CNN-based anomaly detection for packet payloads of industrial control system - Song, Joo Yeop; Paul, Rajib; Yun, Jeong Han; Kim, Hyoung Chun; Choi, Young June (International Journal of Sensor Networks, 2021)
Industrial control systems (ICSs) are more vulnerable to cyber threats owing to their network connectivity. The intrusion detection system(IDS) has been deployed to detect sophisticated cyber-Attack but the existing IDS uses the packet header information for traffic flow detection. IDS is inefficient to detect packet deformation; therefore, we propose the adoption of packet payload in IDS to respond to a variety of attacks and high performance. Our proposed model detects packet modification and traffic flowby inspecting each packet and sequence of packets. For evaluation, cross verification is conducted to increase the reliability of the statistics.
Keywords: Anomaly detection; Convolutional neural network; Industrial control systems; Intrusion Detection Systems; Intrusion-Detection; N-gram methods; Networks security; Packet detection; Sequence detection; Single packet detection; Anomaly detection; Convolutional neural network; Industrial control system; Intrusion detection; n-gram method; network security; Sequence detection; Single packet detection