Biblio

Smart grid has evolved as the next generation power grid paradigm which enables the transfer of real time information between the utility company and the consumer via smart meter and advanced metering infrastructure (AMI). These information facilitate many services for both, such as automatic meter reading, demand side management, and time-of-use (TOU) pricing. However, there have been growing security and privacy concerns over smart grid systems, which are built with both smart and legacy information and operational technologies. Intrusion detection is a critical security service for smart grid systems, alerting the system operator for the presence of ongoing attacks. Hence, there has been lots of research conducted on intrusion detection in the past, especially anomaly-based intrusion detection. Problems emerge when common approaches of pattern recognition are used for imbalanced data which represent much more data instances belonging to normal behaviors than to attack ones, and these approaches cause low detection rates for minority classes. In this paper, we study various machine learning models to overcome this drawback by using CIC-IDS2018 dataset [1].

Real-time clock circuits are widely used in modern electronic systems to provide time information to the systems at the beginning of the system power-on. In this paper, we present two types of Hardware Trojan designs that employ the time information as the trigger conditions. One is a real-time based Trojan, which will attack a system at some specific realworld time. The other is a relative-time based Trojan, which will be triggered when a specific time period passes after the system is powered on. In either case when a Trojan is triggered its payload may corrupt the system or leakage internal information to the outside world. Experimental results show that the extra power consumption, area overhead and delay time are all quite small and thus the detection of the Trojans is difficult by using traditional side-channel detection methods.