Security Infrastructure Research Articles

Вопросы квалификации неправомерного доступа к объектам критической информационной инфраструктуры

The article provides a criminal legal analysis of the crime provided for in part. 2.4 tbsp. 2741 of the Criminal Code of the Russian Federation — unlawful access to objects of critical information infrastructure of the Russian Federation. The authors conducted a comparative study of the objects regulated by the federal law no. 187-FZ of July 26, 2017 “On the security of critical information infrastructure of the Russian Federation” and part. 2.4 tbsp. 2741 of the Criminal Code of the Russian Federation, a legal assessment is given to criminal actions expressed in unlawful access using someone else’s account in the absence of such rights to it. The issues of qualification of crimes in the field of computer information are considered using examples of forensic investigative practice.

A methodology to assess the maturity level of the digital ecosystem and its testing on the IT parks case

Subject. The article addresses a technopark as a form of a digital ecosystem that has a certain structure and is characterized by the features of structural components and their interrelationships. Objectives. The study aims to develop a methodology for assessing the maturity level of digital ecosystem, which enables to determine the stage of ecosystem's life cycle and the level of its development. Methods. The assessment rests on the developed multifactorial methodology. It includes three interrelated complex parameters (resource and infrastructure security, the level of development of central relational mechanisms, and ecosystem productivity). The methodology is based on methods of scoring and identifying correlation between indicators characterizing the maturity level of each component of the ecosystem. Results. From a theoretical perspective, we developed an assessment methodology. It is unique in its multifactorial and structural approach to assessing the maturity of ecosystems. From a practical point of view, we built ratings of IT parks, which clearly reflect the stage of ecosystem development, factors ensuring the growth of productivity, and socio-economic effect from ecosystem mechanisms of the IT park. The study identifies the best ecosystems of IT parks and ecosystems that need to be developed and supported financially and institutionally. Conclusions. The assessment methodology is applicable for monitoring and evaluating the stage of development of IT parks as digital regional ecosystems.

Analysis of Reinforced Concrete Structure Subjected to Blast Load

The research focuses on a specific standoff distance of 15m & 20m , a critical parameter in blast analysis, to gain a understanding of the building's behavior under static loading conditions of blast load weight of 150kgs & 100kgs of TNT. To simulate realistic scenarios, the models are subjected to appropriate boundary conditions, including foundation constraints to account for the building's interaction with the ground. The blast load is assigned as joint loads, considering the standoff distance of the blast from the source. The standoff distance significantly influences the building's response to the blast, with profound implications for structural integrity and security. The Analysis of the Bare frame with blast load added as Joint Load, the deflection is controlled by adding Shear walls along various location along the periphery & core for 10 Story RCC Structures. The analysis is conducted using linear time history analysis (Response Spectrum Analysis) within ETABS, capturing the response of the building. Roof Displacement, Base Shear, Story Drift, Stiffness, Time period & Modal participation mass ratios are monitored during the analysis. The outcomes of this study contribute valuable insights into the structural responses of buildings to blast loads at specific standoff distances. This research advances our understanding of blast effects on buildings thereby enhancing the security and resilience of vital infrastructure in the face of evolving threats. Keywords: Blast Load, Response Spectrum Method, Roof Displacement, Base Shear, Story Drift, Stiffness, Time period & Modal participation mass ratios.

Методика оценки уровня готовности розничной торговли в России к процессу цифровой трансформации

Chain of events 2019–2022 (COVID-19, sanctions, rising inflation, import substitution policy and economic situation in the market) have had a significant impact on consumer behavior. Many consumers have started to shop online more often than offline stores. Consumer preferences and active adoption of digital technologies by market leaders set the vector of overall digitalization and require companies in the industry to adapt in three areas: customer service, business processes and business models. In order to develop a personalized digital transformation strategy that takes into account a company's strengths and weaknesses, it is necessary to assess the level of digital maturity. The study analyzed the most well-known methodologies for assessing digital transformation, which revealed a number of short-comings, such as the lack of assessment of investment potential and the current level of implementation of digital tools, as well as insufficient attention to the security of digital infrastructure. The authors have developed a new system for assessing the level of digital transformation. The proposed criteria can be interpreted to make further strategic, managerial decisions regarding the implementation of digital technologies at the enterprises of the industry. Taking into account the results obtained, it is necessary to assess the level of digital maturity taking into account the prioritization of tasks and the dynamic nature of digital transformation. This will allow choosing the most relevant strategy on the data-driven retailing matrix.

The Role of API Security in Modern Enterprise Platforms

This article explores the critical role of API security in modern enterprise platforms, addressing the growing importance of protecting Application Programming Interfaces (APIs) as they become increasingly central to digital ecosystems. We examine the fundamental challenges organizations face in securing their APIs, including authentication, data encryption, rate limiting, and version management. The paper presents a comprehensive overview of best practices for API security, such as implementing OAuth and token-based authentication, utilizing Transport Layer Security (TLS), deploying API gateways, and conducting regular audits. We discuss implementation strategies that integrate security throughout the API development lifecycle and highlight the importance of developer training. Through case studies, we illustrate both successful API security implementations and lessons learned from security breaches. The article also looks ahead to future trends in API security, considering the impact of emerging technologies like AI and quantum computing, as well as the evolving threat landscape. By providing a thorough analysis of current practices and future directions, this paper aims to equip organizations with the knowledge needed to develop robust, secure, and scalable API infrastructures that can support innovation while protecting critical data and resources in an increasingly interconnected digital world.

A Comprehensive Comparative Analysis of Deep Learning Architectures for Suspicious Activity Detection in Video Surveillance

Abstract: In the landscape of modern security infrastructure, video surveillance has evolved into a ubiquitous and indispensable tool for ensuring public safety and safeguarding critical assets. This research paper delves into an extensive examination of various deep learning architectures for the purpose of detecting suspicious activities in video surveillance. The investigation encompasses Convolutional Long Short-Term Memory (ConvLSTM), Convolutional Neural Network (CNN) combined with Long Short-Term Memory (LSTM), ConvLSTM, Bidirectional Long Short-Term Memory (BiLSTM), and diverse combinations thereof. Each model is rigorously trained and tested on a carefully curated dataset designed to encapsulate a spectrum of normal and suspicious activities like shooting and fighting. The study aspires to identify the most efficacious model for enhancing the accuracy of suspicious activity detection in complex and dynamic environments. Metrics such as accuracy, precision, recall, and F1 score will be rigorously assessed to ascertain the model’s comparative performances

Configuración de una red LAN utilizando Python scripting para generar redes definidas por software (SDN).

Network management is an essential task in modern organizations, where the complexity of technological infrastructures demands efficient and accurate solutions. With network devices from leading manufacturers such as Cisco and Mikrotik. In response to these challenges, automating the configuration of network devices has gained popularity as an approach to improve efficiency and reduce errors. Python, a programming language widely adopted for its versatility and ease of use, has become a key tool for this automation. Within this ecosystem, Netmiko emerges as a specialized library that facilitates automated interaction with network devices using SSH. Netmiko allows network administrators to write scripts that can connect to Cisco and Mikrotik devices, execute configuration commands, and retrieve information about the network status. This approach not only speeds up the configuration process, but also ensures consistency and accuracy in deploying configurations, especially in multi-device environments. As networks continue to grow in complexity, the adoption of tools such as Netmiko becomes essential to maintaining the operability and security of network infrastructures.

An expert system of recommendations for combating cyber threats using CVSS metrics and game theory

This study is focused on the creation of an expert system for generating recommendations on cyber security. The developed expert system uses a game-theoretic model as a inference engine to transform expert knowledge into recommendations for end-users, who may be chief IT security officers (CISOs), system administrators, or cyber security engineers. Expert knowledge is presented in the form of an estimate of the base group of CVSS metrics - Common Vulnerability Score System, for each type of attack and adjusted values of CVSS in the case that the counterattack strategy is applied. Given a set of attacks and a base of expert attack knowledge, the system generates a game matrix of zero-sum game with a cybercriminal and a cyberdefense expert as players. The inference engine of the expert system is a game-theoretic model responsible for solving the game using the Brown-Robinson iterative method and generating cyber protection recommendations. An experiment was conducted on the convergence of the BrownRobinson algorithm on the 2022 vulnerability dataset from the Cybersecurity and Infrastructure Security Agency database, as a result of which it was determined that the convergence of the algorithm for solving the matrix game is achieved at a number of iterations of 1000. As a result of the work, expert system was designed and implemented along with the Web interface, which provides input by experts of CVSS level assessments of collected threats, threats countermeasures and output of recommendations for combating cyber threats

Development of Secure Infrastructure for Advancing Generative AI Research in Healthcare at an Academic Medical Center.

The increasing interest in leveraging generative AI models in healthcare necessitates secure infrastructure at academic medical centers. Without an all-encompassing secure system, researchers may create their own insecure microprocesses, risking the exposure of protected health information (PHI) to the public internet or its inadvertent incorporation into AI model training. To address these challenges, our institution implemented a secure pathway to the Azure OpenAI Service using our own private OpenAI instance which we fully control to facilitate high-throughput, secure LLM queries. This pathway ensures data privacy while allowing researchers to harness the capabilities of LLMs for diverse healthcare applications. Our approach supports compliant, efficient, and innovative AI research in healthcare. This paper discusses the implementation, advantages, and use cases of this secure infrastructure, underscoring the critical need for centralized, secure AI solutions in academic medical environments.

Quantum-Secured Data Centre Interconnect in a field environment

In the evolving landscape of quantum technology, the increasing prominence of quantum computing poses a significant threat to the security of conventional public key infrastructure. Quantum key distribution (QKD), an established quantum technology at a high readiness level, emerges as a viable solution with commercial adoption potential. QKD facilitates the establishment of secure symmetric random bit strings between two geographically separated, trustworthy entities, safeguarding communications from potential eavesdropping. In particular, data centre interconnects can leverage the potential of QKD devices to ensure the secure transmission of critical and sensitive information in preserving the confidentiality, security, and integrity of their stored data. In this article, we present the successful implementation of a QKD field trial within a commercial data centre environment that utilises the existing fibre network infrastructure. The achieved average secret key rate of 2.392 kbps and an average quantum bit error rate of less than 2% demonstrate the commercial feasibility of QKD in real-world scenarios. As a use case study, we demonstrate the secure transfer of files between two data centres through the Quantum-Secured Virtual Private Network, utilising secret keys generated by the QKD devices. %This demonstration marks a significant milestone in the deployment of QKD across Singapore, establishing a foundation for widespread adoption and enhancing the infrastructure for practical, quantum-safe communication in commercial environments.

Analisis Efektivitas Sistem Kendali Otomatis PJU Berbasis IoT Menggunakan Mikrokontroler ESP32 dengan Metode Regresi Linier

Public street lighting (PJU) is essential infrastructure for nighttime security. This research develops an automatic PJU control system based on IoT using light and motion sensors with an ESP32 microcontroller. The system allows for PJU lamp control via a website, providing high flexibility for users. The ESP32 microcontroller is connected to a PIR sensor to detect motion. When the sensor detects movement, the intensity of the PJU lamps is increased, and when no movement is detected, the light intensity is reduced to save energy. Users can control the PJU lamps from any internet-connected device. Testing results show an increase in energy efficiency, with an average power consumption reduction of 13.77 watts and an efficiency increase of 42.67%. An R-squared value of 0.629 indicates that the model is quite good at explaining the variability in power consumption data. This system can automatically turn on street lights and be monitored in real-time, And hopes to contribute to the development of smarter and more efficient street lighting systems.

Взгляды военно-политического руководства США на защиту критически важных объектов от угроз кибернетического воздействия

The study examines the potential threats of cybernetic impact on critical U.S. facilities that support the activities of the armed forces, and the measures of the American administration to identify and neutralize them. Based on the results of the analysis, it was concluded that the destructive impact on critical infrastructure undermines the military potential of the state and the combat capability of the armed forces. The multidimensional and cross-border nature of threats objectively necessitates the search for ways to prevent and eliminate such threats, as well as the development of interdepartmental and international cooperation in combating cyber threats. This requirement places a burden on state and military leaders to possess knowledge not only in the realm of military security but also in the field of national cybersecurity. Significant cross-industry risks are associated with energy infrastructure, particularly with the supply of gas to electricity producers. The most vulnerable parts of infrastructure are the energy systems that are utilized to supply electricity to military bases and other U.S. military facilities located abroad. A crucial aspect of ensuring the cyber security of energy infrastructure is the regulation of the supply chain for information technology products. Awareness of vulnerabilities within the cybersecurity framework of the U.S. defense infrastructure can be leveraged to refine the list of critical targets of a potential adversary and inform the planning and execution of information operations by the General Staff of the Armed Forces of the Russian Federation.

Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling—An Oil and Gas Sector Case Study

As security breaches are increasingly widely reported in today’s culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the “big picture” of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems.

Saudi Digital Library Services in the Light of the National and International Sustainable Development Goals

The study aimed to identify the concept of sustainable development, and to explore the role of digital libraries in achieving SDGs, by shedding light on the reality of the SDL in terms of financial, technical and human capabilities to achieve the Kingdom’s 2030 vision for sustainable development. The study revealed that digital libraries play an active role in achieving SDGs through secure and diverse technical infrastructure, and a variety of digital information sources. Digital libraries enhance the level of information awareness among users. Among digital libraries worldwide, SDL is distinguished in terms of knowledge resources acquired, its platforms, and through the large number of beneficiaries of its services, representing all segments of the academic community in Saudi Arabia's higher education institutions. Based on a solid network, and Saudi Arabia vision 2030, SDL succeeded to link higher education institutions through a huge amount of information sources, and facilitated their access, and provided equal opportunities for all. The study recommended that in order to be the national digital platform and effective development partner, SDL must expand the scope of services to include trade and industrial private and public sectors, and raise the level of research awareness and ethics among Arab scholars and researchers.

Improved ECC cryptosystem-based lightweight authentication for SDN-based edge computing in IoT-enabled healthcare systems

The Internet of Things (IoT) consists of smart devices with limited resources that can identify and analyze data. In IoT-enabled healthcare systems, the security of IoT devices and the data they contain is complex. These devices in the healthcare industry, edge computing can provide low-latency information services at a reasonable cost. This work proposes a security infrastructure for Software Defined Network (SDN)-based edge computing in IoT-enabled healthcare systems consisting of three steps: Lightweight authentication, collaborative edge computing and job migration. The lightweight authentication step involves both Improved Lightweight Key Management (ILKM) and Improved Elliptic Curve Cryptography (IECC) schemes to ensure authentication among the devices and edge servers. Moreover, the patient’s data in IoT devices are scheduled to the appropriate edge server by examining the load balancing in the collaborative edge computing phase. This is done optimally using the adopted hybrid optimization model, Osprey Assisted Coati Optimization Algorithm (OACOA). Further, job migration takes place, in which the data is allocated to the edge server by comparing the capacity of edge servers and the data gets migrated to other servers by considering migration cost when the capacity of the edge server is overloaded. Finally, the efficiency of the suggested OACOA scheme is evaluated over traditional models with regard to several metrics. When considering the edge-server 30, the OACOA scheme achieves a makespan of 385, while conventional methods acquired fewer makespan ratings. Also, the OACOA approach obtained the highest security ratings (0.7143) on edge-server 20 when compared to existing schemes.

Enhancing Inspection Tasks: A Dataset for Corrosion Defects in Pipelines

Inspection plays a crucial role in ensuring the longevity, security, and dependability of critical public infrastructure for both governments and businesses. However, traditional inspection processes are often labor-intensive and pose various risks. Consequently, there is a growing need for automation in such tasks. This research paper presents a comprehensive dataset that can be utilized to develop algorithms and systems for automating the inspection process, a critical area in the field of computer vision. The dataset encompasses a diverse range of inspection scenarios and serves as a valuable resource for advancing automation technology specifically for the inspection of steel pipes to detect corrosion defects. Real-life pipe maps have been used to derive scenarios that represent varying levels of corrosion. By leveraging this dataset, researchers and practitioners can contribute to the development of more efficient and accurate automated inspection systems, thus greatly improving the overall efficiency and long-term safety of infrastructure inspection.

How to Make My Bug Bounty Cost-Effective? A Game-Theoretical Model

A bug bounty program (BBP) is an innovative crowdsourcing security solution increasingly adopted by organizations. We use a game-theoretical model to analyze how key characteristics impact BBPs and offer practical insights into managing a BBP as part of an organization’s vulnerability management for better cost-effectiveness. Our findings indicate that organizations with high patching complexity should announce lower bounties, especially if they face limited security resources. BBPs should complement, not substitute, an organization’s security characteristics. Evaluating patching complexity and security posture is crucial when designing a BBP. Furthermore, security researchers drive BBP performance. Higher productivity in researchers doesn’t always require higher bounties even with high postdiscovery costs. Novice productivity can increase total costs if unit postdiscovery costs are high, whereas expert productivity consistently reduces costs. Organizations should disclose high-level product and information technology (IT) features to increase expert productivity. The number of security researchers in a BBP is important, but increasing their numbers doesn’t always necessitate higher bounties. A larger crowd may not always be cost-effective. Lastly, enhanced legal protection for security researchers might not increase organizational risks, especially in organizations with robust security or less sophisticated IT infrastructure. Industrial associations and policymakers should consider these factors in standards and legal frameworks.

Snort Versus Suricata in Intrusion Detection

In the contemporary digital age, the increasing complexity and frequency of cyber threats underscore the need for efficient network intrusion detection systems (NIDS). This paper provides a comprehensive comparative analysis of two prominent NIDS, Snort and Suricata, focusing on their architecture, detection capabilities, and performance metrics. It explores the historical development, operational frameworks, and technological foundations of these systems, highlighting their respective benefits and limitations in different network environments. Snort, known for its extensive rule-based detection, and Suricata, which leverages multi-threading for high-speed traffic handling, are evaluated based on specific security requirements, including traffic volumes, processing speeds, and threat types. The paper also discusses future advancements in NIDS, particularly through the integration of machine learning and AI, to enhance predictive and adaptive capabilities. This analysis aims to inform cybersecurity professionals about the qualifications and capabilities of Snort and Suricata, providing insights for their effective deployment in modern network security infrastructures. The discussion on future trends emphasizes the importance of continuous improvement in NIDS to address evolving cyber threats)

Real-Time Surveillance with AI: A Comprehensive Approach to Security and Monitoring

The last ten years have been quite phenomenal for artificial intelligence and computer vision. For the past few years, it was unstoppable, with outstanding breakthroughs that moved towards advanced real-time surveillance systems-that wholesome approach to real-time surveillance through the power of AI in technological advancement for accuracy, scalability, and responsiveness. The system integrates facial recognition, object detection, and anomaly detection algorithms to give a very stable, adaptive response to the various facets of surveillance needs, such as public safety, institutional monitoring, and private security. Through deep learning models extracted from comprehensive datasets, the system is able to identify known individuals in real time and trace the movements and unusual behaviors of these people. A discussion of challenges in data privacy, computational efficiency, and false positives goes hand in hand with an in-depth analysis of system architecture, which includes thorough hardware and software configuration descriptions. The experimental results show the potential of the system in real-world environments as an alternate scalable solution for future security infrastructures.

Analyzing AWS Edge Computing Solutions to Enhance IoT Deployments

This paper explores integrating Internet of Things (IoT) deployments with edge computing, focusing on Amazon Web Services (AWS) as a key facilitator. It provides an analysis of AWS IoT services and their integration with edge computing technologies, addressing challenges, and practical applications across industries, and outlining future research directions. IoT and edge computing revolutionize data processing by enabling real-time analytics, reduced latency, and enhanced operational efficiency. IoT involves interconnected devices autonomously gathering and exchanging data, while edge computing processes data near its source, decentralizing data processing and minimizing data transmission to centralized servers. AWS facilitates scalable and secure infrastructures for IoT and edge computing. AWS IoT Core manages IoT device connectivity and data ingestion, AWS Greengrass extends AWS capabilities to edge devices, and AWS Lambda enables serverless computing, empowering efficient deployment and scaling of IoT applications. Centralized cloud architectures often struggle with vast IoT data. Edge computing decentralizes data processing, reducing latency, enhancing real-time capabilities, and minimizing bandwidth. AWS ensures secure device connectivity through AWS IoT Core, supporting various protocols for seamless integration with IoT devices. AWS Greengrass allows local data processing and machine learning at the edge, vital for environments with limited connectivity or stringent latency requirements. AWS Lambda supports serverless computing, enabling scalable, event-driven architectures without server management, crucial for fluctuating IoT workloads. In conclusion, AWS advances IoT capabilities at the edge, with practical implementations across industries. As IoT evolves, AWS remains pivotal, innovating to meet dynamic IoT deployment demands.