A machine learning based attack detection and mitigation using a secure SaaS framework

Saisindhutheja, Reddy; Shyam, Gopal K.

doi:10.1016/j.jksuci.2020.10.005

Cited by 20 publications

(13 citation statements)

References 40 publications

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“…Later, Shyam et al [47] proposed a SaaS framework for DDoS attack mitigation based on a Deep Belief Network (DBN). The weight and activation function of the DBN is fine-tuned using the median fitness sea lion optimization technique.…”

Section: ) Communication Network Attacksmentioning

confidence: 99%

Blockchain and AI-Empowered Healthcare Insurance Fraud Detection: an Analysis, Architecture, and Future Prospects

et al. 2022

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Nowadays, health insurance has become an essential part of people's lives as the number of health issues increases. Healthcare emergencies can be troublesome for people who can't afford huge expenses. Health insurance helps people cover healthcare services expenses in case of a medical emergency and provides financial backup against indebtedness risk. Health insurance and its several benefits can face many security, privacy, and fraud issues. For the past few years, fraud has been a sensitive issue in the health insurance domain as it incurs high losses for individuals, private firms, and governments. So, it is essential for national authorities and private firms to develop systems to detect fraudulent cases and payments. A high volume of health insurance data in electronic form is generated, which is highly sensitive and attracts malicious users. Motivated by these facts, we present a systematic survey for Artificial Intelligence (AI) and blockchain-enabled secure health insurance fraud detection in this paper. This paper presents a taxonomy of various security issues in health insurance. We proposed a blockchain and AI-based secure and intelligent system to detect health insurance fraud. Then a case study related to health insurance fraud is presented.Finally, the open issues and research challenges in implementing the blockchain and an AI-empowered health insurance fraud detection system is presented.

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Section: ) Communication Network Attacksmentioning

confidence: 99%

Blockchain and AI-Empowered Healthcare Insurance Fraud Detection: an Analysis, Architecture, and Future Prospects

et al. 2022

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“…Eight attack detection methodologies were discussed with the challenges. Reddy and Shyam 25 devised a model for attack mitigation considering a secure software as a service (SaaS) model. A goal was to offer detection of attack that occurs in deep belief network (DBN), wherein weight was activation function, which was fine‐developed using median fitness oriented sea lion optimization (MFSLnO) algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

Attack detection and mitigation using optimal deep maxout network in cloud computing

Patil¹,

Patil²

2022

Adaptive Control & Signal

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Summary Cloud computing is a promising technique and the majority of information technology based services function in this infrastructure, which addresses the need of today's data center. There involve privacy and security problems in the cloud, which affected the utility of the cloud. Various mitigation solutions are developed in existing, in which the attacks continue to elevate in frequency and severity. This calls for a model for addressing the needs of challenging security threats. This article devises novel attack detection and mitigation technology using an optimization‐driven deep learning model. Here, the system comprises five modules, like data, data owner, cloud server, cloud user, and thin provision virtual volume. Here, each entity performs its own operations. The proposed technique includes five stages, namely, authentication phase, data sharing and communication phase, recorded log file creation phase, attack detection phase, and attack mitigation phase. An initial phase is the authentication phase where an authentication protocol is modeled using different mathematical functionalities. Once the authentication is done, the data sharing and communication are initiated for sharing the sensitive data. Thereafter the log file creation phase is initiated for storing the log entries. Then the attack detection is performed utilizing deep maxout network that is tuned by the developed gray wolf political optimizer (GWPO). The GWPO is the integration of gray wolf optimizer (GWO) and political optimizer (PO). Finally, attack mitigation is performed by reducing the data rates of attack nodes. The developed GWPO‐enabled deep maxout network provided improved performance with the highest accuracy of 97.9%, true positive rate of 97.3%, and true negative rate of 97.7%.

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“…Contrary to the impact of strength frames on one's perception, firms' weakness frames reflect their negative interpretation on IT-based innovation, including vulnerabilities, uncertainty, and losses as the result of using the innovation. In our context, the weakness frames evaluate the extent to which client firms believe that the use of SaaS applications has a low possibility of value creation [28]. Weakness frames in SaaS reflect the degree of client firms' misfit with vendor's service, which creates uncertainty associated with potential losses and risks, and induces efforts on adjustments and adaption to the SaaS services for value creation.…”

Section: Hypotheses Between Perceived Value and Technological Framesmentioning

confidence: 99%

Understanding the Formation of Software as a Service (Saas) Commitment the Relational Value Creation Perspective

Hsu

2021

IJIIS Int. J. Informatics Inf. Syst.

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This study focuses on the use of an important IT innovation--software-as-a-service (SaaS), and draw on sensemaking and the dedication-constraint framework to explain the formation of SaaS commitment. Building on and extending prior studies, we posit that managers' perceived relational value from SaaS consumption transforms their initial sensemaking of SaaS features into commitment. SaaS features are characterized as strength frames and weakness frames. Perceived relational values are conceptualized as process flexibility, task-knowledge coordination, process specificity, and trust. The proposed model and hypotheses are largely supported by the empirical data from 169 SaaS client firms. We discuss theoretical and practical implications.

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A machine learning based attack detection and mitigation using a secure SaaS framework

Cited by 20 publications

References 40 publications

Blockchain and AI-Empowered Healthcare Insurance Fraud Detection: an Analysis, Architecture, and Future Prospects

Blockchain and AI-Empowered Healthcare Insurance Fraud Detection: an Analysis, Architecture, and Future Prospects

Attack detection and mitigation using optimal deep maxout network in cloud computing

Understanding the Formation of Software as a Service (Saas) Commitment the Relational Value Creation Perspective

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