• National Science Foundation (SaTC program): "Collaborative Research: SaTC: CORE: Small: Towards Robust, Scalable, and Resilient Radio Fingerprinting" (CNS:2225161); UNL PI Dr. Nirnimesh Ghose, Lead PI Dr. Boyang Wang (University of Cincinnati); duration 02/2023 - 01/2026 for $586,681 (UNL Share:$299,997).

  • State (Internal):

  • Nebraska Center for Energy Sciences Research: "Smart Grid cybersecurity enhancement using smart authentication and intelligent threat detection"; PI Dr. Byrav Ramamurthy, Co-PI Dr. Nirnimesh Ghose; duration 01/2023 - 12/2024 for $170,000 (my share: $85,000).

  • Nebraska Research Initiative: "Machine Learning, Data Mining and Wireless PHY-layer for a secure IoT System"; PI Dr. Jacques Bou Abdo (UNK), Co-PI Dr. Nirnimesh Ghose (UNL), and Co-PI Dr. Basheer Qolomany (UNK); duration 07/2021 - 06/2022 for $7,100.00 (my share: $1,600).

Research Interests

  • ML assisted Device/Location Identification

  • In many applications, such as IoT, 5G D2D, 5G V2X communications, and many more. Just verifying cryptographic identity does not provide required security properties such as authentications. To tackle this problem we use ML algorithms to perform identification of devices/locations' wireless physical layer identifiers. Which in a way provides a second factor authentication of the cryptographic identities. (Funded by: National Science Foundation (NSF) (CNS:2225161), and Nebraska Center for Energy Sciences Research (NCESR))

  • Bio-Social Inspired Dynamic Spectrum Access

  • Explosion of embedding wireless capabilities in contemporary systems have led to over crowding of ISM bands. FAA proposes dynamic spectrum access of under utilized bands to tackle this problem. We have proposed to develop novel dynamic spectrum access techniques inspired from social learning, reciprocity and social sanction behavior of a society in consumption of resources. We aim to increase the band usage by optimizing local and global throughput while achieving fair distribution of band usage.

  • IoT security

  • Designed a physical layer pairing protocol for IoT devices. IoT devices when introduced in a network to connect to a hub such as the wireless router, do not share a secret with the hub. Proposed a technique to authentication and key-exchange between IoT device and the hub with a helper of a pre-paired device. Also proposed group pairing protocol to pair multiple devices in a single session.

  • Aviation security

  • Designed a physical layer authentication for NextGen ADS-B messages. ADS-B messages are broadcasted by aircraft which has its location, velocity, and other identification information. These are proposed to replace the present RADAR system when NextGen is implemented. Proposed to use Doppler spread value extracted from wireless signal to verify the embedded value of location and velocity in the ADS-B message.

  • Secure voting algorithm

  • Voting algorithms are used by wireless systems when a group of wireless nodes needs to come to a definitive decision. General voting algorithms are implemented on higher layers of OSI model with cryptography. Hence, it takes longer time and higher overhead. Proposed a secure voting algorithm in which energy is injected on different subcarriers in OFDM, such that adversary cannot change the voting outcome.

  • Cyber security's interaction with social networks

  • Addressed the problem of modeling and analyzing cyber attacks using a multimodal graph approach. The main idea was to formulate the stages, actors, victims, and outcomes of cyber attacks as a multimodal graph, where several graphs of various modalities were combined to represent the attributes of the attack. Multimodal graph nodes included cyberattack victims, adversaries, autonomous systems (ASes) used to perpetrate the attacks, and the observed cyber events. To formulate multimodal graphs, single-modality graphs were interconnected according to their interaction during the cyber attack. Once the graph model was constructed, we applied community and centrality analysis to obtain in-depth insights into the attack. In community analysis, we clustered those multimodal graph nodes that exhibit "strong" inter-modal ties. We further used centrality to rank the graph nodes according to their importance in the attack. By classifying nodes according to centrality, we deduced the progression of the attack from the attacker nodes to the targeted nodes. Using these results, we applied our methods to two popular case studies, namely GhostNet and Putter Panda and demonstrated a clear distinction in the attack stages.