# Privacy Framework for the Development of IoT-Based Systems
## 4 Related Work
### AI-Based Approaches for IoTS Security and Privacy  
Another aspect of our investigation into IoT security and privacy focuses on recent studies addressing the automatic detection through AI-based techniques of botnets and malware attacks--two of the most pervasive and disruptive threats in this domain. Effective detection of such threats is essential for safeguarding IoTS. The reviewed studies explore the application of machine learning (ML), deep learning (DL), and federated learning (FL) techniques for forensic analysis and malware detection, highlighting the growing role of these technologies as powerful tools for enhancing IoT security. [PERSON] et al. (2020) examined the application of ML and DL techniques for detecting IoT botnets, noting their high effectiveness--often surpassing traditional signature-based methods--and their significant potential to improve security. By analyzing several publicly available datasets, the study identifies key characteristics of botnet traffic and emphasizes critical challenges such as the lack of standardized benchmark datasets and the need for lightweight, real-time detection solutions suited to resource-constrained environments. The study also raises concerns about the ethical and privacy implications of the extensive data collection required by ML/DL models, which often rely on sensitive device and user information. To address these concerns, it highlights the need for privacy-preserving techniques such as FL, differential privacy, and encrypted model inference. Addressing vulnerabilities to adversarial attacks, enhancing model interpretability, and ensuring compliance with privacy regulations like GDPR are also essential for developing secure and privacy-aware ML/DL-based solutions. A study by [PERSON] et al. (2020) delved into the use of DL techniques for detecting malware in IoT environments, with a particular focus on improving forensic analysis and real-time response. This research underlines the importance of extensive, IoT-specific datasets and interdisciplinary approaches. It also addresses the growing challenge of anti-forensic tactics that obscure malware behavior. Notably, the study emphasizes the integration of FL as a method to mitigate privacy risks by decentralizing the training process and ensuring data remains on local devices. FL is presented as an efficient approach for preserving device efficiency, reducing transmission overhead, and maintaining user confidentiality--all while supporting collaborative model training. While both studies ([PERSON] et al., 2020; [PERSON] et al., 2020) emphasize the effectiveness of ML and DL in enhancing IoT security, they differ in focus and methodology. [PERSON] et al. (2020) concentrate on privacy-preserving techniques to reduce the risks associated with centralized data collection. Moreover, these authors highlight the suitability of FL for privacy-sensitive and resource-constrained IoT environments. In contrast, [PERSON] et al. (2020) take a broader perspective, emphasizing scalable detection methods, the challenge of anti-forensic behaviors, and the potential of DL models to forecast malware events by analyzing evolving threat patterns. Both studies underscore the need for real-time, lightweight models and raise concerns about the lack of high-quality, standardized IoT-specific datasets--an obstacle to effective ML/DL deployment across diverse IoT scenarios. Despite these shared themes, several critical gaps remain in the research. Both studies acknowledge the increasing complexity of IoT security and the privacy risks associated with IoTS, including challenges such as the scarcity of high-quality datasets, the difficulty of achieving real-time threat detection under resource constraints, and the vulnerability of current models to adversarial attacks and anti-forensic techniques. Building upon these insights, our research addresses these persistent challenges by introducing a comprehensive privacy framework tailored for IoTS. Significant research efforts are still required to close these gaps.