IDS Using Reinforcement Learning Automata for Preserving Security in Cloud Environment

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.

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.

Security in cloud environments is paramount due to the increasing prevalence of Distributed Denial of Service (DDoS) attacks, which can severely disrupt services and cause significant financial and data losses. Traditional security mechanisms, such as Intrusion Detection Systems (IDS) and Firewalls, often struggle to detect and mitigate novel, evolving threats due to the lack of predefined detection signatures. This paper proposes a real-time signature-based detection mechanism tailored specifically for cloud environments. The proposed system generates signatures in real-time, enabling the identification and prevention of emerging DDoS attacks. The effectiveness of the solution is validated through extensive experimental evaluations, demonstrating its ability to reduce attack impact and enhance cloud security.

Intrusion Detection Systems(IDSs) are widely used to detect both known attacks and unknown attacks performed by internal and external attackers in wireless networks. However, challenging issues for developing IDSs in Wireless Mesh Networks (WMNs) are 1) supporting interoperability and 2) handling volatile parameters. In addition, security standards for WMN are still in draft stage, and to protect the WMN, IDSs of similar wireless networks such as wireless sensor, Ad-Hoc, MANET can be adopted, but the best performance is not guaranteed. In this paper, we have classified the existing IDSs for wireless networks into four categories namely single layer IDS, cross-layer IDS, reputation-based IDS, reputation based cross-layer IDS, and analyzed the performance of these IDSs with core-layer attacks and detection methodology. Based on our analysis, we address the loopholes in existing IDSs and specify research directions for strengthening the existing IDSs and for developing new efficient IDSs with respect to backbone mesh and client mesh networks.

Cloud computing provides services to the user through the Internet and is cost-efficient. It comprises of a wide range of users that makes the security of data a matter of concern. Intrusion Detection System (IDS) is a multipurpose security system either in hardware or software form applied to a network that detects abnormality in packets. Detecting intrusions involve observing any changes from normal behaviour. Cloud contains huge amount of data for which high storage space is required. To reduce the required space only relevant data is stored. This is done applying feature selection procedure. Hereby, we have proposed an IDS functioning on the concept of feature selection from NSL-KDD dataset. The efficient IDS is based on our proposed algorithm BCS-GA and gives better accuracy.

Most cloud users and officialdoms have a great interest in adoption and use cloud services like cloud storage to store their data on cloud environment. With the speedy growing in cloud computing technology, user's data security and privacy in cloud computing technology is the utmost dangerous issues because data owner stores their sensitive data onto remote cloud storage. However, successful retrieval of confidential data and computation on confidential data is hard to succeed by traditional cryptographic algorithm. Hence, to warrant the cloud security we are using the cryptographic algorithms that supports for computation over encrypted data. In this, Homomorphic cryptographic algorithm is one such technique that has interested homomorphic cryptographic algorithm used to secure user data in cloud server. It performs computation on cipher texts data can be operated directly without affecting the confidentiality of the encryption algorithms. We have established a framework for security of data on a cloud storage by using fully homomorphic cryptographic algorithm. Indeed, In this study, we have evaluated complexity of three homomorphic cryptographic algorithm those are SDC FHE, Paillier and RSA with encryption time, decryption time, throughput and memory usage

Comparative Study of Security Algorithms in Cloud Computing

The integration of cloud computing and data security systems is vital for the operational success and competitiveness of fintech startups. Cloud computing enables these startups to scale quickly, manage resources efficiently, and reduce infrastructure costs, making it an indispensable tool for businesses in the rapidly evolving fintech sector. However, with the benefits come significant challenges, particularly in data protection and cybersecurity. As fintech services handle sensitive financial data, ensuring robust security measures such as encryption, access controls, and continuous monitoring is crucial to maintaining user trust. Furthermore, regulatory compliance, both local and global, adds complexity to the data protection strategies of fintech companies. This research explores the key factors that drive cloud adoption in fintech, the security challenges associated with cloud environments, and the strategies implemented by startups to address these challenges. Interviews with IT managers from Indonesian fintech startups reveal that while cloud computing offers scalability and cost-effectiveness, issues like compliance with local regulations and the protection of sensitive data remain major concerns. The research suggests that fintech startups should invest in both cloud infrastructure and advanced cybersecurity measures to protect their operations and customer data. Additionally, creating a comprehensive roadmap for regulatory compliance and fostering partnerships with cybersecurity firms will help mitigate risks and ensure long-term success. The findings highlight the importance of integrating cloud computing with effective security strategies to navigate the complex regulatory and security landscape of the fintech industry.

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.

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.

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.

SummaryCloud computing is an active environment for accessing shared resources. Security in this environment, is one of the most important issues and concerns. An intrusion detection system is used to provide security and privacy. The intrusion detection system distinguishes between attacks and normal activities and prevents attacks on the cloud environment and notifies managers and cloud providers by creating alerts. Intrusion detection systems are usually implemented with different solutions. In this article, we present an intrusion detection system based on firefly algorithm and random forest algorithm that has the ability to detect intrusion with high accuracy in cloud computing, the results of model testing and evaluation, efficient and appropriate indicates the existence of the proposed method.

The study examined the detection of attacks against computer networks, which is becoming a harder problem to solve in the field of Network security. A problem with current intrusion detection systems is that they have many false positive and false negative events. Most of the existing Intrusion detection systems implemented depend on rule-based expert systems where new attacks are not detectable. In this study, optimization algorithms were added to intrusion detection system to make them more efficient. Self Organizing Migrating Genetic Algorithm (SOMGA) was integrated into intrusion detection system to obtain a more efficient intrusion detection system called ID-SOMGA. This study provides an equally efficient method to implement an intrusion detection system that returns very low false positives. Due to the complexities involved in security issues, and the implementation of the work, selected values of the network log was used to implement the system in order to reduce some of these complexities. The Self Organizing Migrating Genetic Algorithm – Intrusion Detection System was tested and values of the result were compared with that of an IDS with Genetic Algorithm Intrusion Detection System. In terms of detection rates, ID-SOMGA was found to be slower than an IDS with GA, the false positives in ID-SOMGA was lower than what obtains with genetic algorithm. Both schemes were able to identify new patterns almost in the same way. The ID-SOMGA system that was developed improved the security of systems in networked settings allowing for confidentiality, integrity and availability of system resources.

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.

Abstract— In the evolving digital landscape, cloud computing has become a cornerstone for scalable and flexible IT infrastructure. However, the rapid adoption of cloud services introduces a wide range of security threats, compliance challenges, and operational risks. This paper, From Threats to Trust: Securing Cloud Infrastructure through Resilience and Compliance presents a comprehensive analysis of cloud security challenges and explores effective mitigation strategies to enhance resilience and regulatory adherence. The study examines critical issues such as data breaches, insider threats, vendor lock-in, insecure interfaces, and distributed denial-of-service (DDoS) attacks within cloud environments. It further highlights the importance of robust security mechanisms, including identity and access management, encryption techniques, continuous monitoring, intrusion detection systems, and secure key management. Emphasis is also placed on compliance with global data protection regulations and the implementation of governance frameworks. By integrating best practices with proactive risk management approaches, this paper provides a structured framework for building secure, resilient, and compliant cloud infrastructures. The findings aim to support organizations in strengthening their security posture, ensuring data integrity and availability, and fostering trust in cloud-based systems. Keywords— Cloud Security, Cloud Computing, Identity and Access Management and Intrusion Detection Systems.