Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes

Accurate building energy prediction is useful in various applications starting from automation and management to optimal storage control. However, vulnerabilities should be considered when designing models, as intelligent attackers can deliberately influence the model performance using sophisticated attack models. These may consequently degrade accuracy, which affect efficiency of systems. In this paper, we investigate impact bi-level poisoning attacks on regression models usage obtained household appliances. Furthermore, an effective countermeasure against proposed paper. Attacks defenses are evaluated a benchmark dataset. Experimental results show that cyber-attacker poison manipulate decision. our solution successfully ensures defense such effectively compared other techniques.