Analysis of Wireless Security and Networks using COPRAS Method

The wireless networks have become integral to our daily lives, powering everything from smart phones to smart home devices. However, alongside their benefits, wireless networks also bring forth significant security challenges. Wireless networks, unlike traditional wired networks, transmit data over the airwaves, making them susceptible to various forms of unauthorized access and data breaches. Ensuring the security of these networks is crucial to safeguard sensitive information, protect user privacy, and prevent cyber-attacks. The significance of research in wireless security and networks lies in the critical role that wireless technologies play in our interconnected world. As wireless networks become increasingly prevalent in various sectors, including communication, commerce, healthcare, transportation, and more, ensuring their security becomes paramount. Research in wireless security and networks contributes to the advancement of secure and reliable wireless technologies, protecting users' privacy, data, and overall well-being in an increasingly connected world. The COPRAS-G method requires identifying selection criteria, evaluating information related to these criteria, and developing methods to evaluate Meeting the participant's needs Criteria for doing in order to assess the overall performance of the surrogate. Decision analysis involves a Decision Maker (DM) Situation to do consider a particular set of alternatives and select one among several alternatives, usually with conflicting criteria. For this reason, the developed complexity proportionality assessment (COPRAS) method can be used. A genetic algorithm; Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Differential Evolution (DE). Detection Accuracy, Resource Efficiency; Implementation Complexity; Time-to-Deployment. From the result Differential Evolution (DE) is got the first rank whereas is Ant Colony Optimization (ACO) is having the lowest rank

Publication Details

Journal
REST Journal on Data Analytics and Artificial Intelligence
Published
2024-08-13
DOI
https://doi.org/10.46632/jdaai/2/4/5
Citations
17
Primary Topic
Smart Systems and Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Analysis of Wireless Security and Networks using COPRAS Method

17 citations
REST Journal on Data Analytics and Artificial Intelligence
Smart Systems and Machine Learning
article

Analysis of Wireless Security and Networks using COPRAS Method

article en
17 citations

Abstract

The wireless networks have become integral to our daily lives, powering everything from smart phones to smart home devices. However, alongside their benefits, wireless networks also bring forth significant security challenges. Wireless networks, unlike traditional wired networks, transmit data over the airwaves, making them susceptible to various forms of unauthorized access and data breaches. Ensuring the security of these networks is crucial to safeguard sensitive information, protect user privacy, and prevent cyber-attacks. The significance of research in wireless security and networks lies in the critical role that wireless technologies play in our interconnected world. As wireless networks become increasingly prevalent in various sectors, including communication, commerce, healthcare, transportation, and more, ensuring their security becomes paramount. Research in wireless security and networks contributes to the advancement of secure and reliable wireless technologies, protecting users' privacy, data, and overall well-being in an increasingly connected world. The COPRAS-G method requires identifying selection criteria, evaluating information related to these criteria, and developing methods to evaluate Meeting the participant's needs Criteria for doing in order to assess the overall performance of the surrogate. Decision analysis involves a Decision Maker (DM) Situation to do consider a particular set of alternatives and select one among several alternatives, usually with conflicting criteria. For this reason, the developed complexity proportionality assessment (COPRAS) method can be used. A genetic algorithm; Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Differential Evolution (DE). Detection Accuracy, Resource Efficiency; Implementation Complexity; Time-to-Deployment. From the result Differential Evolution (DE) is got the first rank whereas is Ant Colony Optimization (ACO) is having the lowest rank

REST Journal on Data Analytics and Artificial IntelligenceVol. 2(4)
Decent work and economic growth
Openalex Percentile: Top 86%
Smart Systems and Machine Learning
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Analysis of Wireless Security and Networks using COPRAS Method · REST Journal on Data Analytics and Artificial Intelligence (2024) | TGRS Research Map | TGRS