Application of Access Control Framework in Cloud Reliability
Ambuj Kumar Agarwal, Mamoona Anam, Dilip Kumar Sharma, R. Regin, Milu Acharya, K. Ashok · 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) · 2021
Control and monitoring data is transmitted across ICS networks in critical environments like the smart grid. Cyber attacks on smart grid communication can have disastrous implications for energy generation, delivery, and, ultimately, human life. Traditional smart grid security measures such as firewalls and Intrusion Detection Systems (IDS), which are normally put on the network’s edge, cannot detect internal threats since attacks can originate from both within and outside the network. As a result, we must additionally examine the behaviour of internal ICS communication. ICS traffic has predictable and steady communication patterns due to its nature. Statistical models can be used to describe these tendencies. We can develop a statistical profile of the communication based on the patterns found in regular communication traffic by examining chosen elements of ICS network communication such as packet inter-arrival times. This method is efficient, quick, and simple to use. Statistical-based anomaly detection can detect common security incidents in ICS communication, as our experiments indicate. This research utilizes the Local Outlier Factor (LOF) technique to create a statistical model for anomaly identification using selected network packet properties. The IEC 60870- 5-104 (a.k.a. IEC 104) protocol is used to demonstrate the proof-of-concept.