Biblio

Cyber physical systems (CPS) is a dominant technology in today's world due to its vast variety of applications. But in recent times, the alarmingly increasing breach of privacy and security in CPS is a matter of grave concern. Security and trust of CPS has become the need of the hour. Hardware Trojans are one such a malicious attack which compromises on the security of the CPS by changing its functionality or denial of services or leaking important information. This paper proposes the detection of Hardware Trojans at the system level in AES-256 decryption algorithm implemented in Atmel XMega Controller (Target Board) using a combination of side-channel power analysis and machine learning. Power analysis is done with help of ChipWhisperer-Lite board. The power traces of the golden algorithm (Hardware Trojan free) and Hardware Trojan infected algorithms are obtained and used to train the machine learning model using the 80/20 rule. The proposed machine learning model obtained an accuracy of 97%-100% for all the Trojans inserted.

In the past decade, cyber-attack events on the power grid have proven to be sophisticated and advanced. These attacks led to severe consequences on the grid operation, such as equipment damage or power outages. Hence, it is more critical than ever to develop tools for security assessment and detection of anomalies in the cyber-physical grid. For an extensive power grid, it is complex to analyze the causes of frequency deviations. Besides, if the system is compromised, attackers can leverage on the frequency deviation to bypass existing protection measures of the grid. This paper aims to develop a novel specification-based method to detect False Data Injection Attacks (FDIAs) in the multi-area system. Firstly, we describe the implementation of a three-area system model. Next, we assess the risk and devise several intrusion scenarios. Specifically, we inject false data into the frequency measurement and Automatic Generation Control (AGC) signals. We then develop a rule-based method to detect anomalies at the system-level. Our simulation results proves that the proposed algorithm can detect FDIAs in the system.