Mythili Boopathi, Meena Chavan, Jeneetha Jebanazer J. and Sanjay Nakharu Prasad Kumar
The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that…
Abstract
Purpose
The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method.
Design/methodology/approach
This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques.
Findings
The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively.
Originality/value
The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.
Details
Keywords
Manpreet Singh, Urvashi Tandon and Amit Mittal
The purpose of this paper is to identify the antecedents of continued usage intentions in the connected devices ecosystem in health care by analyzing the users' and physicians'…
Abstract
Purpose
The purpose of this paper is to identify the antecedents of continued usage intentions in the connected devices ecosystem in health care by analyzing the users' and physicians' expectations in a new ecosystem where one prefers to connect digitally rather than physically.
Design/methodology/approach
This is a unique study in which data was collected from 242 doctors and 215 end-users to gauge the expectations from the connected devices in health care. Further, these responses were hypothesised using UTAUT-2 and ECT theories to analyze general users’ and professional users’ or doctors’ expectations for continued usage in connected devices ecosystem in the health-care ecosystem.
Findings
Performance expectancy, social influence, facilitating conditions and price value emerged as significant predictors of satisfaction in both user groups. But habit and hedonic motivation reflected an insignificant impact on user satisfaction. Surprisingly, effort expectancy emerged as a significant factor for end-user satisfaction, and this became insignificant for professional user satisfaction. Satisfaction was positively related to continued usage for both user groups, and app quality has a positive impact on all the predictors.
Practical implications
To the best of the authors’ knowledge, this is the first comparative study to understand the factors which influence consumer behavior leading to a holistic model and can be imbibed for creating a better customer experience in an era where we are more comfortable connecting digitally rather than physically.
Originality/value
This study has used the Unified Theory of Acceptance and Use of Technology-2 model and expectation confirmation theory to analyze the key factors influencing the intentions for continued usage of devices in the Internet of Medical Devices setup.
Details
Keywords
Amit Rana, Sandeep Deshwal, Rajesh and Naveen Hooda
The weld joint mechanical properties of friction stir welding (FSW) are majorly reliant on different input parameters of the FSW machine. The study and optmization of these…
Abstract
Purpose
The weld joint mechanical properties of friction stir welding (FSW) are majorly reliant on different input parameters of the FSW machine. The study and optmization of these parameters is uttermost requirement and aim of this study to increase the suitability of FSW in different manufacturing industries. Hence, the input parameters are optimized through different soft computing methods to increase the considered objective in this study.
Design/methodology/approach
In this research, ultimate tensile strength (UTS), yield strength (YS) and elongation (EL) of FSW prepared butt joints of AA6061 and AA5083 Aluminium alloys materials are investigated as per American Society for Testing and Materials (ASTM E8-M04) standard. The FSW joints were prepared by changing the three input process parameters. To develop experimental run order design matrix, rotatable central composite design strategy was used. Furthermore, genetic algorithm (GA) in combination (Hybrid) with response surface methodology (RSM), artificial neural network (ANN), i.e. RSM-GA, ANN-GA, is exercised to optimize the considered process parameters.
Findings
The maximum value of UTS, YS and EL of test specimens on universal testing machine was measured as 264 MPa, 204 MPa and 14.41%, respectively. The most optimized results (UTS = 269.544 MPa, YS = 211.121 MPa and EL = 17.127%) are obtained with ANN-GA for the considered objectives.
Originality/value
The optimization of input parameters to increase the output objective values using hybrid soft computing techniques is unique in this research paper. The outcomes of this study will help the FSW using manufacturing industries to choose the best optimized parameters set for FSW prepared butt joint with improved mechanical properties.