Biblio

We propose an autoencoder based approach to anomaly detection in smart grid systems. Data collecting sensors within smart home systems are susceptible to many data corruption issues, such as malicious attacks or physical malfunctions. By applying machine learning to a smart home or grid, sensor anomalies can be detected automatically for secure data collection and sensor-based system functionality. In addition, we tested the effectiveness of this approach on real smart home sensor data collected for multiple years. An early detection of such data corruption issues is essential to the security and functionality of the various sensors and devices within a smart home.

In the recent years, silicon based Physical Unclonable Function (PUF) has evolved as one of the popular hardware security primitives. PUFs are a class of circuits that use the inherent variations in the Integrated Circuit (IC) manufacturing process to create unique and unclonable IDs. There are various security related applications of PUFs such as IC counterfeiting, piracy detection, secure key management etc. In this paper, we are presenting a novel QUasi-Adiabatic Logic based PUF (QUALPUF) which is designed using energy recovery technique. To the best of our knowledge, this is the first work on the hardware design of PUF using adiabatic logic. The proposed design is energy efficient compared to recent designs of hardware PUFs proposed in the literature. Further, we are proposing a novel bit extraction method for our proposed PUF which improves the space set of challenge-response pairs. QUALPUF is evaluated in security metrics including reliability, uniqueness, uniformity and bit-aliasing. Power and area of QUALPUF is also presented. SPICE simulations show that QUALPUF consumes 0.39μ Watt of power to generate a response bit.