# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 4 ML-Based Cyber-Physical Attack Detection
### Network Layer  
For a vehicle platooning scenario in the IEEE 802.11p based VANET environment, the hybrid jamming attack detection method, combined with a protocol knowledge-based method and a learning-based method, is proposed in [114]. The jamming attack causes collisions of safety-critical cooperative awareness messages (CAMs); however, the CAMs are lost due to the inherent collision nature of the IEEE 802.11 communications protocol, and therefore inherent collisions and malicious collisions may not be classified. The proposed hybrid jamming attack detection method is evaluated by changing the number of platooned vehicles to as many as 25, with which the detection performance of the proposed method shows 95% accuracy. A CNN-based source node identification method for the CAN network is proposed in [115]. The proposed method can be utilized to detect abnormal jamming signals that exploit an inherent vulnerability in the CAN bus network, where the CNN model learns the channel characteristics and patterns of the CAN frame. A traffic sequence learning method for various types of network attack detection on a CAN is proposed in [73]. The authors in [73] consider three attack scenarios (DoS, random packet injection, and malicious packet injection with vehicle knowledge), and the proposed learning method, adopting a DNN model, learns these attacks with CAN traffic patterns for attack-free versus abnormal situations. A performance evaluation of the proposed method is conducted with real data from a CAN bus on a real vehicle, and the detection performance of the DNN model is superior to the decision tree algorithm and the KNN method. A supervised ML-based malicious node attack detector is proposed in [116] in consideration of a VANET with the IEEE 802.11p media access control (MAC) protocol and an ad-hoc on-demand distance vector (AODV) routing protocol, where a malicious node exploits the AODV protocol. The proposed detector adopts KNN models to learn various network features, including IP addresses, delay, jitter, dropped packets, and throughput. Simulation results in an NS-3 environment show 99% accuracy, with values less than 1% for the false-positive and false-negative detection ratios. An SVM-based network attack detector is proposed in [117] for industrial control systems (ICSs). The proposed detector utilizes an SVM model and a K-means clustering method to alert administration to attacks, providing a classification of three types of network attack: the network scan, ARP spoofing, and the flooding attack. A two-stage, packet-level anomaly detector was proposed in [118], where the anomaly detector sequentially inspects the packet signatures and time-series characteristics of the packets. In the first stage, a Bloom-filter method detects packet-level anomalies in exchanged data, and a KNN learning model classifies an attack by inspecting time-series data. The authors validated the performance of the detector with real data from a gas pipeline system. A semi-supervised \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **CPS Area** & **Defense Against** & **ML Model** & **Validation** \\ \hline
[22] & Vehicle & Sensor & RNN & Experiment \\
[103] & Vehicle & Sensor & CNN & Simulation \\
[104] & Vehicle & Sensor & LSTM-CNN & Dataset \\
[105] & Vehicle & Sensor & SVM & Simulation \\
[106] & Water & Sensor & Various & Simulation \\
[107] & Chemical & Various & SVM & HILS \\
[108] & HVAC & Sensor & SVM & Simulation \\
[36] & HVAC & Various & SVM & Simulation \\
[110] & Power & Various & Various & HILS \\
[111] & Power & Sensor & DRE & Simulation \\
[112] & Power & Sensor & SVM & Simulation \\
[113] & power & Sensor & DBN & Simulation \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of ML-based anomaly detection methods in the physical layer. learning technique to detect multiple cyber-physical attacks is proposed in [119], where the authors consider general industrial control systems adopting various industrial communications protocols.