1
Professor and Head, Dept. of ECE, Rajalakshmi Engineering College, Chennai, TN, India
(bhagyalakshmi.l@rajalaksahmi.edu.in)
Abstract :
The SecureDigitalGuard framework gets recognition for its all-encompassing strategy, which combines strict consumer data protection laws with state-of-the-art security safeguards with ease. This all-encompassing approach is designed to guarantee the longevity of digital marketing in the face of constantly changing cyberthreats. This cutting-edge system is based on three key strategies: the Behavioural Threat Detection (BTD) algorithm, the Adaptive Access Control (AAC) algorithm, and the Homomorphic Privacy Guard (HPG) programme. The vital task of dynamically controlling user access levels in response to continuing risk evaluations is taken on by the AAC algorithm. This dynamic control technique improves the framework's capacity to adjust to constantly shifting security circumstances. However, the BTD algorithm is proactive in spotting abnormalities in user behaviour, allowing for quick reactions to any dangers. The SecureDigitalGuard architecture gains an additional degree of protection from this preventive method. In addition, the HPG programme is responsible for doing analytics while maintaining user privacy. This careful approach shows a dedication to finding a fine balance between user protection and data analysis, making sure the framework complies with the strictest privacy regulations. Test results provide empirical evidence that SecureDigitalGuard is effective and that it can keep up with the dynamic and often changing nature of cyber threats. As a result, the architecture makes traditional cybersecurity techniques outdated. In an increasingly complex and dynamic cybersecurity world, SecureDigitalGuard provides a strong solution for protecting digital marketing through the seamless integration of state-of-the-art technology and strict adherence to privacy regulations.
Keywords :
Access Control; Adaptive; Algorithm; Anomaly Detection; Behavioral Threat Detection; Consumer Data Protection; Cybersecurity; Digital Marketing; Homomorphic Privacy Guard; Privacy-Preserving Analytics; Proposed Method; Robust.
References :
[1] C. Moore, “Detecting ransomware with honeypot techniques,” in Proceedings of the 2016 Cybersecurity and Cyberforensics Conference (CCC), pp. 77–81, IEEE, Amman, Jordan, August 2016.
[2] Kharraz and E. Kirda, “Redemption: real-time protection against ransomware at end-hosts,” International Symposium on Research in Attacks, Intrusions, and Defenses, Springer, Cham, Switzerland, Europe, 2017.
[3] L. Karthikeyan, G. Jacob, and B. Manjunath, “Malware images: visualization and automatic classification,” in Proceedings of the 8th International Symposium on Visualization for Cyber Security, no. 4, Pittsburgh, PA, USA, July 2011.
[4] M. Bathre and A. Sahelay, "Energy efficient route discovery algorithm for MANET," Int J Eng Res Technol (IJERT), vol. 2, no. 7, pp. 1291–1295, 2013.
[5] Agustono, M. Asrol, A. S. Budiman, E. Djuana, and F. E. Gunawan, "State of Charge Prediction of Lead Acid Battery using Transformer Neural Network for Solar Smart Dome 4.0," Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 10, pp. 1-10, 2022.
[6] A. Clarin, "Comparison of the Performance of Several Regression Algorithms in Predicting the Quality of White Wine in WEKA," Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 7, pp. 20-26, 2022.
[7] M. Bathre and P. K. Das, "Hybrid Energy Harvesting for Maximizing Lifespan and Sustainability of Wireless Sensor Networks: A Comprehensive Review & Proposed Systems," in Proc. 2020 Int. Conf. on Computing, Intelligence and Smart Power System for Sustainable Energy (CISPSSE), Keonjhar, India, 2020, pp. 1–6, DOI: 10.1109/CISPSSE49931.2020.9212287.
[8] M. Guerroum, M. Zegrari, M. Masmoudi, M. Berquedich, and A. A. Elmahjoub, "Machine Learning Technics for Remaining useful Life Prediction using Diagnosis Data: a Case Study of a Jaw Crusher," Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 10, pp. 122-135, 2022.
[9] M. Bathre and P. K. Das, "Review on an Energy Efficient, Sustainable and Green Internet of Things," in Proc. 2nd Int. Conf. on Data Engineering and Applications (IDEA), Bhopal, India, 2020, pp. 1–6, DOI: 10.1109/IDEA49133.2020.9170736.
[10] M. A. Habib, M. Ahmad, S. Jabbar, S. H. Ahmed, and J. J. P. C. Rodrigues, “Speeding up the internet of things: Leaiot: A lightweight encryption algorithm toward low-latency communication for the internet of things,” IEEE Consumer Electronics Magazine, vol. 7, no. 6, pp. 31–37, 2018.
[11] D. Ucci, L. Aniello, and R. Baldoni, “Survey of machine learning techniques for malware analysis,” Computers & Security, vol. 81, pp. 123–147, 2019.
[12] K. Rieck, P. Trinius, C. Willems, and T. Holz, “Automatic analysis of malware behavior using machine learning,” Journal of Computer Security, vol. 19, no. 4, pp. 639–668, 2011.
[13] 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.
[14] Z. Zhao, J. Wang, and J. Bai, “Malware detection method based on the control‐flow construct feature of software,” IET Information Security, vol. 8, no. 1, pp. 18–24, 2014.
[15] S. Cesare, Y. Xiang, and W. Zhou, “Control flow-based malware VariantDetection,” IEEE Transactions on Dependable and Secure Computing, vol. 11, no. 4, pp. 307–317, 2014.
[16] C. T. Lin, N. J. Wang, H. Xiao, and C. Eckert, “Feature selection and extraction for malware classification,” Journal of Information Science and Engineering, vol. 31, no. 3, pp. 965–992, 2015.
[17] J. B. Park, K. S. Han, T. G. Kim, and E. G. Im, “A study on selecting key Opcodes for malware classification and its Usefulness,” Journal of KIISE, vol. 42, no. 5, pp. 558–565, 2015.
[18] 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
[19] S. Ruan, R. Mehmood, A. Daud, H. Dawood, and J. S. Alowibdi, “An adaptive method for clustering by fast search-and-find of density peaks: adaptive-dp,” in Proceedings of the 26th International Conference on World Wide Web Companion, pp. 119–127, Perth, Australia, April 2017.
[20] Arowolo MO, Fayose FT, Ade-Omowaye JA, Adekunle AA, Akindele SO (2022) Design and Development of an Energy-efficient Audio-based Repellent System for Rice Fields. Int J Emerg Technol Adv Eng 12(10):82–94
[21] V. Tiwari et al., "Real-time soybean crop insect classification using customized deep learning models," in Data Management, Analytics and Innovation: Proc. of ICDMAI 2021, vol. 1, Singapore, 2021, pp. 143-156, Springer Singapore.
[22] Bhujade RK, Asthana S (2022) An Extensive Comparative Analysis on Various Efficient Techniques for Image Super-Resolution. Int J Emerg Technol Adv Eng 12(11):153–158
[23] K. Shubham, V. Tiwari, and K. S. Patel, "Predictive Learning Methods to Price European Options Using Ensemble Model and Multi-asset Data," International Journal on Artificial Intelligence Tools, 2023.
| Style | # |
|---|---|
| MLA | L. Bhagyalakshmi. "Securing the Future of Digital Marketing through Advanced Cybersecurity Approaches and Consumer Data Protection Privacy and Regulatory Compliance." Journal of Cybersecurity and Information Management, Vol. 13, No. 1, 2024 ,PP. 17-27 (Doi : https://doi.org/10.54216/JCIM.130102) |
| APA | L. Bhagyalakshmi. (2024). Securing the Future of Digital Marketing through Advanced Cybersecurity Approaches and Consumer Data Protection Privacy and Regulatory Compliance. Journal of Journal of Cybersecurity and Information Management, 13 ( 1 ), 17-27 (Doi : https://doi.org/10.54216/JCIM.130102) |
| Chicago | L. Bhagyalakshmi. "Securing the Future of Digital Marketing through Advanced Cybersecurity Approaches and Consumer Data Protection Privacy and Regulatory Compliance." Journal of Journal of Cybersecurity and Information Management, 13 no. 1 (2024): 17-27 (Doi : https://doi.org/10.54216/JCIM.130102) |
| Harvard | L. Bhagyalakshmi. (2024). Securing the Future of Digital Marketing through Advanced Cybersecurity Approaches and Consumer Data Protection Privacy and Regulatory Compliance. Journal of Journal of Cybersecurity and Information Management, 13 ( 1 ), 17-27 (Doi : https://doi.org/10.54216/JCIM.130102) |
| Vancouver | L. Bhagyalakshmi. Securing the Future of Digital Marketing through Advanced Cybersecurity Approaches and Consumer Data Protection Privacy and Regulatory Compliance. Journal of Journal of Cybersecurity and Information Management, (2024); 13 ( 1 ): 17-27 (Doi : https://doi.org/10.54216/JCIM.130102) |
| IEEE | L. Bhagyalakshmi, Securing the Future of Digital Marketing through Advanced Cybersecurity Approaches and Consumer Data Protection Privacy and Regulatory Compliance, Journal of Journal of Cybersecurity and Information Management, Vol. 13 , No. 1 , (2024) : 17-27 (Doi : https://doi.org/10.54216/JCIM.130102) |