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Advantages of Cloud computing.
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Cloud computing is one of the most powerful inventions that has grabbed the curiosity of technologists all around the world. Cloud computing has many advantages, but it also has a slew of security risks that no organization can afford to ignore. For a successful Cloud Computing adoption in a corporation, proper planning and awareness of emerging ri...
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... also presents a number of security dangers that no enterprise can afford to ignore. The security concerns arise from the vast spectrum of vulnerabilities inherent in any sort of Cloud computing system, and in the absence of solid security guidelines, companies appear hesitant to use an otherwise powerful environment known as cloud computing [2]. Fig. 1 shows advantages of cloud ...
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The popularization and development of big data and cloud computing in e-commerce still face a series of problems, among which the most prominent one is security. How to establish an effective risk assessment system in the cloud computing environment is the primary concern of e-commerce enterprises. This article mainly discusses the network security...
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... If there was a problem, it may have a serious impact on security. Since users had no control over cloud resources, they must rely on the trust mechanism [17]. Sandhu A K team discussed the definition, classification and characteristics of big data, as well as various cloud services. ...
To address the issues of high storage costs for human resource data, resource management of human resource systems, and employee social interaction in human resource management, this study proposes an adaptive intelligent HR data placement strategy based on genetic algorithms. Additionally, a fitness model and an employee social interaction model are presented. The fitness model utilizes genetic algorithms to optimize data storage strategy and server load balancing, resulting in reduced overall costs and efficient resource utilization. This is achieved by comprehensively considering data transmission latency and storage costs. The employee social interaction model analyzes social media blog post topics and employee ranks, and employs a co-training method to predict employee interactions. The experimental results indicated that the placement strategy can improve the HR data transfer by nearly 50% and effectively reduce the standard deviation of server load by about 10-15%. The accuracy, recall, F1 value, and precision of the employee social interaction model were 77.89%, 63.97%, 70.32%, and 71.78%, respectively. The proposed strategy and model demonstrated higher accuracy and robustness in predicting and analyzing employee social interactions, thus providing more reliable decision support for human resource management.
... In [46], a HIDS framework examines processes that consist of sequences of DLL (Dynamic Link Library) instruction calls made by diverse application and system tasks to the Windows operating system kernel to detect anomalous tasks. In [50], a HIDS system integrated with Convolutional Neural Networks (CNN) is described to ensure security in IoT, with general characteristics that make it compatible with all IoT products. Another study [48] introduces HIDS to enhance cloud system security, integrating several machine learning algorithms (such as KNN, Logistic Regression, and Naive Bayes). ...
... This is an integration between OPDs so that the complaints submitted can be more diverse. System integration is needed in every application because it functions as a medium for connecting and developing applications (Vinoth et al., 2022). ...
This research aims to evaluate the readiness of the Indragiri Hulu Regency government, especially Satpol PP and KPBD, to implement the Si ATAN BEDA application, which has been implemented. E-Readiness analysis was carried out using the STOPE approach. The Si ATAN BEDA service application functions as a channel for public complaints, making it easier for the public to submit reports and complaints regarding disturbances of peace, public order, and regional disasters. Implementing the Si ATAN BEDA application service has not shown optimal results due to the government's lack of understanding regarding E-Readiness and making decisions without adequate consideration of the facts. The research method uses a qualitative approach with a descriptive research type. After the field data was collected, the data was processed using NVivo 12 to provide a visual depiction of the field findings and analyzed based on the theory that supports the findings. The results and discussion concluded that the STOPE framework model, which consists of strategy, technology, organization, people, and environment, is the most appropriate model for evaluating the E-Readiness of the Indragiri Hulu Regency government. In the strategy domain, there is a lack of leadership roles in providing direction and supervision, as well as weaknesses in application development planning. The technology domain shows that there are obstacles in application use and security that still need to be strengthened. The lack of cooperation between the government and the private sector in implementing applications is a challenge in the organizational domain. The human resources domain shows that officers' skill qualifications are not aligned with their educational background, and their understanding of applications still needs to be improved. Meanwhile, in the environmental domain of public knowledge, the lack of public knowledge about the application shows a lack of socialization by the government, so the community has not fully felt the benefits.
... These measures aim to ensure the authentication of devices and users, data privacy protection, and data and resource access control. With the shift of businesses towards cloud-based operations, the security of sensitive data becomes essential [1]. However, cloud computing presents inherent security challenges, which makes it necessary to understand effective mitigation strategies and potential threats. ...
Cloud computing plays a significant role in modern information technology, providing organizations with numerous benefits, including flexibility, scalability, and cost-efficiency. However, it has become essential for organizations to ensure the security of their applications, data, and cloud-based networks to use cloud services effectively. This systematic literature review aims to determine the latest information regarding cloud computing security, with a specific emphasis on threats and mitigation strategies. Additionally, it highlights some common threats related to cloud computing security, such as distributed denial-of-service (DDoS) attacks, account hijacking, malware attacks, and data breaches. This research also explores some mitigation strategies, including security awareness training, vulnerability management, security information and event management (SIEM), identity and access management (IAM), and encryption techniques. It discusses emerging trends in cloud security, such as integrating artificial intelligence (AI) and machine learning (ML), serverless computing, and containerization, as well as the effectiveness of the shared responsibility model and its related challenges. The importance of user awareness and the impact of emerging technologies on cloud security have also been discussed in detail to mitigate security risks. A literature review of previous research and scholarly articles has also been conducted to provide insights regarding cloud computing security. It shows the need for continuous research and innovation to address emerging threats and maintain a security-conscious culture in the company.
... Infrastructure as a Service (IaaS) supplies machines, storage, and networking resources that developers manage by incorporating their supportive assets. Platform as a Service (PaaS) furnishes a platform where developers deploy their applications [2]. ...
This paper presents the cloud vendor lock-in prediction framework (CVL), which aims to address the challenges that arise from vendor lock-in in cloud computing. The framework provides a systematic approach to evaluate the extent of dependency between service providers and consumers and offers predictive risk analysis and detailed cost assessments. At the heart of the CVL framework is the Dependency Module, which enables service consumers to input weighted factors that are critical to their reliance on cloud service providers. These factors include service costs, data transfer expenses, security features, compliance adherence, scalability, and technical integrations. The research delves into the critical factors that are necessary for dependency calculation and cost analysis, providing insights into determining dependency levels and associated financial implications. Experimental results showcase dependency levels among service providers and consumers, highlighting the framework’s utility in guiding strategic decision-making processes. The CVL is a powerful tool that empowers service consumers to proactively navigate the complexities of cloud vendor lock-in. By offering valuable insights into dependency levels and financial implications, the CVL aids in risk mitigation and facilitates informed decision-making.
... Cloud computing offers on-demand resources and services, including computing, networking, storage, databases, intelligence, and analytics [1][2][3]. It decreases the operational cost, runs the application efficiently, and scales the resources when needed. ...
Microservices are containerized, loosely coupled, interactive smaller units of the application that can be deployed, reused, and maintained independently. In a microservices-based application, allocating the right computing resources for each containerized microservice is important to meet the specific performance requirements while minimizing the infrastructure cost. Microservices-based applications are easy to scale automatically based on incoming workload and resource demand automatically. However, it is challenging to identify the right amount of resources for containers hosting microservices and then allocate them dynamically during the auto-scaling. Existing auto-scaling solutions for microservices focus on identifying the appropriate time and number of containers to be added/removed dynamically for an application. However, they do not address the issue of selecting the right amount of resources, such as CPU cores, for individual containers during each scaling event. This paper presents a novel approach to dynamically allocate the CPU resources to the containerized microservice during the autoscaling events. Our proposed approach is based on the machine learning method, which can identify the right amount of CPU resources for each container, dynamically spawning for the microservices over time to satisfy the application’s response time requirements. The proposed solution is evaluated using a benchmark microservices-based application based on real-world workloads on the Kubernetes cluster. The experimental results show that the proposed solution outperforms by yielding a 40% to 60% reduction in violating the response time requirements with 0.5\(\times\) to 1.5\(\times\) less cost compared to the state-of-art baseline methods.
... Examples are email services, storage accounts, applications such as Databricks; and platforms such as Microsoft Azure and Amazon Web Services, which are generally accessible through a web browser. Cloud computing has become the most adopted technology in the industry in the current era [19]. It is a strong ally for many organizations that struggle with the deficiencies of on-premise servers. ...
Historically, money laundering has been the financial crime of most significant interest in the international sphere due to the abrupt amounts of money it involves, the damage it causes to the institution’s reputation, and the government spending to combat it. Numerous international organizations, such as the Financial Action Task Force (FATF) and the United Nations (UN), stipulate standards and norms to regulate its prevention and detection. With the emergence of the 4th industrial revolution, technologies such as artificial intelligence, the Internet of Things (IoT), intelligent apps, cloud computing, and cybersecurity play roles in opposite directions in committing and combating money laundering. While artificial intelligence provides innovative models and algorithms capable of predicting money laundering activity, the Internet of Things and intelligent apps seem to create new means for cyber money laundering where cybersecurity comes as a mitigating measure. Bearing this in mind, the current chapter presents an overview of the impact of emerging technologies and cybercrime in executing and fighting against money laundering.
... The algorithms further split task scheduling using heuristics and meta-heuristics and other task scheduling into three techniques. Finally, prospective research directions and new advancements in this subject [27] are offered, along with a description of the benefits and drawbacks of the recommended algorithms. ...
Cloud computing platform offers numerous applications and resources such as data storage, databases, and network building. However, efficient task scheduling is crucial for maximizing the overall execution time. In this study, workflows are used as datasets to compare scheduling algorithms, including Shortest Job First, First Come, First Served, (DVFS) and Energy Management Algorithms (EMA). To facilitate comparison, the number of virtual machines in the Visual Studio Net framework environment is increased. The experimental findings indicate that increasing the number of virtual machines reduces Makespan. Moreover, the Energy Management Algorithm (EMA) outperforms Shortest Job First by 2.79% for the CyberShake process and surpasses the First Come, First Serve algorithm by 12.28%. Additionally, EMA produces 21.88% better results than both algorithms combined. For the Montage process, EMA performs 4.50% better than Shortest Job First and 25.75% superior to the First Come, First Serve policy. Finally, we ran simulations to determine the performance of the suggested mechanism and contrasted it with the widely used energy-efficient techniques. The simulation results demonstrate that the suggested structural design may successfully reduce the amount of data and give suitable scheduling to the cloud.
... Utility computing: It is the most trending IT service representative paradigm for the cloud computing environment and provides on-demand computing resources, including computation, secure storage, processing, and programming services through Application Programming Interfaces based on payment bases [79]. The use of utility computing at the cloud layer maximizes the utilization of computer resources and services efficiently while minimizing lower costs. ...
Established and emerging technologies such as 5G, Internet of Things (IoT), and blockchain will play an increasingly significant role in smart city applications, which reinforce the importance of designing security and privacy-aware/preserving solutions. Hence, we comprehensively survey articles focusing on blockchain for a secure IoT-enabled smart city based on 5G and beyond, published between 2016 and 2023. In this survey, we first introduce the seminal contributions and background technological knowledge regarding smart city. Furthermore, based on the layered blockchain-based architecture of the IoT-enabled smart city, we provide an all-inclusive summary of previous works and outline the blockchain research framework in smart city. Then, we discuss how to use blockchain and beyond 5G in smart city applications, including smart manufacturing, and smart vehicular networks. In addition to reviewing the existing approaches described in the 125 articles surveyed, we also identify several limitations and present potential extensions to design future blockchain-based solutions for smart cities.
... Data breaches pose a substantial danger due to the communal nature of cloud systems and the extensive presence of sensitive financial information, rendering them appealing targets for cybercriminals. A recent investigation conducted by Vinoth et al. (2022) has demonstrated that inadequate authentication mechanisms, deficiencies in access control protocols, and improperly configured security configurations might lead to unauthorised entry. These security breaches not only result in the compromise of customer information, but also give rise to regulatory infractions and substantial harm to reputation. ...
The adoption of cloud computing introduces a range of potential risks that financial institutions must navigate with prudence. Cloud service providers are entrusted with valuable customer information, and any compromise could have severe consequences, including financial losses and reputational damage. The main objective of this research was to assess the potential risks of cloud computing in financial institutions in Tanzania. This is done in the context of CRDB bank. The research employed a mixed methods approach, incorporating both quantitative and qualitative data collection methods. The data was acquired through questionnaires, specifically targeting the employee population of CRB bank. The data underwent quantitative analysis. The research sampled population is 201 respondents from ICT, legal and procurement departments at the financial institution. Cloud computing poses hazards that financial organizations must carefully manage. Security of sensitive financial data comes first. Any compromise of cloud service providers' client data could result in financial losses and reputational damage. Data privacy risks occur as legislative contexts change. Cross-border cloud services can challenge data sovereignty and local legislation. Another crucial factor is operational continuity. Financial institutions depend on uninterrupted service, putting them exposed to cloud service provider outages and technical issues. Maintaining financial services and client satisfaction are crucial. The regulatory compliance challenge is unique. Cloud computing requires vigilance in local and international legal systems. To retain financial ecosystem confidence, financial institutions must ensure their cloud-based solutions meet industry standards and laws. The study stressed the importance of a holistic strategy to cloud computing in financial institutions like CRDB Bank PLC. Cloud technology has many benefits, but stakeholders must be cautious and implement risk management and mitigation strategies. The conclusions of this study can help CRDB Bank PLC and other Tanzanian financial institutions make educated cloud technology implementation decisions. These decisions must prioritize financial system security, privacy, and resilience. The results also highlight the need for financial industry-regulatory cooperation to keep the regulatory framework up to date with technology.
