Evasion Attack against CNN-Adaboost Electricity Theft Detection in Smart Grid
Santosh Nirmal, Pramod Ravindra Patil · 2024
As deep learning models become more prevalent in smart grid systems, ensuring their accuracy in tasks like identifying abnormal customer behavior is increasingly important. As its use is increased in smart grids to detect energy theft, crafting adversarial data by attackers to deceive the model to get the desired output is also increased. Evasion attacks (EA) attempt to evade detection by misclassifying input data during testing. The manipulation of data inputs is done so that it is not noticeable to humans but can cause the machine learning (ML) model to produce incorrect results. Electricity theft has become a major problem for utility companies that need to be dealt with effectively. Convolutional Neural Network (CNN) and AdaBoost hybrid model have been developed that promise to detect electricity theft with high accuracy. However, this model is also vulnerable to evasion attacks that can render it ineffective. In this paper, to make the detection system more robust, we present an algorithm to create adversarial data for evasion attacks against a hybrid model combining Convolutional Neural Network and Adaboost (CNN-Adaboost). Generated adversarial data from the proposed algorithm is crafted on the model to test its performance. Our proposed attack is validated with State Grid Corporation of China (SGCC) dataset. We test the CNN-Adaboost energy theft detection model and other models’ performance under 5% and 10% evasion attacks. Our findings reveal model performance degradation under our proposed generative evasion attack ranging from 96.35% to ${8 9. 2 3 \%}$. These adversaries are useful for designing robust and secure ML models. The proposed attack can be utilized to test energy theft detection (ETD) models in industrial and commercial settings.