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Salman, A., Diehl, W., Kaps, J. P..  2017.  A light-weight hardware/software co-design for pairing-based cryptography with low power and energy consumption. 2017 International Conference on Field Programmable Technology (ICFPT). :235–238.

Embedded electronic devices and sensors such as smartphones, smart watches, medical implants, and Wireless Sensor Nodes (WSN) are making the “Internet of Things” (IoT) a reality. Such devices often require cryptographic services such as authentication, integrity and non-repudiation, which are provided by Public-Key Cryptography (PKC). As these devices are severely resource-constrained, choosing a suitable cryptographic system is challenging. Pairing Based Cryptography (PBC) is among the best candidates to implement PKC in lightweight devices. In this research, we present a fast and energy efficient implementation of PBC based on Barreto-Naehrig (BN) curves and optimal Ate pairing using hardware/software co-design. Our solution consists of a hardware-based Montgomery multiplier, and pairing software running on an ARM Cortex A9 processor in a Zynq-7020 System-on-Chip (SoC). The multiplier is protected against simple power analysis (SPA) and differential power analysis (DPA), and can be instantiated with a variable number of processing elements (PE). Our solution improves performance (in terms of latency) over an open-source software PBC implementation by factors of 2.34 and 2.02, for 256- and 160-bit field sizes, respectively, as measured in the Zynq-7020 SoC.

Salman, A., Elhajj, I.H., Chehab, A., Kayssi, A..  2014.  DAIDS: An Architecture for Modular Mobile IDS. Advanced Information Networking and Applications Workshops (WAINA), 2014 28th International Conference on. :328-333.

The popularity of mobile devices and the enormous number of third party mobile applications in the market have naturally lead to several vulnerabilities being identified and abused. This is coupled with the immaturity of intrusion detection system (IDS) technology targeting mobile devices. In this paper we propose a modular host-based IDS framework for mobile devices that uses behavior analysis to profile applications on the Android platform. Anomaly detection can then be used to categorize malicious behavior and alert users. The proposed system accommodates different detection algorithms, and is being tested at a major telecom operator in North America. This paper highlights the architecture, findings, and lessons learned.