Volume 3 , Issue 1 , PP: 08-17, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Ashish Raghuwanshi 1 *
Doi: https://doi.org/10.54216/FinTech-I.030101
The ever-changing world of digital marketing makes it more important than ever to protect the integrity of brands. This study presents a novel method called "Enhanced Brand Safety Assurance through Cybersecurity Protocols" that combines three important algorithms: Ad Fraud Detection and Prevention, Real-time Behavioral Analysis, and Threat Intelligence Integration. The security of digital advertising, privacy of sensitive information, and customer confidence may all be assured with this framework's proactive threat detection and mitigation capabilities. A strong protection system against ever-changing cyber threats is created by combining the unique characteristics of each algorithm. To react to the constantly changing cybersecurity scene, the suggested solution uses adaptive thresholds, machine learning, and sophisticated analytics. When compared to more conventional approaches, the suggested solution outperforms them in terms of important efficacy indicators and practical implementation details. Experiments show that it can learn a lot, integrate AI, adapt to threats, monitor in real-time, and identify threats very well. Brands can protect themselves from the complex digital threat environment with this comprehensive and proactive cybersecurity solution that tackles the many problems associated with digital marketing.
Adaptive Defense , Ad Fraud Detection, Advanced Analytics , Artificial Intelligence , Behavioral Analysis , Brand Safety , Cybersecurity Protocols , Digital Advertising , Machine Learning , Proactive Approach , Real-time Monitoring , Reputation Preservation , Threat Intelligence.Top of Form
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[1] R. Rubiyanti, “Strategi k digital marketing ptbtjb,” Edukasi Masyarakat Sehat Sejahtera (EMaSS): JurnalPengabdiankepada Masyarakat, vol. 2, no. 1, pp. 21–29, 2020.
[2] S. Yamamura, L. Fan, and Y. Suzuki, “Assessment of urban energy performance through integration of BIM and GIS for smart city planning,” Procedia Engineering, vol. 180, no. 4, pp. 1462–1472, 2017.
[3] P. De Pelsmacker, S. Van Tilburg, and C. Holthof, “Digital marketing strategies, online reviews and hotel performance,” International Journal of Hospitality Management, vol. 72, pp. 47–55, 2018.
[4] D. Pathak and R. Kashyap, "Neural correlate-based E-learning validation and classification using convolutional and Long Short-Term Memory networks," Traitement du Signal, vol. 40, no. 4, pp. 1457-1467, 2023. [Online]. Available: https://doi.org/10.18280/ts.400414
[5] R. Kashyap, "Stochastic Dilated Residual Ghost Model for Breast Cancer Detection," J Digit Imaging, vol. 36, pp. 562–573, 2023. [Online]. Available: https://doi.org/10.1007/s10278-022-00739-z
[6] D. Bavkar, R. Kashyap, and V. Khairnar, "Deep Hybrid Model with Trained Weights for Multimodal Sarcasm Detection," in Inventive Communication and Computational Technologies, G. Ranganathan, G. A. Papakostas, and Á. Rocha, Eds. Singapore: Springer, 2023, vol. 757, Lecture Notes in Networks and Systems. [Online]. Available: https://doi.org/10.1007/978-981-99-5166-6_13
[7] L. M. Lekhanya, “An exploration of the impact of digital marketing on SMEs growth and brand popularity in rural South Africa,” Journal of Economics and Behavioral Studies, vol. 7, no. 5, pp. 37–42, 2015.
[8] J. Sulaksono, “Peranan digital marketing bagiusahamikro, kecil, dan menengah (umkm) desa tales kabupatenkediri,” Generation Journal, vol. 4, no. 1, pp. 41–47, 2020
[9] W. Ritz, M. Wolf, and S. Mcquitty, “Digital marketing adoption and success for small businesses: the application of the do-it-yourself and technology acceptance models,” The Journal of Research in Indian Medicine, vol. 13, no. 2, pp. 19–22, 2019.
[10] J. G. Kotwal, R. Kashyap, and P. M. Shafi, "Artificial Driving based EfficientNet for Automatic Plant Leaf Disease Classification," Multimed Tools Appl, 2023. [Online]. Available: https://doi.org/10.1007/s11042-023-16882-w
[11] V. Roy et al., “Detection of sleep apnea through heart rate signal using Convolutional Neural Network,” International Journal of Pharmaceutical Research, vol. 12, no. 4, pp. 4829-4836, Oct-Dec 2020.
[12] R. Kashyap, "Machine Learning, Data Mining for IoT-Based Systems," in Research Anthology on Machine Learning Techniques, Methods, and Applications, Information Resources Management Association, Ed. IGI Global, 2022, pp. 447-471. [Online]. Available: https://doi.org/10.4018/978-1-6684-6291-1.ch025
[13] M. Pouryazdan, B. Kantarci, T. Soyata, and H. Song, “Anchor-assisted and vote-based trustworthiness assurance in smart city crowdsensing,” IEEE Access, vol. 4, no. 1, pp. 529–541, 2017.
[14] A. T. Hashem, V. Chang, N. B. Anuar et al., “The role of big data in smart city,” International Journal of Information Management, vol. 36, no. 5, pp. 748–758, 2016.
[15] H. P. Sahu and R. Kashyap, "FINE_DENSEIGANET: Automatic medical image classification in chest CT scan using Hybrid Deep Learning Framework," International Journal of Image and Graphics [Preprint], 2023. [Online]. Available: https://doi.org/10.1142/s0219467825500044
[16] S. Stalin, V. Roy, P. K. Shukla, A. Zaguia, M. M. Khan, P. K. Shukla, A. Jain, "A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach," Mathematical Problems in Engineering, vol. 2021, Article ID 2942808, 11 pages, 2021. [Online]. Available: https://doi.org/10.1155/2021/2942808
[17] M. W. Libbrecht and W. S. Noble, “Machine learning applications in genetics and genomics,” Nature Reviews Genetics, vol. 16, no. 6, pp. 321–332, 2015.
