Luke Mirowski

An RFID Attacker Behavior Taxonomy

A taxonomy of system attacker behavior reveals security vulnerabilities in RFID authorization and monitoring systems. RFID systems are classified by their informational goals-typically, authorization and monitoring. Authorization systems replace the more traditional approaches of granting an entity access to a particular zone, whereas monitoring systems establish an entitys location in that zone. Although their informational goals differ, the underlying hardware is identical for both types of systems; consequently, attacks at the hardware level are the same. However, because attacker behavior invalidates each subsystems informational goals differently, RFID security requirements should consider these goals individually.

Water quality monitoring using abnormal tail-beat frequency of crucian carp.

Fish are rapidly becoming favored as convenient sentinels for behavioral assays of toxic chemical exposure. Tail-beat frequency (TBF) of fish is highly correlated with swimming speed, which has been used to detect toxicants. Here we examined the effect on TBF of exposure to two chemicals, and evaluated the ability of this novel behavioral parameter to accurately monitor water quality. To further refine our approach, the Wall-hitting rate (WHR) was used to characterize behavioral avoidance after exposure. Overall, exposure to test chemicals at different levels induced significant increase in both behavioral parameters of the red crucian carp during 1-h exposure periods. Furthermore, the TBF achieved better performance as an indicator when it was calculated in cases where the fish hit the tank wall. Collectively, this study demonstrates the capacity of the TBF of fish to assess water quality in a reliable manner.

Exploring a role for MCRDR in enhancing telehealth diagnostics

In-home telehealth devices are becoming increasingly popular when it comes to supporting the health management of home-based patients. With the devices capable of highly active monitoring, using sensors which produce large amounts of data, the deployment of telehealth devices into the home highlights the need for improved ways to collate, classify and dynamically interpret data safely and effectively. For clinicians working at a distance, the amounts of data generated by all in-home patient telematics devices poses questions on how best to intelligently filter, analyze and interpret this data to make diagnoses and respond to changes in patient conditions. In order to manage this issue, expert systems, applied for decades in other health fields, might play a role. In this paper, we explore how one type of expert system, Multiple Classification Ripple Down Rules (MCRDR), might address the issues. This paper begins by reviewing the capabilities of expert systems. Specifically, MCRDR is reviewed and its integration with an example telehealth device, MediStation, is explored. The range of potential benefits which might accrue when MCRDR and the MediStation are linked is identified as are some research and development challenges. Moving forwards, a simple simulator is introduced as one approach which is shown to be effective at exploring this exciting area of research. This paper takes the first steps towards introducing expert systems into the uHealth field and presents a simulator for this purpose.

How RFID Attacks Are Expressed in Output Data

Attacks at the “RFID layer”, where tags and readers interact, appear in the output data used by the “strategic layer” to monitor or authorize entities. Previous research has used contextual information from the “real world layer” or “strategic layer” for attack detection purposes; this means that detection methods need to be customized to the application environment. In contrast, we introduce the concept of an “RFID Layer Context Model” to contextualize output data at the RFID layer. The improvement is that context at this layer is divorced from the application environment, and thus, attack detection does not need to be customized to the application. As the features we use are generic, this work takes the first steps towards automating the detection of attacks at the RFID layer.

Mobile Application Based Heavy Vehicle Fatigue Compliance in Australian Operations

The Australian National Heavy Vehicle Regulator (NHVR) defines the rules for fatigue management in heavy haulage trucking operations. The rules place restrictions on total work and minimum rest hours, and are aimed at regulating the potential for fatigue risk amongst drivers. This paper presents a performance-based fatigue management system based on driver fatigue data stored in simple mobile databases and deployed via Android smart phones. The system funded by WorkSafe Tasmania and entitled, Logistics Fatigue Manager (LFM), was evaluated with a cohort of heavy haulage drivers in Australian forestry. The correlation between driver fatigue estimates and actual sleep hours (recorded using FitBits) is confirmed, and is also supported through driver interviews. The benefit is that management of fatigue risk could be more tailored to individual drivers opening up efficiency gains across supply chains.

Tyrell: A RFID simulation platform

Attacks in RFID systems are known to be expressed in output data. As these systems become more widespread, research into detecting attacks in the output data which is produced when RFID components interact will become more important. However, the problem limiting practical research developments in this area has been the availability of output data containing attacks. As the output data is contextualized by the application environment which produced it, detecting attacks requires knowledge of the environment to construct relevant plausibility checks. To overcome this problem we introduce a software based RFID simulation platform called Tyrell. The end-user instantiates the values of a real system and attacks into the simulation platform. The interaction between the simulated components leads to the production of output data containing attacks. The improvement is that output data can now be produced containing attacks in a repeatable way.