Visible to the public Biblio

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Lalouani, Wassila, Younis, Mohamed.  2020.  Machine Learning Enabled Secure Collection of Phasor Data in Smart Power Grid Networks. 2020 16th International Conference on Mobility, Sensing and Networking (MSN). :546–553.
In a smart power grid, phasor measurement devices provide critical status updates in order to enable stabilization of the grid against fluctuations in power demands and component failures. Particularly the trend is to employ a large number of phasor measurement units (PMUs) that are inter-networked through wireless links. We tackle the vulnerability of such a wireless PMU network to message replay and false data injection (FDI) attacks. We propose a novel approach for avoiding explicit data transmission through PMU measurements prediction. Our methodology is based on applying advanced machine learning techniques to forecast what values will be reported and associate a level of confidence in such prediction. Instead of sending the actual measurements, the PMU sends the difference between actual and predicted values along with the confidence level. By applying the same technique at the grid control or data aggregation unit, our approach implicitly makes such a unit aware of the actual measurements and enables authentication of the source of the transmission. Our approach is data-driven and varies over time; thus it increases the PMU network resilience against message replay and FDI attempts since the adversary's messages will violate the data prediction protocol. The effectiveness of approach is validated using datasets for the IEEE 14 and IEEE 39 bus systems and through security analysis.
Broström, Tom, Zhu, John, Robucci, Ryan, Younis, Mohamed.  2018.  IoT Boot Integrity Measuring and Reporting. SIGBED Rev.. 15:14–21.
The current era can be characterized by the massive reliance on computing platforms in almost all domains, such as manufacturing, defense, healthcare, government. However, with the increased productivity, flexibility, and effectiveness that computers provide, comes the vulnerability to cyber-attacks where software, or even firmware, gets subtly modified by a hacker. The integration of a Trusted Platform Module (TPM) opts to tackle this issue by aiding in the detection of unauthorized modifications so that devices get remediation as needed. Nonetheless, the use of a TPM is impractical for resource-constrained devices due to power, space and cost limitations. With the recent proliferation of miniaturized devices along with the push towards the Internet-of Things (IoT) there is a need for a lightweight and practical alternative to the TPM. This paper proposes a cost-effective solution that incorporates modest amounts of integrated roots-of-trust logic and supports attestation of the integrity of the device's boot-up state. Our solution leverages crypto-acceleration modules found on many microprocessor and microcontroller based IoT devices nowadays, and introduces little additional overhead. The basic concepts have been validated through implementation on an SoC with an FPGA and a hard microcontroller. We report the validation results and highlight the involved tradeoffs.