# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 5 Smart Grid Attacks
### Non-Cryptography Solutions
#### 7.2.1 Anomaly Detection
The evaluation results showed that the industrial smart grid (ISG) achieves an accuracy of 97.28% in binary classification and between 96.36 and 95.04% in multi-class classification. In [253], [PERSON] et al. presented an IDS called MENSA (anoMaly dEtection aNd claSificAtion), which adopts a novel Autoencoder-Generative Adversarial Network (GAN) architecture to detect operational anomalies and classify cyberattacks. The e valuation results showed that MENSA achieves high accuracy with a low False-Positive Rate (FPR). In [254], [PERSON] et al. presented a hybrid Multilayer Perceptron (MLP) Sequential-Feed Forward Neural Network (SEQ-FFNN) as a self-learning model that performs a principal component analysis, hyper-parametrization, testing, training, and prediction of accurate outputs per code iteration. The results showed that the detection accuracy achieved 98.97% for tanh-activated layers and 99.59% for the Sigmoid-activated model. In [255], [PERSON] et al. presented a federated learning (FL)-based smart grid anomaly detection scheme where ML models are trained locally in smart meters without sharing data with a central server to ensure users' privacy while securing model parameter updates for the Secure Sockets Layer/Transport Layer Security (SSL/TLS) protocol.The study showed that it performs efficiently regarding memory, bandwidth, CPU usage, and power consumption at edge devices and can be deployed in resource-constrained environments. In [256], [PERSON] et al. focused on the Internet of Energy (IoE) devices and network resources [257; 258]. They considered three machine learning techniques, including Long Short-Term Memory (LSTM), Isolation Forest (IF), and Support Vector Machine (SVM), while also describing the data preparation process. The authors also showed that a more accurate classification can be achieved by increasing the number of analyzed features. In [3], [PERSON] et al. surveyed the concept of Machine learning (ML)-based Intrusion Detection System (IDS) approaches in smart grids, especially in transmission and distribution-side power components of a smart power grid while taking into consideration the security aspect. Meanwhile, several solutions were presented, such as the following: In [259], [PERSON] et al. presented an unsupervised anomaly detection based on a statistical correlation between measurements and using symbolic dynamic filtering (SDF) to reduce the computational burden. The results showed an accuracy of 99%, with a true-positive rate of 98%. In [260], [PERSON] et al. presented an enhanced cyberattack detection strategy using unlabeled data to monitor ICS traffic and detect suspicious data transmissions, with two semi-supervised hybrid deep-learning-based anomaly detection methods being designed for intrusion detection in ICS traffic of the smart grid. The experimental results show that this strategy achieved enhanced detection with an averaged F1-score of 0.98. In [261], [PERSON] et al. presented a secure Demand-Side Management (DSM) engine using machine learning for the IoT-enabled grid to preserve the efficient utilization of energy. In contrast,a specific resilient model was presented to control intrusions in the smart grid. Analysis showed that the projected DSM engine is less vulnerable to intrusion and effective enough to reduce power utilization. Finding unusual patterns and strange behaviors in smart grid operations that indicate cyberattacks or system failures requires the use of anomaly detection techniques, especially those based on machine learning. Anomaly detection, for instance, can be used to track grid voltage shifts and identify any sudden variations that could point to a substation equipment failure or a False Data Injection Attack (FDIA).