Secure Method Research Articles

Research on the integrated failure analysis method of safety and security of fieldbus transmitters

Fieldbus transmitters are commonly used in modern industrial productions, particularly in Safety Instrumented Systems (SIS). Safety and security are critical considerations in the design and operation of these transmitters. Previous research has tended to address safety issues and security issues separately, but with the increasing complexity of network technology, it is important to analyze them simultaneously. In this paper, a systematic framework for comprehensively analyzing random failures and cyber-attack failures is proposed. The framework adopts the FMEA-IMEA method, which combines Failure Modes and Effects Analysis (FMEA) and Intrusion Modes and Effects Analysis (IMEA), to analyze failure modes and effects of fieldbus transmitters. In addition, by extending Reliability Block Diagrams (RBD), the impact of random failures and cyber-attack failures on fieldbus transmitters is quantitatively determined. At the same time, calculation approach of the residual error rate (RER), Component counting method, and Monte Carlo are used to determine random failure rate and cyber-attack failure rate. Using fieldbus pressure transmitter and fieldbus temperature transmitter as examples, the results demonstrate that security issues can significantly impact the safety integrity level. In fact, the safety integrity level is reduced from SIL3 to SIL1 when cyber-attacks are considered. Compared to existing FMEA, the proposed approach offers a more comprehensive analysis of random failures and cyber-attack failures in fieldbus transmitters.

Mapping the future of public security, justice and the law enforcement

This article presents a thorough analysis of the evolution and future prospects in the field of public security, employing both bibliometric and prospective methods. The bibliometric analysis examines the historical development of research up to 2023, identifying patterns, main areas of study, and influential authors. This deep understanding of the past lays the groundwork for prospective analysis, which uses foresight methods to predict possible scenarios and emerging trends in public security. By combining these approaches, the study offers a comprehensive view of the past and future dynamics of the field. The results of the bibliometric analysis provide a solid understanding of the foundations upon which future research can be built. Through prospective analysis, the article explores potential future directions, critical challenges, and opportunities in the realm of public security.

An Intelligent Security System Using Enhanced Anomaly-Based Detection Scheme

Abstract Ensuring the security of computer networks is of utmost importance, and intrusion detection plays a vital role in safeguarding these systems. Traditional intrusion detection systems (IDSs) often suffer from drawbacks like reliance on outdated rules and centralized architectures, limiting their performance in the face of evolving threats and large-scale data networks. To address these challenges, we present an advanced anomaly detection-based IDS that utilizes a decentralized communicative multi-agent reinforcement learning (MARL). In our approach, multiple reinforcement learning agents collaborate in intrusion detection, effectively mitigating the non-stationarity problem and introducing a specialized secure communication method. We further enhance the learning process by incorporating external knowledge. Our approach is evaluated through extensive experiments conducted on the benchmark NSL Knowledge Discovery and Data Mining dataset. These experiments encompass diverse scenarios, involving varying numbers of agents to prove scalability feature. The results underscore the effectiveness of our method, which surpasses the performance of existing state-of-the-art solutions based on MARL, achieving a high accuracy rate of 97.80%.

Comparative analysis of identity management, access control, and authorization practices in public and private universities

Background This research delves into the critical aspects of identity management, access control, and authorization practices within the domains of public and private universities. Identity management involves the meticulous management and control of user identities, encompassing the establishment and maintenance of user profiles, role assignments, and access privileges. Access control is the practice of defining and enforcing policies that govern who can access an IT system or application and which resources they can interact with. Authorization, meanwhile, determines the specific actions and privileges granted to users based on their roles and permissions. Methods To understand the variances in identity management and access control approaches, we conducted a comparative analysis between public and private universities. Our investigation scrutinized the user populations with access to university systems, the enforcement of access limitations, authentication methods, and password policies. Additionally, we examined the nuances of authorization processes, levels of authorization, access approval authorities, user status and role changes, unique user account management, account deletion procedures, user authentication methods, password complexity and expiration policies, password storage methods, and session termination policies. Results This study revealed that both public and private universities prioritize these security measures, with a common categorization of these processes. Nevertheless, there exist disparities, such as the inclusion of contractors and vendors in the user population at private universities, the manual deletion of user accounts in private institutions, and variations in password policies and storage methods. Private universities tend to enforce stricter password policies, employ more secure password storage methods, and implement automatic session termination features. Conclusions This research provides valuable insights into the practices and approaches adopted by public and private universities to safeguard their digital environments. The findings serve as a valuable resource for enhancing identity management, access control, and authorization protocols, enabling institutions to fortify their cybersecurity defenses in an ever-evolving threat landscape.

Simple and secure thrombectomy without circulatory arrest for acute pulmonary embolism

BackgroundSurgical pulmonary artery thrombectomy is a well-established emergency treatment for massive pulmonary embolism (PE) in which fibrinolysis or thrombolysis are not effective. However, surgery for massive PE that requires peripheral pulmonary artery thrombus removal remains challenging. We established a simple and secure pulmonary artery thrombectomy method using cardiopulmonary bypass and cardiac arrest. In this procedure, the surgical assistant arm, typically used for coronary artery bypass grafting, is used to obtain a feasible working space during thrombectomy.Case presentationWe present seven consecutive massive PE cases that were treated with the present surgical method and successfully weaned from cardiopulmonary bypass or extracorporeal membrane oxygenation postoperatively.ConclusionsThis procedure can be used to prevent right ventricular failure after surgery as surgeons can remove the peripheral thrombus with clear vision up to the second branch of the pulmonary artery.

A Novel Method for Hill Cipher Encryption and Decryption Using Gaussian Integers Implemented in Banking Systems

Cryptographic tools like Hill's encryption algorithm protect digital data. In this work, we present a novel Hill cipher security method that utilizes Gaussian integers from number theory. Using these intriguing mathematical entities to disguise plaintext values dramatically boosts assault resistance and duration. This research includes a three-pass protocol for encryption and decryption without key exchange, ensuring a safe, efficient, and dependable solution. Pandas is used for efficient data processing, and Numpy for computational tasks, notably matrices. Hill cipher-based encryption and decryption can be utilized in real life. It also demonstrates how to save Pandas DataFrame data to Excel. This strategy assures progress in cryptography. Uniquely designed for banking, it emphasizes its applicability and possible influence on present financial systems.

Enhancing Industrial IoT Network Security through Blockchain Integration

In the rapidly evolving landscape of industrial ecosystems, Industrial IoT networks face increasing security challenges. Traditional security methods often struggle to protect these networks adequately, posing risks to data integrity, confidentiality, and access control. Our research introduces a methodology that leverages blockchain technology to enhance the security and trustworthiness of IoT networks. This approach starts with sensor nodes collecting and compressing data, followed by encryption using the ChaCha20-Poly1305 algorithm and transmission to local aggregators. A crucial element of our system is the private blockchain gateway, which processes and classifies data based on confidentiality levels, determining their storage in cloud servers or the Interplanetary File System for enhanced security. The system’s integrity and authenticity are further reinforced through the proof of authority consensus mechanism. This system employs Zero Knowledge Proof challenges for device authorization, optimizing data retrieval while maintaining a delicate balance between security and accessibility. Our methodology contributes to mitigating vulnerabilities in Industrial IoT networks and is part of a broader effort to advance the security and operational efficiency of these systems. It reflects an understanding of the diverse and evolving challenges in IoT security, emphasizing the need for continuous innovation and adaptation in this dynamic field.

Self Care for Overcoming Sleep Disorders in Cancer Patients: Literature Review

Cancer can cause pain, weakness, sleep problems, and fatigue, and affect functional limitations that affect daily life. One of the most common and frequently complained symptoms in cancer patients is sleep disturbance. This paper aims to determine the effect of self-care to overcome sleep disorders in cancer patients. The author uses several databases, such as Scopus, SpringerLink, and Sage Journal. Clinical Key Nursing for use in the search for relevant articles. There are a total of 8 articles have been analyzed where several self-care interventions are used to treat sleep disorders in cancer patients, including mindfulness, exercise, yoga, dance, and personal psychotherapy. most of them showed results in a decrease in sleep disturbances in cancer patients, but there was one article that did not show significant results related to sleep disturbances. All the articles found were forms of intervention from self-care. There are three articles related to insomnia, one related to sleep quality, and four others related to sleep disturbance. Based on several journals that have been analyzed, it can be concluded that the provision of self-care interventions can affect the incidence of sleep disorders in cancer patients. A practical and secure method, exemplified by self-care practices, seeks to enhance sleep quality, general comfort, and the patient's overall quality of life.

A Survey on Secure Eye: Blink-Based Morse Authentication

Real-time password authentication methods are widely used by people, but they can be exploited or defeated with the help of a hot track or high-speed scan. By using blinding hand motions in place of visible fingerprints, pin verification offers a more secure method of password entry. The procedure of blinking the eyes while taking many picture frames and coming up with a PIN is known as blink-based authentication. This paper provides a range of real-time applications that combine instant-based PIN, facial detection, and One-time Passwords to eliminate shoulder-to-shoulder and scratch tracking

PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data.

Modern machine learning models toward various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by designing a decentralized differential private federated learning algorithm. We applied PPML-Omics to analyze data from three sequencing technologies and addressed the privacy concern in three major tasks of omic data under three representative deep learning models. We examined privacy breaches in depth through privacy attack experiments and demonstrated that PPML-Omics could protect patients' privacy. In each of these applications, PPML-Omics was able to outperform methods of comparison under the same level of privacy guarantee, demonstrating the versatility of the method in simultaneously balancing the privacy-preserving capability and utility in omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed method with robust and generalizable empirical performance in protecting patients' privacy in omic data.

Quality attributes and shelf-life of freshly cut beef coated with waste feather keratin-ginger starch composite enriched with avocado peel polyphenolic-rich extract

The utilization of coatings composed of bio-based materials in the processing and preservation of meat presents an environmentally conscious, secure, cost-effective, and superior method for prolonging the storage life of meat while also preserving its nutritional value. In this study, changes in physical, chemical, and microbiological characteristics of freshly cut beef coated with distilled water (control) and keratin-starch composites (K-S) functionalized with 0.0-, 0.2-, 0.6-, and 1.0-mL avocado peel polyphenolic-rich extract (APPPE) kept at 4°C for 12 days were evaluated periodically at 3-day interval using standard techniques. Keratin was extracted from waste feathers, while starch was obtained from ginger rhizomes. Following a 12-day storage period, beef coated with APPPE-enriched K-S composites exhibited a significant (p<0.05) improvement in shelf life by minimizing deteriorative changes in pH and color (as determined by metmyoglobin level) in addition to inhibiting oxidative changes in lipids (as determined by TBARS level) and proteins (protein carbonyl level) in comparison to control and K-S composite without APPPE. Furthermore, microbial growth was significantly (p<0.05) suppressed in meat coated with K-S composite functionalized with APE at 0.6 and 1.0 mL compared to the control. The study suggested that APPPE-enriched K-S composite could offer an eco-friendly and safe food preservation technique for fresh meat.

Mage transposon: a novel gene delivery system for mammalian cells.

Transposons, as non-viral integration vectors, provide a secure and efficient method for stable gene delivery. In this study, we have discovered Mage (MG), a novel member of the piggyBac(PB) family, which exhibits strong transposability in a variety of mammalian cells and primary T cells. The wild-type MG showed a weaker insertion preference for near genes, transcription start sites (TSS), CpG islands, and DNaseI hypersensitive sites in comparison to PB, approaching the random insertion pattern. Utilizing in silico virtual screening and feasible combinatorial mutagenesis in vitro, we effectively produced the hyperactive MG transposase (hyMagease). This variant boasts a transposition rate 60% greater than its native counterpart without significantly altering its insertion pattern. Furthermore, we applied the hyMagease to efficiently deliver chimeric antigen receptor (CAR) into T cells, leading to stable high-level expression and inducing significant anti-tumor effects both in vitro and in xenograft mice models. These findings provide a compelling tool for gene transfer research, emphasizing its potential and prospects in the domains of genetic engineering and gene therapy.

Structuring and organizing database security domain from big data perspective using meta-modeling approach

Database security is an area focused on safeguarding databases against harmful access. It involves ensuring data accuracy, blocking unauthorized entry, and preventing harmful code within the database. Although various security models and methods exist, they often don't comprehensively cover all aspects of database security. This leads to a diverse and unclear understanding of database security among experts. This study proposes a unified framework, the Database Security Meta-model (DBSM), which acts as a standard language in this field. The DBSM, comprising twelve main elements, is thoroughly vetted to align with security needs and offers guidelines for practitioners to create specific security solutions.

Event-driven based simulation method for security and stability control strategies of power system

Since the security and stability control strategies (SSCSs) play a pivotal role in maintaining system stability, devising the strategies accurately and reliably is of great significance. Currently, SSCSs are typically formulated according to the ‘offline simulation and online matching’ approach. However, existing commercial simulation software is difficult to simulate the dynamic triggering mechanisms and intricate control logic of SSCSs. To simulate the set of SSCSs systematically and comprehensively, this paper has proposed an event-driven approach for simulations. Firstly, the SSCS is modeled as an event-driven model containing three elements: event, event listening, and event processing. Then, on the basis of the event triggering mechanism presented in this paper, the complex control logics of SSCSs, such as the prioritization of tripping generators, are implemented. Through event-driven models of SSCSs and subsequent utilization of the event-triggered mechanism, independent and dynamic responses of SSCSs can be realized, thus avoiding interference in the simulation of multiple SSCSs. Finally, the effectiveness of the proposed method is verified based on the IEEE 39-bus system.

A lightweight and robust authentication scheme for the healthcare system using public cloud server.

Cloud computing is vital in various applications, such as healthcare, transportation, governance, and mobile computing. When using a public cloud server, it is mandatory to be secured from all known threats because a minor attacker's disturbance severely threatens the whole system. A public cloud server is posed with numerous threats; an adversary can easily enter the server to access sensitive information, especially for the healthcare industry, which offers services to patients, researchers, labs, and hospitals in a flexible way with minimal operational costs. It is challenging to make it a reliable system and ensure the privacy and security of a cloud-enabled healthcare system. In this regard, numerous security mechanisms have been proposed in past decades. These protocols either suffer from replay attacks, are completed in three to four round trips or have maximum computation, which means the security doesn't balance with performance. Thus, this work uses a fuzzy extractor method to propose a robust security method for a cloud-enabled healthcare system based on Elliptic Curve Cryptography (ECC). The proposed scheme's security analysis has been examined formally with BAN logic, ROM and ProVerif and informally using pragmatic illustration and different attacks' discussions. The proposed security mechanism is analyzed in terms of communication and computation costs. Upon comparing the proposed protocol with prior work, it has been demonstrated that our scheme is 33.91% better in communication costs and 35.39% superior to its competitors in computation costs.

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

The cybersecurity concerns get increasingly intricate as the digital world progresses. In light of the increasing complexity of cyber threats, it is imperative to develop and implement advanced and flexible security strategies. Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods. This paper examines the significance of ML in the field of cybersecurity, with a special emphasis on the identification of threats and the implementation of protective measures. By incorporating ML algorithms into cybersecurity frameworks, organisations may automate decision-making processes, facilitating prompt responses to ever-changing threats. The initial segment explores the terrain of cyber threats, highlighting the necessity for dynamic and aggressive security methods. Conventional solutions that rely on signatures are frequently inadequate when it comes to handling sophisticated, shape-shifting attacks. ML algorithms, in contrast, have exceptional proficiency in identifying nuanced patterns and irregularities within extensive datasets, therefore offering a more efficient method of detecting potential threats. The second section delves into several ML methodologies utilised in cybersecurity, including supervised and unsupervised learning, deep learning, and reinforcement learning. Every approach is assessed based on its suitability for threat detection, demonstrating its advantages and constraints. Furthermore, the relevance of feature engineering and data pretreatment in improving machine learning models for cybersecurity applications. The versatility of ML algorithms allows them to grow with emerging threats, making them a useful tool in the ever-changing arena of cyber warfare. The final segment focuses on real-world applications of machine learning in cybersecurity, presenting successful use cases across sectors. From anomaly detection to behavior analysis, ML algorithms contribute to the discovery of dangerous activity, lowering false positives and strengthening the overall security posture. Lastly, the paper covers the obstacles and ethical issues related to the adoption of ML in cybersecurity. Issues like as adversarial assaults, skewed datasets, and the interpretability of ML models are examined, highlighting the necessity for a holistic strategy that integrates modern technology with ethical considerations. The fusion of human expertise and machine intelligence offers a formidable defense against evolving cyber threats, paving the way for a more resilient and secure digital future.

Enhancing Cybersecurity in Distributed Microgrids: A Review of Communication Protocols and Standards

The effective operation of distributed energy sources relies significantly on the communication systems employed in microgrids. This article explores the fundamental communication requirements, structures, and protocols necessary to establish a secure connection in microgrids. This article examines the present difficulties facing, and progress in, smart microgrid communication technologies, including wired and wireless networks. Furthermore, it evaluates the incorporation of diverse security methods. This article showcases a case study that illustrates the implementation of a distributed cyber-security communication system in a microgrid setting. The study concludes by emphasizing the ongoing research endeavors and suggesting potential future research paths in the field of microgrid communications.

An introduction of transaction session‐induced security scheme using blockchain technology: Understanding the features of Internet of Things–based financial security systems

AbstractInternet of Things (IoT)–based financial systems leverage the capabilities of blockchain and artificial intelligence (AI) to enable seamless transactions and data exchange between devices. IoT‐based financial systems involve interconnected devices and services, such as payment terminals, wearables, and smart appliances, which collect, transmit, and process sensitive financial information. The study explores security methods incorporated in the financial systems designed using IoT and blockchain technologies to improve the background features. The study data were gathered from Complaint Data from the Consumer Financial Protection Bureau 2018–2022, and data‐based analysis is used in this study for detecting illegitimate interrupted transactions. Propensity Score Matching (PSM) is used for the robustness and endogeneity test; descriptive statistics is utilized in this study. Financial security systems are introduced to reduce the forging and breaching of intruders amid transactions. This study offers a novel contribution to the field of blockchain technology by furnishing a comprehensive analysis of the features of IoT‐based financial security systems from the perspective of the transaction, broadening the understanding of the feature focusing on financial security, and providing practical recommendations to address the features of IoT‐based financial security systems in blockchain technology. The study highlights how IoT devices can securely record and verify financial transactions by leveraging the blockchain's distributed ledger, preventing tampering or unauthorized access. The results of the study identify that the TS3 program relies on the transaction gaps between financial sessions, security requests between successive transactions, and sessions saved depending on the time delay. The study finds that sessions were analyzed for violations and fraud using information stored on the blockchain. The study suggests the design and building of devices and sensors in an IoT in financial security systems. Transparency should contribute to setting data privacy and safety problems in financial security systems.

Super Encryption of the Hill Cipher Method and the AES Method for Security of Employee Salary Data

Data security is a security system that is needed to protect data in systems, networks and other digital components. In a unit or agency such as the South Binjai sub-district office, the management of employee salary data is done digitally, namely via a computer. However, in this increasingly sophisticated digital era, it can cause many irresponsible individuals to easily wiretapping the company's data so that it can be easily accessed by other parties, as well as employee salary data which contains a lot of data. important. In order to avoid unwanted things such as theft of personal data by irresponsible persons, a security system is needed that can maintain the confidentiality of data and secure data. Therefore, in this case, a security system is needed that can maintain the confidentiality of data and secure employee salary data. With that there are many algorithms that can be used in securing employee salary data. And the algorithm used is hill cipher and AES algorithm. The hill cipher algorithm is an encryption-decryption algorithm that uses a transformation matrix. The AES algorithm is a symmetric block ciphertext that can encrypt and decrypt information. The use of these two algorithms can further strengthen the security of employee salary data. From the results of the trials carried out, the sample data used containing employee salary data was successfully secured with both hill cipher and AES algorithms.

Use of an airway bundle to reduce unplanned extubations in a neonatal intensive care unit.

Following the opening of an infant cardiac neonatal intensive care unit, our aim was to determine a baseline UE rate and implement initiatives to target a goal less than 0.5 UEs/100 ventilator days. We utilized the Model for Improvement. Key stakeholders included neonatal providers, nurses, and respiratory therapists. We focused on the creation of an airway bundle that addressed securement methods, communication and education. From October 2017 to January 2018, our baseline UE rate was 0.92 UEs/100 ventilator days. Subsequent to the implementation of an airway bundle with high compliance, we observed a significant change in the centerline (0.45 to 0.02 UEs/100 ventilator days) during the spring of 2021, followed by a period of 480 days with no UEs. In a unit where UEs were infrequent events, high compliance with an airway bundle led to a significantly sustained decrease in our UE rates.