  Demand-Side Management (DSM) is a vital tool that can be used to ensure power
system reliability and stability. In future smart grids, certain portions of a
customers load usage could be under automatic control with a cyber-enabled DSM
program which selectively schedules loads as a function of electricity prices
to improve power balance and grid stability. In such a case, the security of
DSM cyberinfrastructure will be critical as advanced metering infrastructure,
and communication systems are susceptible to hacking, cyber-attacks. Such
attacks, in the form of data injection, can manipulate customer load profiles
and cause metering chaos and energy losses in the grid. These attacks are also
exacerbated by the feedback mechanism between load management on the consumer
side and dynamic price schemes by independent system operators. This work
provides a novel methodology for modeling and simulating the nonlinear
relationship between load management and real-time pricing. We then investigate
the behavior of such a feedback loop under intentional cyber-attacks using our
feedback model. We simulate and examine load-price data under different levels
of DSM participation with three types of additive attacks: ramp, sudden, and
point attacks. We apply change point and supervised learning methods for
detection of DSM attacks. Results conclude that while higher levels of DSM
participation can exacerbate attacks they also lead to better detection of such
attacks. Further analysis of results shows that point attacks are the hardest
to detect and supervised learning methods produce results on par or better than
sequential detectors.
