An Introduction to Cybersecurity and CompTIA

The cloud computing plays the prominent role in many organizations and researchers were focus on securing the cloud computing. The privacy preserving is the major challenge that grows exponentially with increases in user. In this paper, the depth survey is conducted on the recent methodologies of the cloud storage security related with the cloud computing. The overview of the cloud computing and security issues is analyzed in this paper. The key security requirements such as data integrity, availability and confidentiality. Security issues in the recent methodologies of cloud security is analyzed. The challenges in the cloud security is analyzed and possible future scope of the method is discussed. The paper involves in analyzing the state-of-art method to investigate the advantages and limitations.

In March 2020, the SARS-CoV-2 coronavirus pandemic took the entire world by surprise. In an incredibly short period of time the effects of the pandemic affected practically all aspects of personal and professional life. The situation of the epidemic has been a challenge for social workers in their professional and personal life. It makes social workers face the need to apply new methods and forms of action and imposes a number of additional professional responsibilities and psychological burdens on them. Changing the organization of work in a pandemic is often a direct threat to a social worker’s physical health, as well as it enters and violates the area of their non-professional life. The goal of this article is to show the problem of keeping balance between professional work and personal life of a social worker during the SARS-CoV-2 pandemic. The empirical material was collected basing on the experience of an employee of the Municipal Social Welfare Centre in Olsztyn.

2021 ACOMS Elaine A. Stuebner Scholars Award winning essay

ABSTRACT: As cloud computing continues to play a pivotal role in data storage and processing, ensuring the security and integrity of transferred data remains a critical concern. This project introduces a novel approach for secure data transfer and detection leveraging the Counting Bloom Filter in cloud computing environments. The proposed system addresses the vulnerabilities associated with traditional data transfer methods by incorporating the Counting Bloom Filter, a probabilistic data structure. This technology aids in optimizing storage efficiency and enhances the accuracy of data detection, crucial for maintaining data integrity during transmission. The project focuses on these cure transfer of sensitive information within cloud computing environments, providing an added layer of protection against unauthorized access and potential data corruption. The Counting Bloom Filter is employed to detect anomalies and ensure the integrity of the transferred data, contributing to a robust and reliable cloud-based data transfer mechanism. Key objectives of the project include the implementation of advanced encryption techniques for secure data transfer, the integration of the Counting Bloom Filter for efficient data detection, and the development of a comprehensive system ensuring data integrity in the cloud. The project's innovation lies in its ability to enhance the security and reliability of data transfer in cloud computing, addressing the evolving challenges associated with data protection and privacy. In conclusion, the Secure Data Transfer and Detection from Counting Bloom Filter in Cloud Computing project represents a significant advancement in the realm of secure data transmission. By combining encryption methods with the unique capabilities of the Counting Bloom Filter, the project offers an effective solution for securing data during transfer, ensuring the integrity of information in cloud computing environments. Keywords:- Cloud computing, Data storage, Data processing, Data transfer, Security, Integrity, Counting Bloom Filter, Probabilistic, data structure, Storage efficiency, Data detection, Sensitive information, Encryption techniques, Unauthorized access, Data corruption, Anomaly detection, Data protection, Privacy, Secure data transfer, Cloud-based data transfer, System development, Data transmission.

The rapid digitalization of healthcare systems has led to an unprecedented accumulation of sensitive patient data across various platforms, exposing the industry to growing risks of data breaches, unauthorized access, and integrity compromise. Ensuring the security, privacy, and trustworthiness of healthcare data is paramount to maintaining patient confidentiality and enhancing clinical decision-making. This study proposes a conceptual framework that synergistically integrates Blockchain technology and Artificial Intelligence (AI) to enhance healthcare data security. The framework is designed to address key challenges such as data integrity, access control, real-time threat detection, and secure interoperability across healthcare stakeholders. Blockchain, with its decentralized and immutable ledger capabilities, provides a robust foundation for tamper-proof data storage and transparent audit trails. Smart contracts are employed to automate access controls and ensure compliance with regulatory requirements. AI, on the other hand, plays a critical role in intelligent threat detection and anomaly monitoring. By leveraging machine learning algorithms, the framework can identify suspicious patterns, detect insider threats, and predict potential breaches in real time. The proposed architecture comprises four core components: Secure data ingestion, blockchain-based data storage, AI-powered analytics, and a privacy-preserving access management layer. The framework also incorporates role-based authentication and homomorphic encryption techniques to enhance data privacy while supporting authorized data sharing. Case scenarios such as electronic health record (EHR) exchange and remote patient monitoring are used to demonstrate the practicality and scalability of the model. This integrated approach not only ensures the confidentiality, integrity, and availability of healthcare data but also fosters trust among patients, providers, and regulatory bodies. The framework aligns with global healthcare data standards and complies with regulations such as HIPAA and GDPR. Future directions include the deployment of federated learning models to further decentralize AI training while maintaining data privacy across institutions.

Cloud computing has become an integral part of the digital world, offering cost effective and flexible features for data storage, processing, and transfer. However, security concerns have become a major challenge with the increased reliance on cloud computing. In particular, secure text transfer in the cloud is a crucial aspect of cloud computing, as it involves the transmission of sensitive information. This research paper proposes a cloud-based secure text transfer mechanism that addresses the security challenges of text transfer in the cloud environment. The suggested technique guarantees the confidentiality, integrity, and availability of data during transmission, and provides a high level of security for cloud users. We present a comprehensive analysis of the proposed mechanism and evaluate its performance against various security metrics. The results of our research demonstrate the effectiveness of the proposed mechanism in providing secure text transfer in the cloud environment. Keywords: Cloud Computing, Secure Text Transfer, Confidentiality, Integrity, Availability, Security, Data Transmission, Cloud Users, Security Metrics.

As COVID-19 continues to spread around the world, the healthcare industry has accelerated the transformation to digital healthcare services. In the era of big data, many hospitals prefer to use remote cloud servers to store and manage massive electronic medical data. However, cloud-assisted medical data systems cannot guarantee the confidentiality, integrity, and availability of data. Searchable encryption can effectively address the above challenges by enabling data search on the ciphertext, which achieves the availability of medical data while ensuring data security and privacy. However, the search server may return mismatched search results due to economic interests or single points of failure. Blockchain is a decentralized computing paradigm with public verifiability, which provides an efficient solution to this problem. However, the existing blockchain-based searchable encryption solutions do not consider the flexible search function of multiple users and the restriction of encrypted data for medical scenarios. Therefore, we propose a blockchain-based multiuser normalized searchable encryption (BNSE) scheme and design a blockchain-based normalized searchable encryption system for medical data (BNSEM) based on the scheme. To verify the practicality of the system, we evaluate the performance from both theoretical and experimental aspects.

Staff compliance is a crucial factor in maintaining the confidentiality, integrity, and availability of patient data, in line with the Minister of Health Regulation No. 269/2008 and the ITE Law No. 11/2008. EMR also increases the risk of sensitive patient data leakage if not managed with strict security procedures. Annisa Hospital Bogor, as an institution that has implemented EMR, is obliged to ensure the confidentiality, integrity, and availability of data. This study aims to measure and analyze the level of compliance of medical and non-medical staff at Annisa Hospital Bogor with information security procedures in managing Electronic Medical Records (EMDR). The method used is descriptive quantitative by distributing questionnaires to the EMR user staff population. The results showed that the average compliance rate was 81.6%, but vulnerabilities were found in the use of passwords and log-out protocols after accessing the system. The study recommends the need for regular training and systematic audits to improve staff awareness and discipline.

Cet article decrit, du point de vue d'un clinicien generaliste, une solution pour l'implementation de dossiers concernant les patients en toute securite, ainsi que les strategies mises en place pour cette implementation. Cet article souligne donc l'importance de la securite pour la confidentialite de ces dossiers, mais aussi la difficulte de mettre en place un systeme de protection de l'information sur

Šeimos ir karjeros derinimas – kiekvienam žmogui aktuali problema, tačiau dažniausiai su tuo susiduria moterys, auginančios vaikus. Visuomenėje tebevyrauja stereotipas, kad moteris turi aukotis dėl šeimos. Išanalizavus mokslinę ir metodinę literatūrą išsiaiškinta, kad moterims, auginančioms vaikus, karjeros derinimą sunkina šie veiksniai: 1) profesinio ir asmeninio gyvenimo derinimo konfliktas; 2) karjeros pertrūkiai; 3) nelankstus vaikų priežiūros organizavimo pobūdis; 4) lyčių vaidmenys (lyties diskriminacija); 5) darbdavių požiūris į reprodukcinio amžiaus moteris.Tyrimo tikslas – ištirti, kokių sunkumų kyla moterims, auginančioms vaikus ir derinančioms šeimą ir karjerą. Tyrimo duomenys buvo rinkti taikant pusiau struktūruoto interviu metodą. Interpretuojant duomenis naudotasi turinio analizės metodu. Taikyta tikslinė informantų atranka (tyrimo dalyvės – penkios moterys, auginančios vaikus ir derinančios šeimą ir karjerą). Empirinio tyrimo rezultatai liudija, kad didžiausias sunkumas moterims yra profesinio ir asmeninio gyvenimo derinimo konfliktas. Sunkumus lemiančios priežastys dažniausiai yra šios: laiko stoka, darbdavių nesupratingumas ir namų rutina, sąlygojanti socialinių ir profesinių įgūdžių stoką. Dermės sėkmę lemia vaidmenų kaita ir tolygus darbo ir pareigų pasidalijimas šeimoje. Moterys akcentuoja, kad valstybės politika nėra orientuota į pagalbą moterims, norinčioms derinti šeimą ir karjerą.

This report documents the outcomes of Dagstuhl Seminar 11492 “Secure Architectures in the Cloud”. In cloud computing, data storage and processing are offered as services, and data are managed by external providers that reside outside the control of the data owner. The use of such services reduces the burden of the owners in managing their data, and may provide significant cost savings. However, cloud computing introduces new security and privacy concerns. In fact, there is little consensus on how to guarantee the confidentiality, integrity, and availability of data in cloud computing scenarios. Also, it is unclear to what extent parties can be held accountable in case something goes wrong. In this seminar, we searched for architectures, modelling approaches, and mechanisms that can help in providing guarantees for cloud security. We proposed the concept of verification-as-a-service that can guide architectures for verification of cloud architectures and configurations, as well as results of computations. We also proposed architectures for organizing customisability of security and privacy for cloud customers.

Cloud computing is the fruit of recent developments in information technology, it provides access to many online services as well as remote computing resources as needed. To be more specific, cloud computing stands today as a satisfactory answer to the problem of storage and computing of data encountered by companies. It provides treatment and accommodation of their digital information via a fully outsourced infrastructure. The latter enables users to benefit from many online services without worrying about the technical aspects of their use. In the meanwhile, it limits costs generated by the management of these data. However, this advanced technology has immediately highlighted many serious security troubles. The major issue that prevents many companies to migrate to the cloud is the security of sensitive data hosted in the provider. Actually, the security problem related to this technology has slowed their expansion and restricted in a severe way their scope. The work in this paper deals to present a literature review of data security approaches for cloud computing, and evaluates them in terms of how well they support critical security services and what level of adaptation they achieve.

Computer network security plays a crucial role in ensuring the integrity, confidentiality, and availability of data in the modern digital era. One of the primary tools used to safeguard networks from unauthorized access and external threats is the firewall. This paper explores the significance of firewalls in protecting network systems by examining their functions, various types, and capability to regulate data traffic. Firewalls work by enforcing specific rules to limit access, effectively reducing the risk of cyber threats such as malware, hacking attempts, and unauthorized intrusions. The study also involves practical observation of firewall implementation in a real network setup to evaluate their efficiency in mitigating security risks. Findings indicate that properly configured firewalls can considerably strengthen a network’s defense. Nevertheless, relying solely on firewalls is insufficient. They should be supported by additional security technologies and best practices to achieve comprehensive network protection. In conclusion, firewalls are essential elements in upholding the reliability and safety of computer networks.

Cloud computing is one of the prominent areas in today's world. Several advantages exist in cloud computing, but the major drawback is that it is difficult to build a secure cloud storage system. Cloud environment is controlled by several service providers who are semi-trusted, and also hackers can attack the service providers easily without the owner's knowledge. The proposed system concentrates on the construction of a secure cloud storage system in a decentralized way. The proposed work does not depend on a third-party system. In this scheme, secure user authentication can be provided by using the iris verification system and data confidentiality by using new hybrid cryptographic techniques such as AES (Advanced Encryption Standard), DES (Data Encryption Standard), and CST (Cyclic Shift Transposition). In addition, secure data retrieval and data integrity can be provided by using data matrix code and blockchain verification scheme. The proposed system provides more security and integrity in cloud environment in a well-organized manner. In this scheme, the service provider cannot access the data without the data owner's knowledge. Hence, data ownership is protected in this scheme.

A Wireless Sensor Network (WSN) encompasses interconnected Sensor Nodes (SNs) that interact wirelessly to collect and transfer data. Security in the context of WNS refers to protocols and measures implemented for the overall functionality of the network, along with protecting the availability, confidentiality, and integrity of data against tampering, unauthorized access, and other possible security risks. An Intrusion Detection System (IDS) utilizing Deep Learning (DL) and Feature Selection (FS) leverages advanced methods to enhance effectiveness in the detection of malicious activities in a network by enhancing relevant data features and leveraging the power of Deep Neural Networks (DNNs). This study presents a Dynamic Arithmetic Optimization Algorithm within a DL-based IDS (DAOADL-IDS) in WSNs. The purpose of DAOADL-IDS is to recognize and classify intrusions in a WSN using a metaheuristic algorithm and DL models. To accomplish this, the DAOADL-IDS technique utilizes a Z-score data normalization approach to resize the input dataset in a compatible format. In addition, DAOADL-IDS employs a DAOA-based FS (DAOA-FS) model to select an optimum set of features. A Stacked Deep Belief Network (SDBN) model is employed for the Intrusion Detection (ID) process. The hyperparameter selection of the SDBN model is accomplished using the Bird Swarm Algorithm (BSA). A wide experimental analysis of the proposed DAOADL-IDS method was performed on a benchmark dataset. The performance validation of the DAOADL-IDS technique showed an accuracy of 99.68%, demonstrating superior performance over existing techniques under various measures.