Thaier Hayajneh

Fordham University, Computer and Information Sciences, Faculty Member

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Thaier Hayajneh, Ph.D., specializes in Cybersecurity, and Data Networks, is the founding director of Fordham Center for Cybersecurity, Associate professor of Computer Science, program director of MS in Cybersecurity, program director of MS in Data Analytics, and Director of the Cybersecurity Research and Education lab at Fordham University. Prior to joining Fordham University, Dr. Hayajneh was a full-time faculty of computer science at New York Institute of Technology and founder director of NYIT Center of Excellence in Cyber Security (2014-2016). He also worked as Chair and Associate Professor at the Computer Engineering Department at the Hashemite University in Jordan (2009-2014). Dr. Hayajneh received his Ph.D. and M.S. degrees in Information Sciences with specialization in cybersecurity and Networking from the University of Pittsburgh, PA, USA in 2009 and 2005, respectively. He also received his M.S. and B.S. in Electrical and Computer Engineering from Jordan University of Science and Technology, Irbid, Jordan, in 1999 and 1997, respectively.
His research focuses on cybersecurity and networking, including wireless networking security, information assurance and privacy, crypto-resilient attacks, applied cryptography, cyber-physical systems and WBAN security, and wireless networks coexistence. Dr. Hayajneh has published over 50 papers in peer-reviewed journals and international conferences. Dr. Hayajneh is currently serving as Editor for Springer Wireless Networks (WINE) journal and Editor-in-Chief for EAI Endorsed Transactions on Pervasive Health and Technology, and as guest editor for Sensors and International Journal of Distributed Sensor Networks. He also serves as a reviewer (reviewed over 70 papers) for several prestigious journals.
Address: New York, New York, United States

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Hardware Trojan detection for lightweight ciphers implemented on field‐programmable gate arrays using the replay algorithm

International Journal of Circuit Theory and Applications, 2021

Hardware Trojans (HTs) are malicious and intentional modification of the hardware design, embedde... more Hardware Trojans (HTs) are malicious and intentional modification of the hardware design, embedded by an adversary to leak sensitive data, modify functionality, or cause malfunction. Lightweight ciphers are designed for resource‐constrained devices (RCDs) to balance resources and security, but often targeted by HTs. This work aims to create a trusted lightweight cipher design and detect complex sequential HTs using a runtime monitoring method with minimal resource overhead. The novelty of the approach is that it combines full or partial temporal redundancy, resource minimization, and runtime monitoring. The algorithm employs temporal redundancy, by executing a first run of the cipher, and then replays the cipher fully (ReplayN method) or partially (ReplayN/2 method). A mismatch indicates HT activity, and a third replay is executed to determine the correct results. The ReplayN/2 algorithm further optimizes time and energy by limiting replay to the second half of the cipher rounds. The proposed method is compared with other existing methods including triple modular redundancy (TMR), adapted triple modular redundancy (ATMR), and reverse‐function redundancy (RFR). All methods were implemented in field‐programmable gate array (FPGA) technology. Implementation performance metrics were measured and compared, including resource utilization (i.e., logical elements [LEs]), power, energy (E), and speed (i.e., Fmax). The results show that the proposed algorithm outperforms existing methods in most of the metrics. In terms of speed and area, the replay algorithm is faster by 24% and reduces total LEs by 42% when compared with existing methods. Regarding power, replay algorithm reduces power by 45% when compared with TMR and ATMR. With respect to energy, replay methods reduce energy by an average of 6% when compared with TMR and ATMR. The ReplayN/2 reduces energy by an average 15% when compared with TMR and ATMR. Based on the E×LE (i.e., energy × area) metric, ReplayN/2 is the top performing method as it balances area and energy metrics. Using E×LEFmax metric, the replay methods are the best performing methods as they best balance area, energy, and speed. Also, based on ThroughputLE2 metric, the replay methods are the top performing methods.

Power-Aware Adaptive Encryption

2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 2019

Recently, there has been a significant increase in number of resource-constrained devices, which ... more Recently, there has been a significant increase in number of resource-constrained devices, which are typically battery-powered. Encryption schemes are employed to protect privacy of exchanged data. In such devices, balancing data security and devices resources is a critical design challenge. For resource constrained devices, energy is the most crucial resource. The research motivation is to adapt cipher energy based to predefined power levels using a single cipher with multiple implementation options.In this paper we propose a scheme to adjust power and energy consumption of single lightweight cipher based on current device power level. The scheme adjusts cipher energy by varying design options and operation complexity of the cipher. This includes varying number of implemented rounds in hardware, key scheduling and number of algorithm iterations. While altering number of algorithm iteration reduces security, it is better than inhibiting encryption or shutting down the device. Our proposed scheme allows adjustable encryption for low power level. Results from Field-programmable gate array (FPGA) implementations show energy savings of 35%-39% compared with implementations of existing designs.

Evolution of Format Preserving Encryption on IoT Devices: FF1+

Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019

Thaier Hayajneh

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