Cloud Security Research Articles

EMERGING TRENDS IN CYBERSECURITY FOR CRITICAL INFRASTRUCTURE PROTECTION: A COMPREHENSIVE REVIEW

As critical infrastructure becomes increasingly interconnected and digitized, the need for robust cybersecurity measures to safeguard essential systems is more pressing than ever. This review article explores the dynamic landscape of cybersecurity for critical infrastructure, focusing on emerging trends, current challenges, and future prospects. The historical overview delves into the evolution of cyber threats, emphasizing the need for adaptive security measures. Key components of critical infrastructure are examined, elucidating the specific challenges each sector faces. The current state of critical infrastructure cybersecurity is analyzed, with a spotlight on frameworks that guide organizations in bolstering their defenses. The heart of the review explores emerging trends in cybersecurity, covering artificial intelligence and machine learning for threat detection, IoT security, blockchain applications, and advancements in cloud computing security. Challenges and threats on the horizon, including advanced persistent threats and quantum computing implications, are scrutinized to provide insights into potential vulnerabilities.
 Keywords: Cybersecurity; Critical Infrastructure; Artificial Intelligence; Internet-of-Things; Blockchain.

Enhanced Cloud Computing Security Using Application-Based Multi-Factor Authentication (MFA) for Communication Systems

The objective of this study is to create an advanced cloud computing security system that addresses the security vulnerabilities inherent in existing authentication processes in communication systems. Specifically, the study developed an application-based multi-layer and multi-factor authentication (MFA) program that enhances cloud security measures. The system presented an improved approach to cloud computing security through the implementation of application-based multi-factor authentication (MFA). By leveraging encryption techniques, the system guarantees secure access based on user profiles. These profiles consist of valid usernames, passwords, and token numbers generated by the application. In addition, the system enhances security by integrating the Time-based One-Time Password (TOTP) algorithm (IETF RFC 6238) with location checks, augmenting the overall protection measures. A thorough testing procedure was carried out on the system, with a specific focus on a test web application hosted on the cloud server. The result validated the efficacy of all three authentication factors integrated within the application.

An improved finegrained ciphertext policy based temporary keyword search on encrypted data for secure cloud storage

We present a temporary keyword search over sensitive and confidential health data in a cloud environment. The cloud constitutes a semi-trusted domain, making it necessary for data owners to secure their data before outsourcing it through techniques like encryption. Attribute-based keyword search techniques tend to perform a search operation using a search token generated by an authorized user. These search tokens can lead to serious privacy threats, as they can extract all ciphertexts that may have been generated along with their keyword. Therefore, restricting search tokens to extract ciphertexts generated within a time interval is a more promising solution. In this paper, we present a novel ciphertext policy fine-grained temporary keyword that prevents the misuse of these search tokens. Further, it mitigates the risk of insider threats within healthcare organizations by limiting the window of opportunity for unauthorized access to minimum. To assess the security, our proposed scheme is formally proven to be secure against Selectively Chosen Keyword Attacks in the generic bilinear group model. Additionally, we demonstrate that the encryption algorithm’s complexity is linear in relation to the number of attributes. Our scheme’s significance and practicality are revealed by the performance evaluation.

Innovations in Cloud Security: Enhanced Hybrid Encryption Approach with AuthPrivacyChain for Enhanced Scalability

Innovations in Cloud Security: Enhanced Hybrid Encryption Approach with AuthPrivacyChain for Enhanced Scalability

Cloud security: sharing is caring

When it comes to the security of data in the cloud, everyone has a part to play. Cloud service providers can only do so much, and it is the responsibility of the data owners to secure the parts that the service providers can't reach. This has led to the development of the shared responsibility model (SRM).

Performance enhancement of cloud security with migration algorithm for choosing virtual machines in cloud computing

The management of the hardware as well as software resources entrusted to third-party service suppliers have shown to be extraordinarily remarkable and accessible with Cloud Computing (CC), a potential computing technology for the future. Yet, a major undertaking is characterized by security as well as the use of Virtual Machines (VMs) to perform a request. The main issue with using technology is security worries, which raises the need for a strong security system to safeguard data on the cloud. Thus, to address this problem, a method called Weighted Mean-based Radial Basis Function Network with Advanced Encryption Standard (WMRBFN-AES) is developed to improve cloud security along with the time of execution. Random keys are produced utilizing the Pseudo Random Number Generator (PRNG) technique, and the best keys are created employing the WMRBFN technique. In the WMRBFN technique, randomly generated keys are used as input, and the hidden layers yield an estimate associated with the secret key of the input. The output layer generates the most precise secret keys to encrypt the data. Encryption is carried out using the AES algorithm. The files about the encrypted data are kept in a cloud storage system. Reduced execution times for stakeholder demands (tasks) as well as optimal cloud resource utilization result from the selection of VMs, which significantly improves reliability. The Migration Algorithm (MA) is used in the cloud framework to optimize the selection of VMs to improve resource utilization and performance. The experimental findings of the suggested framework and the state-of-the-art frameworks are compared, and it was found that the new model performed better in terms of cost, resource utilization, service latency, throughput, and execution time.

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

<div>Cloud computing (CC) is a rapidly developing IT approach with intrusion detection system being a crucial tool for safeguarding virtual networks and machines from potential threats, thereby mitigating security concerns in the cloud environment. The intrusion detection system (IDS) system demands significant improvements, primarily based on optimizing performance and bolstering security measures. This research aims to implement an IDS in cloud computing utilizing deep learning (DL) method. The DL model is a promising technique and is widely used to detect intrusions. The implemented hierarchical long short-term memory (HLSTM) method’s performance is evaluated for feature selection through variance threshold-based regression (VTR) on two IDS network datasets: Bot-IoT and network security lab-knowledge discovery and data mining (NSL-KDD). This paper concludes the use of an intrusion detection network resulting in high security and performance. Moreover, the implemented method on the NSL-KDD and Bot-IoT datasets obtains respective accuracies of 99.50% and 0.995. It is compared with the existing methods namely, ensemble ID model for CC utilizing DL, LeNet, fuzzy deep neural network with a Honey Bader algorithm for privacy-preserving ID, and improved metaheuristics with a fuzzy logic-based IDS for cloud security, and beluga whale-tasmanian devil optimization based on deep convolutional neural network (CNN) with TL, chronological slap swarm algorithm-based deep belief network (DBN), and dragonfly improved invasive weed optimization-based Shepard CNN.</div>

Cyber Security; Etiology and Importance

Background: Cyber Security is to protect online data and software from cyber threats. These cyberattacks are typically intended to gain access to, change, or delete sensitive information; extort money from users; or disrupt regular corporate activities. It is difficult to keep up a regular follow up with new technologies, so it is necessary to keep the important data safe from cyber threats. There are many types of cyber threats, malware, ransomware, social engineering, phishing etc. To prevent cyber-attacks one can, use password manager tools like LastPass and others. People also use two factor authentication for double security on their accounts. Methods: Boards such as the National Institute of Standards and Technology (NIST) are developing frameworks to assist firms in understanding their security risks, improving cybersecurity procedures, and preventing cyber assaults. The fight against cybercrimes and attack, organizations needed a strong base there are 5 types of cyber securities: Critical Infrastructure Security, application security, network security, cloud security and (IoT) Security. In the modern time the US is highly based on computers and on different software, so it is really important for the US to be more conscious about the security as they get many threats almost every day for hacking their data and accounts. Results and Conclusion: Nowadays, even small businesses rarely recover their loss from the cyber-attacks and many back-off from continuing their businesses after being target of hackers. The first cybercrime attack was recorded in 1988 by a graduate student. Now that large companies and even small businesses are aware of cyber-attacks, they try their best to take every precaution to prevent hacking with double security and password manager tools.

Enhanced Feature Optimization for Multiclasss Intrusion Detection in IOT Fog Computing Environments

To overcome the shortcomings of traditional security measures in IoT fog computing, The Multiclass Intrusion Detection (MCID) model is put forward. The model's goal is to improve intrusion detection by identifying and classifying different attack types. The behavioral, temporal and anomaly features are fused through SVM-BFE for obtaining the best possible selection of high worth features. Finally we use a Random Forest algorithm to robustly classify them. There is also its adaptability to the ever-changing security demands of fog computing. MCID's ability to improve the cloud security of fog computing is shown by a 4-fold cross validation, which returns performances including precision rates up to 99.43%, recall about 95% and F-measures at as much as 97.17%. Moreover there are specificity rate totals coming in over this whole range that hit close or right.

Cloud computing security: a taxonomy, threat detection and mitigation techniques

ABSTRACT Cloud Computing is a rapidly growing emerging technology in the IT environment. Internet-based computing provides services like sharing resources e.g. network, storage, applications and software through the Internet. Cloud customers can access these services according to their need and pay only for those services which they utilize. Almost every industrial sector like banking, retail, education, healthcare, manufacturing and businesses is adopting cloud techniques within their enterprise either privately or in sharing with other enterprises. However, many security issues must be resolved because they are a barrier in adopting this fast-growing technology. This paper reviews various threats on cloud computing like distributed denial of service attack, spoofing attacks (e.g. ARP Spoofing, IP Spoofing, DNS Spoofing), man-in-the middle attack, data loss, data breach, reused IP address attack, malicious insiders, insecure interfaces & APIs etc. These security issues prevent users from adopting cloud services. This study presents various detection and prevention methods to address cloud security concerns. The effectiveness of each strategy is then analyzed based on its attributes, benefits and drawbacks. Deep learning and machine learning algorithms produce more precise findings for both known and unidentified attack patterns.

Modified DNA-based Cryptography System in the Cloud: Deep Maxout-based Fined Tuned Key Generation

Cloud security is a set of practices and tools created to address both internal and external security threats to businesses. Organizations must have cloud security as they implement their digital transformation schemes and include cloud-based tools and services in their infrastructure. Cryptography is a mechanism for preventing illegal access to data. In this paper, modified DNA-based cryptography (MDNAC), which is defined as data hiding with respect to DNA sequence is used. The steps involved in the proposed MDNAC is: encryption and decryption with optimal key generation. A way of converting plain text into cipher text is known as encryption. Two components make up the encryption process: a key and an encryption algorithm. For the encryption algorithm, we employed a modified DNA algorithm. In the decryption phase, the reverse operation is performed to get the plain text from the cipher text. Moreover, a deep learning model is used for generating the keys; the model used is deep maxout. To ensure the appropriate key generation process, the weights of the deep maxout are optimally tuned by the new feedback assisted Archimedes optimization (FAAO) algorithm. Based on the generated keys, the encryption process takes place. Finally, the performance of MDNAC is evaluated using conventional methods with respect to different measures. Additionally, the MDNAC obtained a correlation value of 0.20297 for the mean case scenario, despite the fact that the corresponding values are 0.02%, 0.17%, 0.2%, 0.7%, 0.12%,0.41%, 0.86%, and 0.46% as compared to the other models such as FAT, AOA, BMO, COOT, BOA, SSO, WOA, and LES respectively.

Machine learning-based intelligent security framework for secure cloud key management

Machine learning-based intelligent security framework for secure cloud key management

Self-Attention conditional generative adversarial network optimised with crayfish optimization algorithm for improving cyber security in cloud computing

The decentralized and distributed architecture of cloud computing promotes adoption and growth in various societal domains, including education, government, information technology, business, entertainment. Cloud computing (CC) makes a broad range of information technologies available. Security and privacy are key challenges in storing big data in the cloud. To overcome this challenge, a Self-Attention Conditional Generative Adversarial Network Optimised with Crayfish Optimization Algorithm for Improving Cyber Security in Cloud Computing (CybS-CC-SACGANCOA) is proposed in this paper to enhance the safety of CC environment. The input data is amassed from NSL-KDD database. After that, the data is fed to pre-processing segment. The segment of pre-processing eliminates Cloud data termination and missing value replacement using Reformed Phase Conserving Vibrant Range Compression filter. The results of pre-processing serves for feature selection. The optimal features are selected by means of Manta Ray Foraging Optimization Algorithm (MRFOA). Cloud data is categorized as normal and abnormal data, like Denial-of-Service (DoS), Probe, Remote to local attack (R2L), User to root attack (U2R) by the help of SACGAN. Crayfish Optimization Algorithm (COA) is proposed to optimize the SACGAN classifier that classifies anomaly data precisely. The proposed CSCC-SACGANCOA technique is activated in Python under some metrics. The proposed CSCC-SACGANCOA approach has attained higher detection accuracy, lower computation time, higher AUC, higher scalability and lower detection error rate compared to the existing methods.

Machine learning-enhanced hybrid cryptography and image steganography algorithm for securing cloud data

Ensuring data security in cloud computing is crucial due to the growing reliance on cloud-based services. Hybrid cryptography and image steganography have emerged as robust techniques to enhance data confidentiality in the cloud. In this research paper, we propose a novel algorithm, “Machine Learning-Enhanced Hybrid Cryptography and Image Steganography,” integrating these methods to provide comprehensive data protection. The algorithm employs key generation, encryption, steganography, cloud storage, data retrieval, and machine learning-based attack detection to defend against advanced cyber threats. Our experimentation demonstrates the algorithm’s effectiveness in detecting DoS attacks, data breaches, and data leakage attempts using SVM, Neural Network, Isolation Forest, and Random Forest models. The proposed approach offers broad applicability, fortifying data security and fostering further advancements in cloud security research.

Cloud Security System for ECG Transmission and Monitoring Based on Chaotic Logistic Maps

Biomedical data or information must be transmitted securely via the internet for smart healthcare. The Electrocardiogram (ECG) signal is amongst the most essential clinical signals which must be delivered to hospital facilities. Prime focus of this research is on the encryption of ECG for secure transmission. Chaos theory is used for the development of deterministic nonlinear systems, that can be used to create random numbers for the Chaotic Logistic Map (CLM) based encryption. This study describes a cryptographic algorithm for encrypting ECG signals that uses a mix of the CLM and fingerprint data. The common factor between the patient section and monitoring section is the operation on sample data points of ECG. The choice of proper encryption and decryption theme can save more amount of time and is invulnerable both to noise-based attacks and hacking instances. The proposed framework is implemented on Dropbox based cloud storage and access is possible from any given locations. Simulation tests are used to assess the system performance in terms of Structural Similarity Index Matrix (SSIM), Histogram, Spectral Distortion (SD), Correlation and Log-Likelihood Ratio (LLR). The incorporation of complex layers of CLM encryption increases security.

Identity-based remote data integrity auditing from lattices for secure cloud storage

Identity-based remote data integrity auditing from lattices for secure cloud storage

A multi-objective privacy preservation model for cloud security using hunter prey optimization algorithm

A multi-objective privacy preservation model for cloud security using hunter prey optimization algorithm

Strategic Implementation of AWS Security Services: A Focus on Best Practices, SSM and Secrets Manager

Purpose: As organizations increasingly migrate to the cloud, ensuring robust data security becomes paramount. This technical paper explores the purpose, methodology, findings, and unique contributions in cloud security, focusing on AWS Systems Manager (SSM) and Secrets Manager. The objective is to provide a comprehensive guide for engineers and architects seeking to enhance data security in their cloud environments.
 Methodology: The comparative study conducted involves analyzing the features, capabilities, and integration possibilities of AWS SSM and Secrets Manager. Real-world use cases, best practices, and security measures associated with these services are explored through a methodology that includes hands-on exploration, case studies, and a survey of existing literature to distill critical insights.
 Findings: The paper uncovers a rich landscape of security measures facilitated by AWS SSM and Secrets Manager. Findings highlight the strengths and limitations of each service, emphasizing the importance of their integration for a holistic security approach. Automated rotation of credentials, encryption options, IAM policies, and monitoring strategies emerge as critical findings, contributing to the overall understanding of secure cloud data management.
 Unique contributor to theory, policy, and practice: This work uniquely provides a detailed comparative analysis that empowers engineers and architects to make well-informed decisions. The paper offers insights into the nuances of AWS SSM and Secrets Manager, enabling professionals to tailor their security strategies based on specific use cases and requirements. The focus on real-world scenarios and identifying best practices make this contribution practical and applicable, serving as a valuable resource for those navigating the complex landscape of cloud security engineering and architecture.

Enhancing data storage for cloud security

The cloud is a pervasive technology, which is why any threat to its security raises significant concerns for an organisation. Unfortunately, too many organisations lack the in-house skills to ensure cloud security. Often, cloud services are managed by people without specific training and qualifications. To help you make sure your use of the cloud doesn't leave you at risk, this article offers five steps for improving your security.

AI-Driven Cloud Security: Examining the Impact of User Behavior Analysis on Threat Detection

This study explores the comparative effectiveness of AI-driven user behavior analysis and traditional security measures in cloud computing environments. It specifically examines their accuracy, speed, and predictive capabilities in detecting and responding to cyber threats. As reliance on cloud-based solutions intensifies, the integration of Artificial Intelligence (AI) and machine learning into cloud security has become increasingly vital. The research focuses on how AI-driven security systems, with their advanced pattern recognition and anomaly detection, compare to traditional methods in identifying deviations from standard user behaviors in cloud settings. Employing a quantitative approach, the study utilizes a detailed survey strategy, targeting cybersecurity professionals across multiple industries, including finance, healthcare, information technology, retail, and government sectors. The survey, comprising both closed-ended and Likert-scale questions, is designed to elicit nuanced responses on the perceptions and experiences of these professionals regarding AI-driven versus traditional security methods in cloud environments. The data, collected from a purposive sample of 243 cybersecurity personnel, is analyzed using multiple regression analysis. This analysis facilitates an understanding of the impact of different security systems on the efficacy of threat detection and response in cloud contexts. The results indicate that while both AI-driven and traditional methods significantly improve threat detection accuracy, traditional methods show a slight edge. Conversely, AI-driven systems demonstrate notably superior predictive capabilities and overall enhanced security performance. These findings suggest the necessity of a hybrid security strategy in cloud computing. Such an approach would combine the advanced capabilities of AI, particularly in predictive analytics and adaptability, with the rapid and reliable responses of traditional methods. This integrated strategy is proposed to effectively address the unique challenges posed by the dynamic and complex nature of cloud-based cyber threats. This study provides valuable insights for both businesses and IT professionals on the effective integration of AI-driven security measures in cloud environments. It highlights the evolving role of AI in cloud security and the importance of maintaining a balance between innovative AI approaches and established traditional methods to create a robust, comprehensive cloud security framework.