Enhancing DNP3 Security Using CNN Deep Learning Techniques

Industrial Automation and Control Systems (IACS) are necessary for enabling secure information exchange between smart devices; ensuring security in Industrial Control Systems (ICS) is of importance due to the presence of these devices at distant locations and their control over vital plant activities. Intelligent devices and hosts use protocols such as Modbus, DNP3, IEC 60870, IEC 61850, and others. This paper focuses on the analysis and development of techniques for detecting of network traffic within the industrial environment, more specifically anomalies in the application ZZZAlayer in the to the protocol called Distribution Network Protocol (DNP3) is an open-source protocol used in Supervisory Control and Data Acquisition (SCADA) systems and widely recognized as the standard for the water, sewage, and oil and gas industries. it is used in the realm of industrial automation; they are critical facilities for the population and must be secured against any security breaches. One of the main objectives of cyber attackers is related with these systems. In This paper presents an architecture that, classification system by Deep Learning algorithm with (CNN). The proposed model was evaluated using standard Intrusion Detection Dataset for DNP3, with 7326) and 86field. The CNN algorithm obtained the best results accuracy