• Radhika Kulkarni
• Sana Samreen
• Vinay Warad

A security system in a smart home should have the capability to detect any existing or upcoming threats from the network, devices, sensors, or users. In this paper, we use machine learning algorithm such as random forest for intrusion detection. In particular, we discover anomalous activities that can occur in a smart home environment by using a two-tiered system. We use the dataset and run multiple machine learning classification models such as the random forest, xgboost, and decision tree. The algorithms are trained on the decision layer, and our experiments show that each of the models achieve a different level of accuracy in identifying possible anomalies that indicate attacks. The accuracy level achieved by our approach is promising for its practical implementation in a smart home system.

Anomaly detection,smart home systems, behavioral patterns, security, challenges, threats.

"Efficient Two-Tier Intrusion Detection System for Smart Home Security", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.b394-b399, February-2024, Available :http://www.jetir.org/papers/JETIR2402143.pdf

"Efficient Two-Tier Intrusion Detection System for Smart Home Security", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppb394-b399, February-2024, Available at : http://www.jetir.org/papers/JETIR2402143.pdf