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Author

G. Suseendran

Bio: G. Suseendran is an academic researcher from Vels University. The author has contributed to research in topics: Cellular network & Network security. The author has co-authored 3 publications.

Papers
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Book ChapterDOI
01 Jan 2021
TL;DR: This paper will use the CS dataset, and ML techniques will be applied to these datasets to identify the issues, opportunities, and cybersecurity challenges, and provided a framework that will provide insight into ML and DS’s use for protecting cyberspace from CS attacks.
Abstract: Cybersecurity (CS) is one of the critical concerns in today’s fast-paced and interconnected world. Advancement in IoT and other computing technologies had made human life and business easy on one hand, while many security breaches are reported daily. These security breaches cost millions of dollars loss for individuals as well as organizations. Various datasets for cybersecurity are available on the Internet. There is a need to benefit from these datasets by extracting useful information from them to improve cybersecurity. The combination of data science (DS) and machine learning (ML) techniques can improve cybersecurity as machine learning techniques help extract useful information from raw data. In this paper, we have combined DS and ML for improving cybersecurity. We will use the CS dataset, and ML techniques will be applied to these datasets to identify the issues, opportunities, and cybersecurity challenges. As a contribution to research, we have provided a framework that will provide insight into ML and DS’s use for protecting cyberspace from CS attacks.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article , an emergency vehicle management solution (EVMS) is proposed to determine an efficient vehicle-passing sequence that allows the EV to cross a junction without any delay.
Abstract: An emergency can occur at any time. To overcome that emergency efficiently, we require seamless movement on the road to approach the destination within a limited time by using an Emergency Vehicle (EV). This paper proposes an emergency vehicle management solution (EVMS) to determine an efficient vehicle-passing sequence that allows the EV to cross a junction without any delay. The proposed system passes the EV and minimally affects the travel times of other vehicles on the junction. In the presence of an EV in the communication range, the proposed system prioritizes the EV by creating space for it in the lane adjacent to the shoulder lane. The shoulder lane is a lane that cyclists and motorcyclists will use in normal situations. However, when an EV enters the communication range, traffic from the adjacent lane will move to the shoulder lane. As the number of vehicles on the road increases rapidly, crossing the EV in the shortest possible time is crucial. The EVMS and algorithms are presented in this study to find the optimal vehicle sequence that gives EVs the highest priority. The proposed solution uses cutting-edge technologies (IoT Sensors, GPS, 5G, and Cloud computing) to collect and pass EVs’ information to the Roadside Units (RSU). The proposed solution was evaluated through mathematical modeling. The results show that the EVMS can reduce the travel times of EVs significantly without causing any performance degradation of normal vehicles.

28 citations

Proceedings ArticleDOI
16 Feb 2022
TL;DR: In this article , the authors used Logistic Regression algorithm over Gaussian algorithm to detect spam content over the internet and social media using Logistic regression algorithm over GAussian algorithm.
Abstract: Aim: To detect the spam content over the internet and social media using Logistic Regression algorithm over Gaussian algorithm. Methods and Materials: Detection of spam content messages are performed using Logistic Regression algorithm and Gaussian algorithm (sample size=20) Where values are taken randomly. G-power was maintained to be 80%. Results and Discussion: This article is an attempt to improve the accuracy of spam content detection using the Logistic Regression algorithm, a machine learning algorithm. The AI based Application avoids overfitting. The proposed model has improved accuracy of 95% with p value which is less than 0.03(p<0.05) in spam detection than Gaussian algorithm having accuracy of 93%. Conclusion: The outcomes of the proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed algorithm seems to have higher accuracy than the Gaussian algorithm.

1 citations

Proceedings ArticleDOI
12 Aug 2022
TL;DR: This review research used content analysis to clarify the security risks of 5G and GPS in agriculture, such as the passive and active attacks, 5G architecture core network risks, network access risks, hardware risks etc. from 5G technology, and the disruption of position and timing systems, confidential data loss etc from GPS technology in agriculture.
Abstract: As the big data era comes, the rapid development of ICT and IoT promise more and more efficient technologies in all industries. The use of 5G and GPS is also becoming more and more popular in agriculture around the world. 5G and GPS are providing optimal ways to realize more resilient agriculture and create more agricultural yields. However, each new technology has risks in its application. The improper use of 5G and GPS may result in further catastrophic loss and potential risks. The first step to better leverage the benefits of 5G and GPS in agriculture is to point out the security risks. This review research used content analysis to clarify the security risks of 5G and GPS in agriculture, such as the passive and active attacks, 5G architecture core network risks, network access risks, hardware risks etc. from 5G technology, and the disruption of position and timing systems, confidential data loss etc. from GPS technology in agriculture. Besides, both 5G and GPS have the technology immaturity and high-cost constraints to be adopted in agriculture. And we also provide suggestions for further research about 5G and GPS adoption in agriculture.

1 citations

Proceedings ArticleDOI
16 Feb 2022
TL;DR: This article is an attempt to improve the accuracy of spam content detection using the Logistic Regression algorithm, a machine learning algorithm that seems to have higher accuracy than the Gaussian algorithm.
Abstract: Aim: To detect the spam content over the internet and social media using Logistic Regression algorithm over Gaussian algorithm. Methods and Materials: Detection of spam content messages are performed using Logistic Regression algorithm and Gaussian algorithm (sample size=20) Where values are taken randomly. G-power was maintained to be 80%. Results and Discussion: This article is an attempt to improve the accuracy of spam content detection using the Logistic Regression algorithm, a machine learning algorithm. The AI based Application avoids overfitting. The proposed model has improved accuracy of 95% with p value which is less than 0.03(p<0.05) in spam detection than Gaussian algorithm having accuracy of 93%. Conclusion: The outcomes of the proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed algorithm seems to have higher accuracy than the Gaussian algorithm.

1 citations