Energy efficiency maximization algorithm for underwater Mobile sensor networks

Pasupathi, Subbulakshmi; Vimal, S.; Robinson, Y. Harold; Khari, Manju; Verdú, Elena; Crespo, Rubén González

doi:10.1007/s12145-020-00478-1

Cited by 15 publications

(2 citation statements)

References 42 publications

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“…Electronic data interchange (EDI) method is constructed by introducing new intermediate system between old and new system, and data can be transferred without any modification in source and new system [26][27][28][29] (Fig. 8).…”

Section: B Data Transfer Via Intermediate System (Methodology 2)mentioning

confidence: 99%

Study and Develop an Effective Interfaces to Upload Data into Enterprise Resource Planning System

Robinson

Julie

Raja

et al. 2021

Studies in Big Data

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In today's fast-growing world, day-to-day several applications have specialized features that new features have failed to enhance the satisfaction of the customers in web interfaces and user interfaces. So when an organization goes for application change or database change or need of data from other system, they need to go for major data transfer without any data loss. In this paper, we proposed a targetbased data transformation. The proposed work performs an efficient data transfer between source and designation system and ensures data transfer without any loss and with proper validation based on designation system data model.

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Section: B Data Transfer Via Intermediate System (Methodology 2)mentioning

confidence: 99%

Study and Develop an Effective Interfaces to Upload Data into Enterprise Resource Planning System

Robinson

Julie

Raja

et al. 2021

Studies in Big Data

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“…Provided that these outlines follow the information on common network practices, the anomaly-based detection system is able to identify obscure attacks. Overall, it is accepted in the research field [22]. A wide variety of anomaly-based detection frameworks have been implemented since the most current decade.…”

Section: Motivation and Gapsmentioning

confidence: 99%

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

Sanjalawe¹,

Althobaiti²

2023

Computers, Materials &Amp; Continua

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Intrusion Detection System (IDS) in the cloud Computing (CC) environment has received paramount interest over the last few years. Among the latest approaches, Deep Learning (DL)-based IDS methods allow the discovery of attacks with the highest performance. In the CC environment, Distributed Denial of Service (DDoS) attacks are widespread. The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic, resulting in financial losses. Although various researchers have proposed many detection techniques, there are possible obstacles in terms of detection performance due to the use of insignificant traffic features. Therefore, in this paper, a hybrid deep learning mode based on hybridizing Convolutional Neural Network (CNN) with Long-Short-Term Memory (LSTM) is used due to its robustness and efficiency in detecting normal and attack traffic. Besides, the ensemble feature selection, mutualization aggregation between Particle Swarm Optimizer (PSO), Grey Wolf Optimizer (PSO), Krill Hird (KH), and Whale Optimization Algorithm (WOA), is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC. A benchmark dataset proposed by the Canadian Institute of Cybersecurity (CIC), called CICIDS 2017 is used to evaluate the proposed IDS. The results revealed that the proposed IDS outperforms the state-of-the-art IDSs, as it achieved 97.9%, 98.3%, 97.9%, 98.1%, respectively. As a result, the proposed IDS achieves the requirements of getting high security, automatic, efficient, and self-decision detection of DDoS attacks.

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