Biblio

A scheme for preserving privacy in MobilityFirst (MF) clean-slate future Internet architecture is proposed in this paper. The proposed scheme, called Anonymity in MobilityFirst (AMF), utilizes the three-tiered approach to effectively exploit the inherent properties of MF Network such as Globally Unique Flat Identifier (GUID) and Global Name Resolution Service (GNRS) to provide anonymity to the users. While employing new proposed schemes in exchanging of keys between different tiers of routers to alleviate trust issues, the proposed scheme uses multiple routers in each tier to avoid collaboration amongst the routers in the three tiers to expose the end users.

By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of smart-grid systems, with techniques such as denial-of-service (DoS) attack, random attack, and data-injection attack. In this paper, we present a mathematical model of the system to study these pitfalls and propose a robust security framework for the smart grid. Our framework adopts the Kalman filter to estimate the variables of a wide range of state processes in the model. The estimates from the Kalman filter and the system readings are then fed into the χ2-detector or the proposed Euclidean detector. The χ2-detector is a proven effective exploratory method used with the Kalman filter for the measurement of the relationship between dependent variables and a series of predictor variables. The χ2-detector can detect system faults/attacks, such as DoS attack, short-term, and long-term random attacks. However, the studies show that the χ2-detector is unable to detect the statistically derived false data-injection attack. To overcome this limitation, we prove that the Euclidean detector can effectively detect such a sophisticated injection attack.

The security of Smart Grid, being one of the very important aspects of the Smart Grid system, is studied in this paper. We first discuss different pitfalls in the security of the Smart Grid system considering the communication infrastructure among the sensors, actuators, and control systems. Following that, we derive a mathematical model of the system and propose a robust security framework for power grid. To effectively estimate the variables of a wide range of state processes in the model, we adopt Kalman Filter in the framework. The Kalman Filter estimates and system readings are then fed into the χ2-square detectors and the proposed Euclidean detectors, which can detect various attacks and faults in the power system including False Data Injection Attacks. The χ2-detector is a proven-effective exploratory method used with Kalman Filter for the measurement of the relationship between dependent variables and a series of predictor variables. The χ2-detector can detect system faults/attacks such as replay and DoS attacks. However, the study shows that the χ2-detector detectors are unable to detect statistically derived False Data Injection Attacks while the Euclidean distance metrics can identify such sophisticated injection attacks.