Enhancing privacy‐preserving mechanisms in Cloud storage: A novel conceptual framework

Mishra, Alok; Jabar, Thr Satar; Alzoubi, Yehia Ibrahim; Mishra, Kamta Nath

doi:10.1002/cpe.7831

Cited by 5 publications

(2 citation statements)

References 83 publications

(266 reference statements)

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“…Notably, an action document has been published, which features the official positions of over 50 countries on cyberspace, cybercrime, or cybersecurity. This demonstrates the growing recognition and efforts to address cybersecurity challenges internationally [17]. Multiple systems should be employed simultaneously to provide cybersecurity since the chances of an attack are growing as Internet usage grows.…”

Section: Cybersecurity On Twittermentioning

confidence: 98%

Social Media Zero-Day Attack Detection Using TensorFlow

Topcu,

Alzoubi,

Elbasi

et al. 2023

Electronics

Self Cite

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In the current information era, knowledge can pose risks in the online realm. It is imperative to proactively recognize potential threats, as unforeseen dangers cannot be eliminated entirely. Often, malware exploits and other emerging hazards are only identified after they have occurred. These types of risks are referred to as zero-day attacks since no pre-existing anti-malware measures are available to mitigate them. Consequently, significant damages occur when vulnerabilities in systems are exploited. The effectiveness of security systems, such as IPS and IDS, relies heavily on the prompt and efficient response to emerging threats. Failure to address these issues promptly hinders the effectiveness of security system developers. The purpose of this study is to analyze data from the Twitter platform and deploy machine learning techniques, such as word categorization, to identify vulnerabilities and counteract zero-day attacks swiftly. TensorFlow was utilized to handle the processing and conversion of raw Twitter data, resulting in significant efficiency improvements. Moreover, we integrated the Natural Language Toolkit (NLTK) tool to extract targeted words in various languages. Our results indicate that we have achieved an 80% success rate in detecting zero-day attacks by using our tool. By utilizing publicly available information shared by individuals, relevant security providers can be promptly informed. This approach enables companies to patch vulnerabilities more quickly.

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Section: Cybersecurity On Twittermentioning

confidence: 98%

Social Media Zero-Day Attack Detection Using TensorFlow

Topcu,

Alzoubi,

Elbasi

et al. 2023

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…One major worry that could cause a data breach and delay responding to an attack is the lack of insight into cloud infrastructure. Mishra A. et al (2023) discussed advanced mechanisms for enhancing cloud data security, including privacypreserving attributes, cyber vaulting, intelligent threat detection approaches, and machine learning privacy-preserving features can be used to provide a flexible and efficient framework for cloud data storage and privacy.…”

Section: Advancement In Authentication Mechanismsmentioning

confidence: 99%

Enhancing cloud data security: User-centric approaches and advanced mechanisms

Aslam,

Kumar

2024

The Scientific Temper

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Cloud storage has led to a transformative era of data management for organizations, but this paradigm shift has also introduced critical security challenges. This paper is motivated by the urgent need to strengthen cloud data security against unauthorized access and breaches. Our investigation revolves around the vulnerabilities stemming from distributed links in cloud storage, shedding light on the paramount importance of safeguarding crucial commercial records. These challenges encompass the menace posed by both external attackers and unscrupulous customers, amplified within the complex landscape of multi-tenant architectures. This work presents the Secure-Cloud-Guard algorithm to achieve these goals—a multifaceted approach integrating encryption, biometric authentication, and MAC address security mechanisms. The Secure-Cloud-Guard algorithm, rooted in the user-centric paradigm, enhances data protection in cloud storage environments by orchestrating multiple layers of security. The algorithm’s simulation involves thorough evaluation through encryption performance analysis, biometric authentication testing, and MAC address security validation. The simulation results reveal the effectiveness of our proposed algorithm. Encryption performance metrics showcase the encryption throughput and latency, thereby gauging the efficiency of the encryption process. The biometric authentication simulation calculates the false acceptance rate (FAR) to determine the algorithm’s accuracy and false rejection rate (FRR). The simulation of MAC address security illustrates the algorithm’s ability to authenticate devices through MAC addresses, providing insights into its authentication success rate (ASR). In conclusion, an, aligning with user-centric approaches and incorporating advanced mechanisms, such as encryption, biometric authentication, MAC address security, contribute to the ongoing efforts of fortifying the security landscape of cloud storage and computing

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Research trends in deep learning and machine learning for cloud computing security

Alzoubi,

Mishra,

Topcu

2024

Artif Intell Rev

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Deep learning and machine learning show effectiveness in identifying and addressing cloud security threats. Despite the large number of articles published in this field, there remains a dearth of comprehensive reviews that synthesize the techniques, trends, and challenges of using deep learning and machine learning for cloud computing security. Accordingly, this paper aims to provide the most updated statistics on the development and research in cloud computing security utilizing deep learning and machine learning. Up to the middle of December 2023, 4051 publications were identified after we searched the Scopus database. This paper highlights key trend solutions for cloud computing security utilizing machine learning and deep learning, such as anomaly detection, security automation, and emerging technology's role. However, challenges such as data privacy, scalability, and explainability, among others, are also identified as challenges of using machine learning and deep learning for cloud security. The findings of this paper reveal that deep learning and machine learning for cloud computing security are emerging research areas. Future research directions may include addressing these challenges when utilizing machine learning and deep learning for cloud security. Additionally, exploring the development of algorithms and techniques that comply with relevant laws and regulations is essential for effective implementation in this domain.

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Enhancing privacy‐preserving mechanisms in Cloud storage: A novel conceptual framework

Cited by 5 publications

References 83 publications

Social Media Zero-Day Attack Detection Using TensorFlow

Social Media Zero-Day Attack Detection Using TensorFlow

Enhancing cloud data security: User-centric approaches and advanced mechanisms

Research trends in deep learning and machine learning for cloud computing security

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