Effective cyber security system to secure optical data based on deep learning approach for healthcare application

Kanagala, Praneeth

doi:10.1016/j.ijleo.2022.170315

Cited by 16 publications

(3 citation statements)

References 33 publications

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“…Additionally, they extracted the most significant characteristics from NSL-KDD data set by employing binary-based particle swarm optimization. Data gain as well as principal component analysis method for detecting network intrusion was presented in work [19]. MLP, instance-based learning, and SVM classifiers were used to evaluate their method's performance, and it produced superior results.…”

Section: Cloud Environment Based Security Analysis Existing Techniquesmentioning

confidence: 99%

Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

Thomas

Gupta

Rajan

et al. 2023

Preprint

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Artificial neural networks (ANN), fuzzy inference systems, approximate reasoning, and derivative-free optimization approaches like evolutionary computation, among others, are all components of Soft Computing (SC), an innovative method for building computationally intelligent methods. Due to its widespread use and benefits, cloud computing is currently a major focus for researchers. The distributed nature of cloud computing and its complete reliance on the internet for service provision present security challenges, the most serious of which is insider Distributed Denial of Service (DDoS), which results in complete deactivation of services. Based on machine learning and the cloud, this study proposes a novel method for improving computing network security. Using a trust-based secure cloud environment and the Kernel principal component encoder architecture, the network monitoring is carried out. The soft computing environment-based detection of a cyberattack on the network is the purpose for the security enhancement. Throughput, QoS, latency, and packet delivery ratio for various monitored cyber security datasets are the focus of the experimental analysis.the proposed technique attainednetwork security analysis of 89%, throughput of 98%, QoS of 66%, latencyof 59%, packet delivery ratio of 83%.

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Section: Cloud Environment Based Security Analysis Existing Techniquesmentioning

confidence: 99%

Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

Thomas

Gupta

Rajan

et al. 2023

Preprint

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show abstract

“…An ever-increasing volume of data is being generated by IoT devices equipped with all kinds of sensors, including those embedded in smart medical devices, monitoring systems for industrial equipment, and environmental sensors for agricultural fields [ 1 , 2 ]. This deluge of data necessitates a streamlined exchange across multiple organizations, as more and more IoT applications become global in scope.…”

Section: Introductionmentioning

confidence: 99%

Sensing Data Concealment in NFTs: A Steganographic Model for Confidential Cross-Border Information Exchange

Al-Sumaidaee,

Žilić

2024

Sensors

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In an era dominated by rapid digitalization of sensed data, the secure exchange of sensitive information poses a critical challenge across various sectors. Established techniques, particularly in emerging technologies like the Internet of Things (IoT), grapple with inherent risks in ensuring data confidentiality, integrity, and vulnerabilities to evolving cyber threats. Blockchain technology, known for its decentralized and tamper-resistant characteristics, stands as a reliable solution for secure data exchange. However, the persistent challenge lies in protecting sensitive information amidst evolving digital landscapes. Among the burgeoning applications of blockchain technology, non-fungible tokens (NFTs) have emerged as digital certificates of ownership, securely recording various types of data on a distributed ledger. Unlike traditional data storage methods, NFTs offer several advantages for secure information exchange. Firstly, their tamperproof nature guarantees the authenticity and integrity of the data. Secondly, NFTs can hold both immutable and mutable data within the same token, simplifying management and access control. Moving beyond their conventional association with art and collectibles, this paper presents a novel approach that utilizes NFTs as dynamic carriers for sensitive information. Our solution leverages the immutable NFT data to serve as a secure data pointer, while the mutable NFT data holds sensitive information protected by steganography. Steganography embeds the data within the NFT, making them invisible to unauthorized eyes, while facilitating portability. This dual approach ensures both data integrity and authorized access, even in the face of evolving digital threats. A performance analysis confirms the approach’s effectiveness, demonstrating its reliability, robustness, and resilience against attacks on hidden data. This paves the way for secure data transmission across diverse industries.

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“…Establishing data retention and purge strategies: when data is no longer needed, it should be securely deleted or purged to protect the privacy of the data [7][8][9].…”

mentioning

confidence: 99%

A Novel Information Security Framework for Securing Big Data in Healthcare Environment Using Blockchain

Venugopal,

Rajaganapathi,

Birjepatil

et al. 2023

RAiSE-2023

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Effective cyber security system to secure optical data based on deep learning approach for healthcare application

Cited by 16 publications

References 33 publications

Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

Sensing Data Concealment in NFTs: A Steganographic Model for Confidential Cross-Border Information Exchange

A Novel Information Security Framework for Securing Big Data in Healthcare Environment Using Blockchain

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