Comparative Study of Security Algorithms in Cloud Computing

With the fast development of cloud computing and its wide application, data security plays an important role in cloud computing. This paper brought up a novel data security strategy based on artificial immune algorithm on architecture of HDFS for cloud computing. Firstly, we explained the main factors influence data security in cloud environment. Then we introduce HDFS architecture, data security model and put forward an improved security model for cloud computing. In the third section, artificial immune algorithm related with negative selection and dynamic selection algorithm that adopted in our system and how they applied to cloud computing are depicted in detail. Finally simulations are taken by two steps. Former simulations are carried out to prove the performance of artificial immune algorithm brought up in this paper, the latter simulation are running on Cloudsim platform to testify that data security strategy based on artificial immune algorithm for cloud computing is efficient.

In the modern era of the computing world, the data producing and using it is becoming large and instant at various places. For availing the data at different locations for processing we need to store it in the global platform. Cloud environment provides a best and easy way for this. Cloud computing is becoming as the essential thing for high-quality data services. However there are some potential problems with respect to data security. Here encryption techniques can be used for providing security, but with restricted efficiency. In this paper we propose a new encryption mechanism for providing data security in cloud environment. We propose a two round searchable encryption which supports multi keyword retrieval. Here we adapted a vector space model for improving search accuracy; the elgamal encryption technique allows users to involve in the ranking, while the essential key part of encryption will be done at the source itself. The proposed improves the data security and reduces data leakage.

Virtual computing resources are provided via a cloud system that is both clever and intelligent. Based on the user's request, computing resources are made available. A hybrid cloud is the best option for storing and accessing user data for cloud deployments. Maintaining security in a hybrid cloud environment is time-consuming. This study provides a novel strategy for securing data in the hybrid cloud by ensuring the user's data is protected. Users' data in a hybrid cloud is protected using cryptographic approaches provided in this approach. Using this strategy, users' data may be protected in public and private clouds using various encryption methods. The suggested data security paradigm offers various advantages to both consumers and providers in terms of data security. Three symmetric encryption methods are offered as a service in the cloud. The concept is implemented as a cloud-based application hosted in the cloud, and the effectiveness of three strategies is assessed. They are evaluated in terms of performance and security. Using the recommended encryption methods in a hybrid cloud environment is more efficient than using other methods. The proposed technique can be used for relational data. It can be modified and enhanced to process multimedia data.

Ensuring Data Security in Cloud Environment Through Availability

The security issues in the cloud have been well studied. The data security has much importance in point of data owner. There are number of approaches presented earlier towards performance in data security in cloud. To overcome the issues, a class based multi stage encryption algorithm is presented in this paper. The method classifies the data into number of classes and different encryption scheme is used for different classes in different levels. Similarly, the user has been authenticated for their access and they have been classified into different categories. According to the user profile, the method restricts the access of user and based on the same, the method defines security measures. A system defined encryption methodology is used for encrypting the data. Moreover, the user has been returned with other encryption methods which can be decrypted by the user using their own key provided by the system. The proposed algorithm improves the performance of security and improves the data security.

Modern technological advances have given rise to the popularity and success of cloud in organizations and industries because it provides data storage facility and computing services at minimal cost. Data security is one of the important requirements for outsourced data in a cloud storage environment. It also brings new security vulnerabilities for assuring the data confidentiality, data integrity and data access control. Data security of a cloud system depends heavily on the cloud service provider, data owner and cloud users. So, there needs to be some methodology to monitor security vulnerabilities and providing data security in cloud environment. Now a day’s providing data security in cloud storage is one of the most critical tasks. For the past few years many works have been done to assure data security but they are failed in providing data security such as information leakage, secret-key management, cloud-user authentication, breach of data confidentiality, because of collusion attack and lots of computational activities (because of large number of secret keys).In this paper, we used threshold cryptography technique for providing data security in cloud storage system. Proposed approach addresses the data security issues in cloud system more efficiently. In this Threshold cryptography technique data owner create users in groups based on place or location, project, and department, data owner allocates single secret key to each group of users for decryption of data and every users in the group shares parts or pieces of the secret key. Proposed methodology also uses data access control list to control the data access. Proposed approach provides data security more efficiently, it also increases the performance of the system and also it minimizes the number of secret keys.

Cloud security is a branch of cyber security that focuses on securing cloud computing systems. It is a method for enterprises to use the internet to access storage and virtual services while saving money on infrastructure, considered as the next-generation architecture since it combines application software and databases into big data centers. However, difficulties with confidentiality, protection, integrity, and compliance may surface when the information is not kept, examined, or computed locally. A wide range of data security-related topics are covered by cloud-based data security. For securing an organization's data across the network, data encryption is a common and effective security strategy that is a great alternative. The most vulnerable data is financial and payment systems, and medical data, which can expose customers or clients critical information. Several academics have also offered a range of approaches to safeguards to maintain data privacy and security while it is being transmitted, but there are still a lot of obstacles to the safety of data that is being stored in the cloud. In this paper, several research papers that have been authored and published in this topic are thoroughly examined and analysed. This study gives a review of the literature on different methods for establishing data security in cloud environment.

Hybrid encryption techniques are gaining popularity in the cloud platform due to their ability to address security concerns and data storage challenges. In order to ensure confidentiality at every stage of processing, this article presents an innovative strategy that merges fully homomorphic encryption (FHE) and secure hash algorithm 3 (SHA-3), offering robust protection against cyber threats. FHE and SHA-3 offer a solution to this problem. We conducted experiments using different datasets to test the performance and security strength of this hybrid approach. Our method takes into account factors such as computational overheads, complexity requirements, key management challenges, and limited support from vendors/providers offering specialized hardware/software capable of supporting complex mathematical functions required by each technique separately before combined into a hybrid form suitable enough scaled-out horizontally across different nodes within clouds themselves today globally among end-users relying upon them heavily day-to-day tasks involving mission-critical workloads running inside virtualized machines hosted on public/private clouds ecosystems worldwide. The results show that implementing FHE together with SHA-3 can significantly improve an organization's overall cyber security posture when operating critical infrastructure components residing within cloud environments. According to the results, it performs better than other methods that are currently in used complexity of encryption and decoding, including homomorphic encryption and RSA, AES & DSA, and AES & RSA. The hybrid algorithm we are proposed using fully homomorphic & SHA3 with 2048 bits 0.789 seconds of encryption time and decryption time with 0.001 seconds and execution time with 4.25 seconds. Therefore, we conclude that our proposed hybrid encryption technique is the best approach to address cyber security risks associated with storing sensitive data in cloud computing environments.

Cloud computing relies on sharing computing resources. With high availability and accessibility of resources, cloud computing is under the threat of major cyber-attacks. To detect attacks and preserve security in cloud environment, having an efficient intrusion detection system (IDS) is required. In this article, an effective and efficient IDS is proposed to maintain high level security of data in cloud. The authors have incorporated Reinforcement Learning Automata with their proposed IDS while detecting and classifying attacks. Using learning automata an effective rule set is generated with the proposed algorithm from vast training set to improve the learning process at reduced computation cost and time. After which, the proposed reinforcement learning algorithm helps in classification of attacks accurately using the reinforcement signal. This proposed model was experimented with NSL-KDD as well as KDD 10% dataset and have proved its robustness by detecting attacks more accurately being an IDS.

This Nowadays system hear that CLOUD computing is ubiquitous. Most of tech giants have converted their owned in-house data centers to using CLOUD technology and outsourcing the part of their services to public cloud and to opt for hybrid cloud architecture. There is no doubt that CLOUDs have the potential for being the next generation model computational services, but with that potential comes the risk of theft and concerns related to security and privacy. A lot of development and progress has already been made in CLOUD technologies, there still remains a wide range of concerns this article system identify and classify the main security concerns and solutions in cloud computing, and propose a method of security in cloud environment. In this paper Row Column Diagonal (RCD) based engine has been proposed. Along with the cloud sub-cloud. Data Security using engine in Environment is also explained. Further, engine system has been proposed in Central Cloud and RCD Supervisor modes in environment along with the situation of uploading and downloading. Validation and analysis have been performed for Engine and indicates based engine is much powerful and secure. As Algorithm is much more robust and easy to implement with considering all the aspects of main and sub cloud using supervisor as the central body.

Cloud computing is becoming popular because it can provide cloud consumers with IT services scaled up globally over the internet. These services include platforms, applications, and infrastructure. Moreover, cloud computing can be provided on demand and offered in different pricing packages. To schedule the task optimally in a cloud environment is considered an NP-hard problem, which has become complex with the introduction of variables such as resource dynamicity and on-demand consumer applications. The proposed research introduces a Whale Optimization Algorithm (WOA) incorporating a transfer function (TF) and a tent chaotic map to tackle scheduling challenges in cloud computing. The performance of the proposed chaotic-based whale optimization algorithm (CWOA) is compared to that of well-known metaheuristics methods. The results show that CWOA may significantly reduce the makespan of the task scheduling problem compared to standard Grey Wolf Optimizer (GWO) and BAT algorithms. Furthermore, it converges quickly as the search space grows more prominent, making it suitable for large-scale scheduling issues.

Cloud computing and big data: Technologies and applications

In the rapidly evolving landscape of digital services, cloud computing has become a cornerstone for data storage, processing, and transfer, significantly amplifying security challenges. This environment faces numerous threats and security issues, including cyberattacks, data breaches, and malware, in addition to challenges related to ensuring confidentiality, integrity, and availability. This study aims to highlight the effective role of machine learning techniques in enhancing cloud security by detecting cyber threats, protecting data privacy, and preventing attacks. It underscores the pressing need to adopt intelligent solutions, given the limitations of traditional systems in addressing the complex and evolving threats within cloud environments. Through a critical review of recent research on this topic, the study examines various machine learning methods proven effective in tackling key cloud security issues, such as network intrusion detection, financial fraud detection, and malware identification. The findings confirm that machine learning techniques significantly improve the efficiency of security systems. However, challenges remain, including adapting to continuously evolving threats, ensuring the quality of available training data, and addressing the high computational costs of real-time model operation. The study recommends further research to enhance cloud security, improve adaptability to emerging threats, and establish a theoretical foundation to guide future efforts toward strengthening security in cloud environments.

Cloud computing, thus, ensure more healthy demand for cloud services, certain measures against threats in order to protect information must be taken. In essence, the current paper seeks to extend knowledge regarding the improvement of paradigms of cloud computing with particular focus on the aspect of certification of authentic users and protection of the contents that are hosted in these environments. It mentions a new approach in the context of the authentication method that was deployed for the purpose of the AAA certification is presented there along with the watermarking and this RSA algorithms to enhance the security of the cloud file-sharing. This concept is far more effective than the conventional access policies that are top down and cal beforehand for systematic risk avoidance; hence, it helps to minimize the chances of an organization being vulnerable to various risks by right management. Iturbide’s data loss prevention technique eliminates the probability of losing sensitive information to the rest of the world as well as other unauthorized persons or organizations regaining access to privileged keys while Shamir’s secret sharing algorithm- polynomial interpolation ensures that the generation of keys is not a time-consuming exercise. In order to address the issues of persistent security threats in cloud computing paradigm, the proposed model includes computation, encryption and access to improve the overall security of cloud environment. However, these measures have been implemented, some of them can offer the best security when it comes to the ever-evolving threat. To maintain the privacy of clients’ data in the CL reinforcement learning, the paper suggests the use of homomorphic encryption for privacy-preserving RL inference. Therefore – as a result of the work – the necessity of implementing the suggested security model for the cloud computing environments as well as protecting the data and enhancing the level of trust in cloud solutions is stated. Keywords - QoS (Quality of Service), Energy Efficiency, Ontology, AAA (Authentication, Authorization, and Accounting), RSA Algorithm, RSA Algorithm, ReDCIM (Reconfigurable Digital Computing-In-Memory) Processor, tableopencache, LSTM (Long Short-Term Memory)

This research examines file protection in cloud environments using the SHA-256 algorithm. It evaluates secure hash variants in file transfer systems, aiming to improve throughput while meeting security requirements. Data analysis involved gathering and cleaning datasets, measuring encryption times, and applying statistical and graphical methods. Results show SHA-256 as an effective base for encryption, with parallel processing increasing efficiency when real-time speed is essential. The study highlights the value of transformational techniques to boost performance and recommends hybrid systems that combine SHA-256 with other algorithms for stronger security. These outcomes support further work on cryptographic methods to strengthen the safety and reliability of cloud infrastructures.