Floyd–Warshalls algorithm and modified advanced encryption standard for secured communication in VANET

Nova, Kannan; Umaamaheshvari, A.; Jacob, Suma Sira; Banu, G.; Balaji, M. Sundar Prakash; Srithar, S

doi:10.1016/j.measen.2023.100796

Cited by 9 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work developed VANET security and sybil attack detection. The gradient-based GBO optimizes elephants.According to testing, the proposed approach increases security by 96% and reduces encryption time by 19(s) for 100(kb) data [12]. To practice streaming data from IoT networks, forensic skills analysts are crucial.…”

Section: Literature Surveymentioning

confidence: 99%

Advanced DDos Attack Detection in SD-IoT Using DNFN and Nature-Inspired Optimizations

Kumbhar Kalpana

2024

jes

View full text Add to dashboard Cite

The purpose of this research is to improve the detection of Distributed Denial of Service (DDoS) attacks in systems that employ Software-Defined Internet of Things (SD-IoT). First, feature selection techniques such as PCA is used to improve the Deep Neuro Fuzzy Network (DNFN) model's detection accuracy of DDoS assaults. With an overall accuracy of 0.969, the findings show that the DNFN model has above average accuracy rates when applied to feature selection technique. To further improve the DDoS detection capabilities, optimization approaches like Elephant Herding Optimization (EHO) and the hybrid Elephant-Herding-Water-Cycle-Algorithm (EHWCA) are then developed. The EHWCA approach is superior than the current EHO method, as shown by a comparative study that compares the two. The DNFN model achieves an accuracy of 0.99 when optimized using EHWCA, whereas it only achieves 0.97 with EHO. The suggested system's scalability and efficiency are greatly enhanced by the inclusion of the water cycle in the optimization process. Overall, this research contributes to the development of strong cybersecurity solutions for IoT networks by demonstrating the efficacy of sophisticated optimization approaches, in particular EHWCA, in improving the detection of DDoS assaults in SD-IoT settings.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Advanced DDos Attack Detection in SD-IoT Using DNFN and Nature-Inspired Optimizations

Kumbhar Kalpana

2024

jes

View full text Add to dashboard Cite

show abstract

“…Utilizing the sun's energy, solar cells provide a sustainable means of producing electricity. Solar energy system efficiency can be significantly increased when radio wave-based wireless communication and AI-driven optimization are integrated [3]. This article explores the effects of solar cell, radio wave, and artificial intelligence integration on business and medical, aiming to give a thorough overview of the subject.…”

Section: Introductionmentioning

confidence: 99%

Synergizing Solar Cell Technology, Radio Waves, AI, and Their Impact on Business and Medicine

Rehman

2023

ijmdsa

View full text Add to dashboard Cite

This analysis examines how solar cell technology, radio waves, and artificial intelligence (AI) are transforming a variety of industries, including business and medicine. Innovation, efficiency, and sustainability are all promised by the combination of these technologies. The trip starts with a look at each technology's historical foundations and development, highlighting its contributions to the revolutions in energy production, healthcare, and communication. Radio waves have changed over time in the context of wireless communication, from early broadcasting to 5G networks, which have ushered in a new era of connectedness. The interplay between radio waves and solar cell technology is especially noticeable in energy harvesting and wireless communication, making sustainable and effective power sources for off-grid places and Internet of Things devices possible. AI's introduction enhances these technologies even more, improving signal processing and spectrum management and bringing predictive maintenance to wireless networks. The application of AI raises ethical questions, especially in the areas of data management, privacy, and security. Sustainable solutions are required due to the influence these technologies have on the environment. An alternative that uses renewable energy sources, lowers operating costs, and leaves fewer carbon footprints is the use of solar-powered AI systems. These systems are used in data centers, manufacturing, smart buildings, and agriculture, promoting sustainability and efficiency in commercial activities. The combination of solar power, artificial intelligence, and radio waves is altering drug development, improving patient care, and revolutionizing medical diagnostics in the healthcare industry. AI-assisted medical diagnostics has the ability to detect diseases early and accurately, especially in the fields of pathology and medical imaging. Telemedicine is bolstered by wearables, solar-powered medical devices, and remote diagnostics, which allow for access to healthcare even in underserved and rural places, as well as remote patient monitoring. Interoperability, energy storage options, scalability, security, and data privacy are among the ongoing challenges. The ethical application of AI in medical diagnostics is crucial, as are environmental considerations in relation to the production and disposal of equipment. Research and development, standardization, cooperation, collaboration, teaching, sustainability projects, and the creation of ethical AI frameworks are some of the future directions.

show abstract

Optimization techniques for IDS‐Generated traffic congestion control in VANET

Kumar,

Subba

2024

Internet Technology Letters

View full text Add to dashboard Cite

Vehicular Ad‐hoc Network (VANET) is an emerging field of wireless networks that enables a variety of vehicle safety and convenience applications. It employs Intrusion Detection System (IDS) frameworks in its different tiers to ensure reliable and secure communication among nodes. However, IDS requires a significant amount of data to process for monitoring intrusive activities in the network. As a result, the volume of traffic increases, resulting in the network congestion. Motivated by this fact, this study provides an overview of the optimization techniques for VANET traffic congestion control. It discusses a state‐of‐the‐art analysis along with the requirements for IDS‐generated traffic congestion control. It highlights the congestion control approaches for the traffic generated by an IDS and identifies the challenges in this domain. This study also proposes a novel IDS framework for reducing IDS‐generated network traffic by combining the Local Outlier Factor and Random Forest classifier. The proposed study achieved a high precision while yielding low false positive and false negative rates. The study outperformed the existing studies with an increase in accuracy of 1.16% and a reduction in attack detection time of 1.1869 seconds. Additionally, it discusses the possible future research directions that can be applied to address the issues of IDS‐generated traffic congestion. Overall, this study serves as a comprehensive guide to the current status of IDS‐generated traffic congestion control and diverse approaches to lessen it that can be employed by academicians and researchers.

show abstract

Floyd–Warshalls algorithm and modified advanced encryption standard for secured communication in VANET

Cited by 9 publications

References 12 publications

Advanced DDos Attack Detection in SD-IoT Using DNFN and Nature-Inspired Optimizations

Advanced DDos Attack Detection in SD-IoT Using DNFN and Nature-Inspired Optimizations

Synergizing Solar Cell Technology, Radio Waves, AI, and Their Impact on Business and Medicine

Optimization techniques for IDS‐Generated traffic congestion control in VANET

Contact Info

Product

Resources

About