Biblio

A class of cyber-attacks called False Data Injection attacks that target measurement data used for state estimation in the power grid are currently under study by the research community. These attacks modify sensor readings obtained from meters with the aim of misleading the control center into taking ill-advised response action. It has been shown that an attacker with knowledge of the network topology can craft an attack that bypasses existing bad data detection schemes (largely based on residual generation) employed in the power grid. We propose a multi-agent system for detecting false data injection attacks against state estimation. The multi-agent system is composed of software implemented agents created for each substation. The agents facilitate the exchange of information including measurement data and state variables among substations. We demonstrate that the information exchanged among substations, even untrusted, enables agents cooperatively detect disparities between local state variables at the substation and global state variables computed by the state estimator. We show that a false data injection attack that passes bad data detection for the entire system does not pass bad data detection for each agent.

Riding on the success of SDN for enterprise and data center networks, recently researchers have shown much interest in applying SDN for critical infrastructures. A key concern, however, is the vulnerability of the SDN controller as a single point of failure. In this paper, we develop a cyber-physical simulation platform that interconnects Mininet (an SDN emulator), hardware SDN switches, and PowerWorld (a high-fidelity, industry-strength power grid simulator). We report initial experiments on how a number of representative controller faults may impact the delay of smart grid communications. We further evaluate how this delay may affect the performance of the underlying physical system, namely automatic gain control (AGC) as a fundamental closed-loop control that regulates the grid frequency to a critical nominal value. Our results show that when the fault-induced delay reaches seconds (e.g., more than four seconds in some of our experiments), degradation of the AGC becomes evident. Particularly, the AGC is most vulnerable when it is in a transient following say step changes in loading, because the significant state fluctuations will exacerbate the effects of using a stale system state in the control.

This paper presents a model for generating personalized passwords (i.e., passwords based on user and service profile). A user's password is generated from a list of personalized words, each word is drawn from a topic relating to a user and the service in use. The proposed model can be applied to: (i) assess the strength of a password (i.e., determine how many guesses are used to crack the password), and (ii) generate secure (i.e., contains digits, special characters, or capitalized characters) yet easy to memorize passwords.

This paper presents a system named SPOT to achieve high accuracy and preemptive detection of attacks. We use security logs of real-incidents that occurred over a six-year period at National Center for Supercomputing Applications (NCSA) to evaluate SPOT. Our data consists of attacks that led directly to the target system being compromised, i.e., not detected in advance, either by the security analysts or by intrusion detection systems. Our approach can detect 75 percent of attacks as early as minutes to tens of hours before attack payloads are executed.