• Dasara Ravikishore
• Manas kumar yogi

False Data Injection (FDI) attacks pose a significant threat to website security, undermining the reliability of web-based services and data integrity. These attacks exploit vulnerabilities in distributed systems to inject malicious or deceptive data, resulting in misinformation and operational disruptions. Traditional centralized detection approaches often rely on aggregating data from multiple sources, which raises privacy concerns and introduces scalability challenges. To address these issues, this paper proposes a Federated Learning (FL)-based privacy-enhancing framework for detecting FDI attacks in websites. The proposed framework leverages FL to enable collaborative model training across distributed websites without transferring raw data, thus preserving user privacy. By incorporating privacy-preserving mechanisms such as differential privacy and secure aggregation, the framework ensures data confidentiality while maintaining high detection accuracy. A tailored anomaly detection model is integrated to identify FDI attack patterns effectively. Experimental results demonstrate the framework's robustness, achieving superior detection performance compared to centralized methods while ensuring privacy preservation. Furthermore, scalability tests reveal its adaptability to varying website sizes and traffic loads. This study highlights the potential of federated approaches in enhancing cybersecurity, offering a novel pathway to safeguard websites against FDI attacks while upholding user privacy. The framework sets the stage for future advancements in secure, decentralized cybersecurity solution.

"A Federated Learning-Based Privacy-Enhancing Framework for Detecting False Data Injection Attacks in Websites", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.f123-f127, November-2024, Available :http://www.jetir.org/papers/JETIR2411515.pdf

"A Federated Learning-Based Privacy-Enhancing Framework for Detecting False Data Injection Attacks in Websites", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppf123-f127, November-2024, Available at : http://www.jetir.org/papers/JETIR2411515.pdf