natural gas industry for gas compression, reinjection, and transportation. Fault diagnosis and identification of centrifugal compressors are crucial. To promptly monitor abnormal changes in compressor data and trace the causes leading to these data anomalies, this paper proposes a security monitoring and root cause tracing method for compressor data anomalies. Additionally, it presents an intelligent system design method for fault tracing in compressors and localization of faults from different sources. This method starts from petrochemical big data and consists of three parts: fault dynamic knowledge graph construction, instrument data sliding fault-tolerant filtering, and the fusion and reasoning of fault dynamic knowledge graph and instrument data variation monitoring. The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)-SPE (Square Prediction Error)-CNN (Convolutional Neural Network) classifier. Combined with dynamic knowledge graph and NLP (Natural Language Processing) inference, it achieves good diagnostic results.

Remote Data Security Monitoring Technology for Computer Networks Based on Machine Learning Algorithms

In an era dominated by increasing cyber threats and the expansion of digital systems, securing computer networks has become a critical challenge. Traditional security techniques, such as firewalls and intrusion detection systems, are often inadequate in addressing the dynamic and evolving nature of modern cyber-attacks. Remote data security monitoring, when combined with machine learning (ML) algorithms, has emerged as a promising solution to address these security gaps. This paper explores the integration of machine learning techniques into remote data security monitoring systems for computer networks. It investigates how ML algorithms can enhance real-time detection, classification, and prediction of security incidents, thereby improving the robustness and efficiency of network defense mechanisms. The study reviews various ML models, their application to data security, and the benefits and challenges of their deployment in remote monitoring systems. Finally, we present case studies and discuss future research directions in this domain.

INFORMATION SECURITY THREATS FOR INTEGRATION SYSTEMS

This article considers threats to integration systems, as well as methods of prevention. Threat modeling involves identifying potential threats to a system, assessing the likelihood and impact of each threat, and developing strategies to prevent or mitigate those threats. For integration systems, there are many potential information security threats, including unauthorized access, malware, insider threats, DoS attacks, SQL injection, and data leakage. To ensure the security and integrity of enterprise integration systems, it is crucial for organizations to understand these threats and implement appropriate security controls and monitoring. In addition, it shows the role of regular security assessments, employee training, and the integration of advanced threat detection technologies to actively address emerging threats and enhance overall system resilience. This article highlights the importance of information security threat modeling for integration systems and provides an overview of some of the key threats that organizations should be aware of when implementing and managing these systems. Keywords: threats, integration threats, sql injection, Biztalk, DOS attacks.

Issues of collecting information about security events from host information security tools as part of security monitoring

Issues of collecting information about security events from host information security tools as part of security monitoring

Assessment оf the level оf energy security оf regions in emergency situations in the gas industry using the example оf the Northwestern Federal District

The article presents an assessment of the energy security level using the example of the subjects of the Russian Federation located in the Northwestern Federal District. The approach to the assessment is based on the use of the apparatus for monitoring and indicative analysis of energy security (ES) developed at the ISEM SB RAS. An assessment of the ES level in the regions is carried out based on modeling the situation with the cessation of operation of the most important gas industry facilities, and an analysis of the dynamics, scales and trends in the change in the state of the ES level is carried out when modeling the situation using peak UGS facilities provided for by the General Scheme for the Development of the Gas Industry for the Period up to 2030 for the analyzed subjects of the Russian Federation.

Оценка воздействия стока некоторых рек Сочинского Причерноморья на прибрежную морскую среду в зонах их плюмов

The paper assesses the impact of water quality of some rivers located within the Black Sea coastal zone in the Sochi area on the ecological security and status of the Black Sea coastal line. The relevance of the study is determined by the lack of a unified methodological approach to establishing parameters for assessing the ecological condition of water bodies within the Black Sea coastal area. The study is the first to consider a unified river-sea system in the context of integrated monitoring of natural water quality. The research is based on biological methods (biotesting, bioindication) to evaluate the state of biological communities and processes in combination with the analysis of geological aspects. This allows a detailed study of the influence of various factors on the marine environment ecological safety. It was revealed that the marine environment has the highest levels of toxicity in the inflow zones of small rivers, which are subject to significant complex impacts from municipal solid waste landfills and industrial enterprises located in their catchment areas. In the marine plumes of rivers classified as medium, which are important for water management, recreation and fisheries in the region, seawater has a lower toxic effect as a result of significant dilution. Thus, small rivers inflows can cause similar negative impacts on the quality of the marine environment and the status of coastal biocenoses as runoff from larger watercourses. The study results can contribute to the development of new approaches for the assessment and monitoring of the environmental security of the coastal marine environment, based on the concept of the ecological system integrity in the context of interdependencies associated with the pollutants and nutrients transfer between qualitatively different environments and associated with them river and cjastal marine hydrobiocenoses.

Secure Healthcare Monitoring and Attack Detection Framework using ELUS-BILSTM and STECAES

The patterns of providing health-centric services have transformed extremely with the enhancement along with innovations in mobile and wireless communication technologies subsuming the Internet of Things (IoT). Due to the rapidly increasing attack, the doctors were not provided with an accurate alerting mechanism by the prevailing health monitoring system. Thus, by utilizing the Exponential Linear activation Units-centred Bidirectional Long Short Term Memory (ELUS-BiLSTM) technique, a novel healthcare monitoring along with an attack detection system is proposed in this work. Attack detection, Data security, and Patient health monitoring are the three primary phases incorporated in the proposed methodology. Initially, from the patient, the data are collected, and then the features are extracted in the attack detection phase. Next, the features being extracted are inputted to the ELUS-BiLSTM classifier where the data is classified as attacked or non-attacked data. After that, by utilizing Skew Tent Elliptic Curve Advanced Encryption Standard (STECAES), the non-attacked data is encrypted whereas the attacked data is stored in the log file. Lastly, to generate the fuzzy rules, the encrypted data is utilized; subsequently, the alert message is sent to the doctor. The experiential outcomes displayed that when analogized with the prevailing methodologies, the proposed model obtained better outcomes.

Mechanical Design and Testing of Mobile Monitoring Units for Security Systems

Mobile monitoring systems are currently used in many applications related to environmental applications or the monitoring of health status. However, security monitoring systems are usually chosen for a specific object, area or perimeter. The main goal of our article is to present the mechanical design of mobile monitoring units. These units create the basis of a developed mobile monitoring security system, which can be applied to monitor any area of interest, even in demanding weather conditions, involving, for example, windiness or wide operational temperature ranges. Therefore, this article is focused on the mechanical design of mobile monitoring units, which are constructed not only so that they can withstand challenging environmental conditions, but also with regard to their simple transportation, manufacturing process and, if necessary, possible repairs. During the design, emphasis was also placed on the vibrations of the mobile monitoring units and their temperature dependence, because vibrations can significantly affect the correct functioning of the mobile monitoring security system and cause false alarm situations. To confirm the correctness of the simulation models, experiments were performed on the mobile monitoring unit prototypes.

Short-term stock indices as a tool for assessing and forecasting scientific and technological security

Objectives. Ensuring scientific and technological sovereignty, defined as one of the objectives of the Concept of Technological Development for the period until 2030, approved by the Order of the Government of the Russian Federation No. 1315-r dated May 20, 2023, implies the use of effective mechanisms for managing the country’s economy. Under contemporary conditions, technology is a crucial element in the country’s economic development and a key component of scientific and technological security. Thus, the aim of the present work is to develop existing methods for analyzing economically significant information relating to the monitoring and diagnostics of scientific and technological security at the meso- and macrolevels.Methods. The developed method for scientific and technological security level monitoring and forecasting, which is based on the dynamics analysis of the values of corporate securities, includes economic and statistical analysis methods, machine learning and time series analysis tools.Results. The approach towards information processing and enhancement of analysis mechanisms in managerial decisions, which relies on diagnostic, analysis, and forecasting tools for socioeconomic system scientific and technological security, is based on information-processing, economic, and mathematical methods. The presented results constitute a developed method for the analysis of systemically important enterprises stock indices, supporting research and development to assess and predict scientific and technological security dynamics at meso- and macro levels of the economy. In order to verify the methodology, a numerical experiment was carried out using statistical data from systemically important companies.Conclusions. The developed methodology is aimed at improved managerial decision-making accuracy and speed when solving problems connected with scientific and technological security underpinning socioeconomic systems. The experimental results quantitatively confirm the significant contribution made by systemically important companies to Russian Federation’s scientific and technological security ensuring.