Computing Security Issues Research Articles

Cybersecurity Behavior in the West Sumatra Universities

User cybersecurity behavior refers to the actions, habits, and decisions made by individuals when using technology and information that affect the level of security of the data and systems they access. Previous research has shown that user cybersecurity behavior is one of the leading causes of computer and information security issues in many organizations, particularly education. To address this issue, researchers must find solutions to improve user cybersecurity behavior within an organization. Therefore, this study aims to find the factors influencing user cybersecurity behavior in higher education institutions in West Sumatra in 2020. This study was conducted using a survey research method. A questionnaire was distributed to 155 respondents. The questionnaire consisted of 28 questions covering seven factors influencing user cybersecurity behavior. The survey data will be analyzed using the Structural Equation Model based on Partial Least Square. The research findings indicate that all variables, such as Misuse Prevention and Compliance, Body of Knowledge, Skill, Behavioral Intervention, Attitude, Security Compliance Behavior, and Technology, have significant relationships. The relationships between these factors will be shown in the framework to be developed. This indicates that the education sector in Indonesia is aware of cyber threats and the importance of security procedures in the workplace. For further research, a deeper exploration of specific security issues is needed to propose potential solutions or actions that can be implemented to improve user cybersecurity behavior in the education sector, particularly in Indonesia.

Non-interactive Private Multivariate Function Evaluation using Homomorphic Table Lookup

To address security issues in cloud computing, fully homomorphic encryption (FHE) enables a third party to evaluate functions using ciphertexts that do not leak information to the cloud server. The remaining problems of FHE include high computational costs and limited arithmetic operations, only evaluating additions and multiplications. Arbitrary functions can be evaluated using a precomputed lookup table (LUT), which is one of the solutions for those problems. Previous studies proposed LUT-enabled computation methods 1) with bit-wise FHE and 2) with word-wise FHE. The performance of LUT-enabled computation with bit-wise FHE drops quickly when evaluating BigNum functions because of the complexity being O(s·2^d·m), where m represents the number of inputs, d and s represent the bit lengths of the inputs and outputs, respectively. Thus, LUT-enabled computation with word-wise FHE, which handles a set of bits with one operation, has also been proposed; however, previous studies are limited in evaluating multivariate functions within two inputs and cannot speed up the evaluation when the domain size of the integer exceeds 2N, where N is the number of elements packed into a single ciphertext. In this study, we propose a non-interactive model, in which no decryption is required, to evaluate arbitrary multivariate functions using homomorphic table lookup with word-wise FHE. The proposed LUT-enabled computation method 1) decreases the complexity to O(2^d·m/l), where l is the element size of FHE packing; 2) extends the input and output domain sizes to evaluate multivariate functions over two inputs; and 3) adopts a multidimensional table for enabling multithreading to reduce latency. The experimental results demonstrate that evaluating a 10-bit two-input function and a 5-bit three-input function takes approximately 90.5 and 105.5 s with 16-thread, respectively. Our proposed method achieves 3.2x and 23.1x speedup to evaluate two-bit and three-bit 3-input functions compared with naive LUT-enabled computation with bit-wise FHE.

Survey of Mobile Cloud Computing Security and Privacy Issues in Healthcare

Survey of Mobile Cloud Computing Security and Privacy Issues in Healthcare

Cloud Computing Security Issues, Challenges, and Solutions

Cloud computing has revolutionized the way agencies manage and deliver their IT offerings by means of imparting scalable, bendy, and fee-effective solutions. However, with these good sized blessings come complicated protection demanding situations. The allotted nature of cloud environments, coupled with multi-tenant infrastructures, offers new risks that require sturdy and adaptive security frameworks. This paper goals to provide an in-intensity exploration of the primary security problems in cloud computing, the underlying challenges companies face in securing cloud services, and the solutions designed to mitigate these dangers

Data Security Issues in Intelligent Cloud Computing Systems

This project takes the intelligent cloud computing system as the main research object. It mainly uses the literature analysis method to explore the data security problems in the intelligent cloud computing system. By investigating the development background of intelligent cloud computing, the author summarizes the definition of cloud computing and the characteristics of cloud computing. The author also makes a specific analysis of the advantages of cloud computing, and concludes that cloud computing is possible through safe and reliable data, low demand for clients, shared cloud data and unlimited possibilities. These four points are the main reasons major enterprises choose cloud computing to replace traditional computing resources. In the topic, the author studied the data security problems in cloud computing from three aspects: the problems exposed in the development process of cloud computing, the effective measures to protect data security in cloud computing and predicting the future development trend of improving data security. The authors believe that future research should focus on a balance between network security issues in cloud computing and building a secure and reliable data system.

CATcAFSMs: Context‐based adaptive trust calculation for attack detection in fog computing based smart medical systems

AbstractFog's basic distributed nature and ability to process data in transit—that is, to make decisions in real time—make it a good fit for scenarios involving several distributed devices that need to communicate, provide real‐time data analysis, and carry out storage functions. The majority of fog computing applications are driven by the user's demands and/or their desire for functioning services, either neglecting or giving security considerations second attention. Fog computing security issues have not received enough attention. Fog computing could be exploitable due to the security difficulties associated with cloud computing. Due to its flexibility to function near the end user and independence from a centralized design, fog computing provides the dependability required by time‐sensitive smart healthcare systems. There is a need for enhanced security and privacy solutions for fog computing, where trust is essential, due to the importance of healthcare data. This research aims to develop a context‐based adaptive trust solution for the smart healthcare environment utilizing Bayesian approaches and similarity measures against bad mouthing and ballot stuffing, while context‐dependent trust solutions for fogs remain an unexplored area of study. The proposed trust model has been simulated in Contiki‐Cooja to evaluate our findings. In contrast to static weighting, adaptive weights are provided to direct and indirect trust using entropy values that ensure the least degree of trust bias, and context similarity calculations eliminate recommender nodes with malicious intent by leveraging server, colleague, and service similarities. The proposed model protects smart healthcare systems from attacks using similarity metrics, incorporates context, and also uses adaptive weighting for trust calculation. By eliminating trust bias and also detecting attacks, this solution enhances the trust calculation by 10% as compared to the previous solution. This paradigm is efficient due to its small trust computation overhead and linear complexity O(n).

Survey of Cloud Computing Security and Privacy Issues

The world of computing and data management has completely changed as a result of cloud computing, which offers both consumers and enterprises scalable resources, on-demand services, and considerable cost savings. The demand for more adaptable and effective computer resources has prompted this paradigm change, and cloud computing provides it by enabling users to access and utilize computing resources over the internet. But using cloud computing also raises a number of security and privacy issues that must be properly handled. In order to assess the present status of cloud computing security and privacy problems, this article will examine key terms, the development of the technology, its significance, current research gaps, and the urgent need for more in-depth analysis.

Cybersecurity Implications of Edge Computing in Data Engineering

Edge computing, as an extension of cloud computing, brings computation and data storage closer to the data source. This shift offers significant advantages in terms of latency reduction, bandwidth optimization, and real-time processing capabilities. By minimizing the distance that data needs to travel, edge computing enhances the performance of applications that require rapid data processing and immediate response times. This is particularly beneficial for Internet of Things (IoT) devices, autonomous vehicles, smart grids, and other applications that demand low-latency interactions. However, the decentralization inherent in edge computing introduces a unique set of cybersecurity challenges that are distinct from those faced in traditional centralized cloud environments. The distributed architecture of edge computing creates numerous points of vulnerability, each of which can be exploited by cyber attackers. Additionally, edge devices often operate with limited computational resources and power, which complicates the implementation of robust security measures. This paper explores the cybersecurity implications of edge computing in the context of data engineering. It begins with a comprehensive review of existing literature to establish the current understanding of edge computing security issues. The review identifies the primary vulnerabilities associated with edge computing, including those related to distributed architecture, data transmission, and resource constraints. Following the literature review, the paper delves into specific security threats that edge computing environments face. These include man-in-the-middle (MitM) attacks, distributed denial-of-service (DDoS) attacks, malware and ransomware, and insider threats. Each threat is analyzed to understand its potential impact on edge computing systems and the data they process.

Revolutionizing Cybersecurity with Machine Learning: A Comprehensive Review and Future Directions

In the realm of computing, data science has revolutionized cybersecurity operations and technologies. The key to creating automated and intelligent security systems lies in extracting patterns or insights from cybersecurity data and building data-driven models. Data science, encompassing various scientific approaches, machine learning techniques, processes, and systems, studies real-world occurrences through data analysis. Machine learning techniques, known for their flexibility, scalability, and adaptability to new and unknown challenges, have been applied across many scientific fields. Cybersecurity is rapidly expanding due to significant advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, and more. Various machine learning techniques have effectively addressed a wide range of computer security issues. This article reviews several machine learning applications in cybersecurity, including phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns associated with machine learning techniques themselves. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine learning model using supervised learning algorithms. These models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and employ ensemble methods that combine multiple machine learning models for improved performance

A Review on Cloud Computing Security Issues, Applicable Solutions and Implementation

Cloud security refers to the methods and technologies used to protect data and resources stored in the Cloud. Cloud security is crucial as businesses increasingly adopt cloud computing to store and process sensitive business data. Effective cloud security solutions include encryption, access control, data loss prevention, and regular security audits. These methods ensure data confidentiality, integrity, and availability. A significant advantage of cloud security is that data can be backed up and protected off-site. This means that in case of any disaster or cyber-attack, important data remains secure and retrievable. Overall, cloud security is essential to ensure data privacy, protect against cyber threats and data breaches, and ensure business continuity.

Securing the Skies- A Critical Analysis of Cloud Infrastructure Vulnerabilities

Cloud computing offers storage, infrastructure, computing, networking, databases, platform, software, and analytics services over the Internet. It provides numerous benefits including scalability, cost management, broad access to resources, elasticity, resource pooling. Though cloud computing is mature and widely adopted computing model in software as well as non-software industries, it has several issues regarding security as it provides most of the cloud services over the public infrastructure. Denial of service (DOS), malware injection, insecure APIs, data loss, data breaches, hypervisor vulnerabilities, VM escape are a few major issues in cloud computing. In this paper, authors tried to provide a comprehensive analysis of critical security issues in cloud computing. Furthermore, this paper critically analyses the existing solutions to the various security issues in the cloud computing model.

Survey of Secure Data Access Control Models in Cloud computing for EHR

Today cloud computing is the first choice of many individuals and different organizations. Computing services offered by different CSP’s are available as per need and the requirement. Most of the healthcare organizations are using cloud services for transmitting, storing and processing electronic health records (EHR) of patients. Patient’s health record is sensitive and there is possibility of violation of privacy by CSP’s. Data access control is one of the best mechanisms to address security issues in cloud computing and ensure the safety, reliability of the system and privacy of the user data. Existing surveys on access control mechanisms in cloud computing focus on all traditional centralized models. However, decentralized access models like block chain with traditional access control model is also used now days for ensuring privacy of EHR. This paper takes the detailed of reviews such existing access control mechanisms in cloud computing, based both types of access control models. Also provides comparisons on each model’s advantages and limitations, and discusses the challenges of, and future research direction for access control.

Create strong and unique passwords for digital security!

Üsküdar University Faculty of Engineering and Natural Sciences Computer Engineering Department Head Prof. Burhan Pektaş evaluated the issue of computer and smartphone security on the occasion of November 30, World Computer Security Day.

ИССЛЕДОВАНИЯ В ОБЛАСТИ КОМПЬЮТЕРНОЙ БЕЗОПАСНОСТИ И ПРЕДОТВРАЩЕНИЯ КОМПЬЮТЕРНЫХ ВИРУСОВ

With the rapid development of computer technology, the use of computers is becoming more common. However, at the same time, computer security problems are becoming more serious. Computer viruses always threaten people’s computer security. To effectively solve this problem, this article first analyzes computer security issues, then explains the main characteristics of computer viruses, and finally conducts a series of discussions on how to improve computer security.

Human Error and Clinical Data Sharing

Computer security issues have a widespread impact across all sectors and are increasingly influencing the healthcare industry. Since 2019, several reported attacks have been specifically aimed at exploiting vulnerabilities in medical network protocols like 'DICOM' and 'HL7,' as well as file formats, particularly those related to digital imaging and medical communications. While numerous articles delve into subjects such as security and blockchain, they often disregard the significant role that human errors play in compromising security. Human errors within hospital computer systems can lead to substantial implications for both patient care and data security. This article delves into the various forms of human errors that can occur within such systems and assesses their potential consequences on the overall effectiveness and security of healthcare information management. The study underscores the necessity of effective mitigation strategies to reduce the associated risks of these errors and enhance the dependability of hospital computer systems

Quantum walks-based classification model with resistance for cloud computing attacks

Cloud computing is considerably investigable and adoptable in both industry and academia, and Software Defined Networking (SDN) has been applied in cloud computing. Although SDN mitigates some security issues in cloud computing, new security issues related to its own architecture are also introduced. In this paper, we propose a quantum walks-based classification model which is available for intrusion detection in cloud computing. The proposed model concentrates feature information of data via Principal Component Analysis, and then aggregates the concentrated data in the way of quantum walks by a training-free clustering algorithm. The clustering algorithm constructs coin transformation and conditional shift transformation based on transition probabilities to move similar data toward each other. To enhance the usability of the proposed model in cloud computing security, we propose a new cloud architecture which adds security layer in SDN to ponder the protection of cloud computing fundamentally, and simplify transition probabilities equations of clustering algorithm without affecting clustering accuracy, decreasing the time complexity from O(nk2) to O(nk). The experimental results on popular datasets (Accuracy: 99.4% on InSDN, 95.8% on NSL-KDD, 98% on UNSW-NB15 and 96.4% on CSE-CIC-IDS2018) revealed that the proposed model is effective dealing with attacks on SDN-based cloud computing, and is able to maintain stable and excellent attack identification ability under different traffic intensities.

Cloud Computing Security Issues and Countermeasure: A Comprehensive Survey

Cloud Computing Security Issues and Countermeasure: A Comprehensive Survey

Machine Learning in Cybersecurity: Techniques and Challenges

In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Due to its distinctive qualities, such as flexibility, scalability, and the capability to quickly adapt to new and unknowable obstacles, machine learning techniques have been used in many scientific fields. Due to notable advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, etc., cyber security is a rapidly expanding sector that requires a lot of attention. Such a broad range of computer security issues have been effectively addressed by various machine learning techniques. This article covers several machine-learning applications in cyber security. Phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns with machine learning techniques themselves are all covered in this study. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine-learning model using supervised learning algorithms. Machine learning models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and to employ ensemble methods that combine multiple machine learning models for better performance.

Using support vector machine regression to reduce cloud security risks in developing countries

The use of the cloud by governments throughout the world is being aggressively investigated to increase efficiency and reduce costs. The majority of cloud computing risk management programs prioritize addressing cloud security issues that government organizations may face when they choose to adopt cloud computing systems, but these programs lack evidence of security risks, and problems with using cloud computing in developing nations are uncommon, so they called for more research in this area. The objective of this paper is to use quantitative models namely Spearman's Rank correlation coefficient, simple regression, and support vector machine regression (SVMR) for estimating cloud security issues based on cloud control factors for improving the mitigation of cloud computing security issues based on control factors using intelligent models in a government organization. Identify the proper cloud control factors for every cloud security issue from estimation errors using a standard for performance measurement like mean square error (MSE) and root mean square error (RMSE), performance measurement to evaluate and validate proposed models. SVMR is an approach to enhance practices for cloud security platforms to mitigate risks and infrastructure for cloud adoption in developing countries in this paper.

Addressing Cloud Computing Security and Visibility Issues

Addressing Cloud Computing Security and Visibility Issues