# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 4 ML-Based Cyber-Physical Attack Detection
### Network Layer
The proposed learning technique simultaneously utilizes a supervised learning model and an unsupervised learning model for automatic feature extraction and network anomaly detection. The proposed technique provides adaptive attack detection in a changing rapid attack-pattern environment, and therefore it has the strength to handle zero-day attacks. In [120], a fusion of learning methods with an LSTM model and a forward neural network (FNN) model is proposed to detect correlated network attacks. The FNN-only attack detection technique shows prominent detection performance only for single attacks, but low detection rates for correlated attacks. Meanwhile, the LSTM-only detection technique shows a remarkable detection capability for correlated attacks, but the accuracy in the detection of a single attack is not competitive. To overcome the defects of these two ML models, the proposed fusion method is adopted in the IDS, which enhances detection accuracy against both single and correlated network attacks. In [121], the authors present malware detection algorithms with various machine learning models for networks under the DNP3 protocol. Stuxnet, which attacks industrial control systems, is selected as the target malware for the validation of the detection algorithms. A bidirectional RNN-based network attack detector for a power system with the IEEE 1815.1 protocol is proposed in [122]. The proposed RNN model separately learns the headers and payloads of the power system packets in order to classify various types of attack: five types of malware, three types of false-data injections, and a disabling re-assembly attack are considered. The proposed detector not only identifies anomalies from the attacks under consideration, but it also detects trials of unauthorized command injections. A CNN-based network attack detector is proposed in [123] for supervisory control and data acquisition (SCADA) networks. The proposed CNN-based detector watches for network attacks in multiple network layers, from the data link layer to the application layer under the DNP3 protocol, where the authors consider 16 types of attacks and their combinations. The evaluation of the proposed detector is conducted on a real dataset from a power delivery system, with a detection performance above 94% precision for all attacks. A one-class SVM model is proposed in [124] to defend against DoS attacks targeting smart grid SCADA systems. In that work, the authors consider software-defined networking (SDN), where SDN is responsible for periodically capturing network device information on a centralized SDN controller. From the captured information, a one-class SVM model is trained, and it classifies DoS attacks against the network. The detection performance of the proposed one-class SVM shows an accuracy better than 99%. We briefly review the ML-based attack detection methods against network layer attacks. Table 2 summarizes the existing ML-based detection methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **CPS Area** & **Defense Against** & **ML Model** & **Validation** \\ \hline
[114] & Vehicle (VANET) & DoS & DNN & Simulation \\
[115] & Vehicle (CAN) & DoS & CNN & Real-data \\
[73] & Vehicle (CAN) & Various & DNN & Real-data \\
[116] & Vehicle (VANET) & Manipulation & Various & Simulation \\
[117] & ICS & Various & Various & Experiment \\
[118] & Gas pipeline & Various & KNN & Real-data \\
[119] & ICS & Manipulation & Various & Experiment \\
[120] & ICS & Various & LSTM-FNN & Experiment \\
[121] & Power & Manipulation & Various & Simulation \\
[122] & Power & Various & RNN & Simulation \\
[123] & Power & Various & CNN & Experiment \\
[124] & power & DoS & SVM & Simulation \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of ML-based anomaly detection methods in the network layer.