Kwame Opuni-Boachie Obour Agyekum
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The paper systematically evaluates eight privacy-preserving techniques for LLM requests, finding that a combination of local inference, redaction, and semantic rephrasing provides the best overall protection.
This paper analyzes how vulnerable various machine learning models are to data poisoning attacks in IoT intrusion detection, finding that ensemble methods are more robust than Logistic Regression and Deep Neural Networks.
Papers
Robustness Analysis of Machine Learning Models for IoT Intrusion Detection Under Data Poisoning Attacks
Fortunatus Aabangbio Wulnye, Justice Owusu Agyemang, Kwame Opuni-Boachie Obour Agyekum, Kwame Agyeman-Prempeh Agyekum +2 more
This paper analyzes how vulnerable various machine learning models are to data poisoning attacks in IoT intrusion detection, finding that ensemble methods are more robust than Logistic Regression and…