Strengthening IoT Resilience: A Study on Backdoor Malware and DNS Spoofing Detection Methods
Devpriya Panda, Neelamadhab Padhy, Kavita Sharma · 2025
This research focuses on discovering backdoor malware and DNS spoofing in IoT networks, as well as threats common in the use of IoT solutions. In this paper, previous IoT cyber attacks are reviewed to explain how data from the networks can be used in recognizing such an attack in advance, as well as the consequences of the attacks in the functioning of the IoT framework and its ability to rebound. The evaluation of detection accuracy included three models: Random Forest, Decision Tree, and Logistic Regression. The Random Forest model exhibited the best results with 95 percent accuracy and 94 percent precision, which would help deal with both types of threats.