About

Mai Abdel-Malek is an assistant professor of practice in electrical and computer engineering at the University of Arizona. Her research interests include next-generation cellular technologies, 5G network security, electric vehicles, UAVs and drone communication, secure Internet of Things networks, and device-to-device communications. Prior to joining the University of Arizona, she was a postdoctoral researcher at Virginia Tech’s Wireless Lab and worked as a research fellow at Virginia Tech-Middle East and North Africa during the 2015-16 academic year. She holds a PhD in Electrical and Computer Engineering from Florida International University, obtained in 2021, a Master’s degree in Wireless Technology from Nile University in 2015, and a Bachelor’s degree in Electrical, Communications and Electronic Engineering from Alexandria University in 2013.

Research topics

• Computer Science
• Computer Security
• Computer network

Selected publications

• Privacy-preserved mutually-trusted 5G communications in presence of pervasive attacks

  Internet of Things · 2025-01-11 · 3 citations

  articleCorresponding
  PublisherDOI

• Automated and Blind Detection of Low Probability of Intercept RF Anomaly Signals

  2024-12-04 · 1 citations

  articleOpen access

  Automated spectrum monitoring necessitates the accurate detection of low probability of intercept (LPI) radio frequency (RF) anomaly signals to identify unwanted interference in wireless networks. However, detecting these unforeseen low-power RF signals is fundamentally challenging due to the scarcity of labeled RF anomaly data. In this paper, we introduce WANDA (Wireless ANomaly Detection Algorithm), an automated framework designed to detect LPI RF anomaly signals in low signal-to-interference ratio (SIR) environments without relying on labeled data. WANDA operates through a two-step process: (i) Information extraction, where a convolutional neural network (CNN) utilizing soft Hirschfeld-Gebelein-Rényi correlation (HGR) as the loss function extracts informative features from RF spectrograms; and (ii) Anomaly detection, where the extracted features are applied to a one-class support vector machine (SVM) classifier to infer RF anomalies. To validate the effectiveness of WANDA, we present a case study focused on detecting unknown Bluetooth signals within the WiFi spectrum using a practical dataset. Experimental results demonstrate that WANDA outperforms other methods in detecting anomaly signals across a range of SIR values (-10 dB to 20 dB).

  PublisherOA PDFDOI

• UAV-fleet management for extended NextG emergency support infrastructure with QoS and cost aware

  Internet of Things · 2023-12-23 · 9 citations

  article1st authorCorresponding
  PublisherDOI

• Optimization and Control of Autonomous UAV Swarm for Object Tracking

  2023-10-30 · 2 citations

  article

  Unmanned Aerial Vehicles (UAVs) swarms are emerging technologies with the ability to carry out a wide range of military missions aiming to avoid human casualties. The UAVs' ability to access remote and inaccessible territories while gathering critical intelligence can be critical. Therefore, swarm real-time coordination on the fly is vital for information exchange and dynamically updating the flying hierarchy without failing the mission. Nevertheless, coordinating UAV swarms to execute maneuvers poses a complex challenge. In our Demonstration, we execute an on-the-fly swarm update based on unexpected or urgent events during the missions to avoid collisions and ensure mission safety. This demo shows a " find and rescue" military mission for security operations using a swarm of UAVs to track other adversaries' UAVs in a restricted area, which requires advanced swarm synchronization and on-the-fly decisions. For this demo, we conducted a successful flying swarming coordination for dynamic on-the-fly maneuvers with a reliable collision avoidance mechanism.

  PublisherDOI

• Frequency Hopping Signal Detection in Low Signal-to-Noise Ratio Regimes

  2023-09-05 · 4 citations

  article

  The detection of unauthorized frequency hopping (FH) signals has several applications in securing the radio frequency spectrum and achieving spectrum awareness in both tactical and cyber-physical systems. However, the blind detection of adversary FH signals is a challenging task, particularly in low signal-to-noise ratio (SNR) regimes, due to the adoption of dynamic hopping patterns. In this study, we propose a cyclo-stationary signal features-based blind FH signal detection scheme to address this challenge. Our proposed scheme consists of two steps: (i) feature extraction, where cyclic features are extracted from the spectral correlation function of the signals, and (ii) feature classification, where the extracted features are associated with ON/OFF detection states using a trained support vector machine (SVM) classifier. We leverage both binary and one-class SVM classifiers to enable adversary FH signal detection with and without pre-existing signal labels. Extensive simulations are conducted to verify the efficacy of the proposed FH signal detection scheme in low SNR regimes. Simulation results also provide insights into the interplay of various system parameters, such as the numbers of cyclic features and emission bandwidth, on the detection performance of the proposed SVM classifiers.

  PublisherDOI

• Uav-Fleet Management for Extended Nextg Emergency Support Infrastructure with Qos and Cost Aware

  SSRN Electronic Journal · 2023-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks

  Sensors · 2022-04-02 · 3 citations

  articleOpen access1st author

  Next Generation cellular networks are expected to offer better service quality, secure and reliable service provisioning, and more cooperative operation even in unexpected stressful situations. Service provider cooperation can facilitate reliable service provisioning and extended coverage in disasters situations or partial network failures. However, the current 4G and 5G standards do not offer security and privacy-friendly support for inter-operator agility and service mobility, a key enabler for such cooperation. The situation becomes more critical in presence of attackers, where establishing trust relationships becomes very complicated. This paper presents a novel UAV-assisted user-agility support framework that enables trustworthy seamless service migration in a zero-trust environment. The proposed framework facilitates temporal authentication-authority delegation and proxying to enable preservice, all-party mutual authentication. The framework is implemented and tested on top of the srsRAN open-source 4G/5G software stack. Experiments showed that the presented framework managed to facilitate effective and efficient trustworthy service migration between heterogeneous service provider networks.

  PublisherOA PDFDOI

• A Proxy Signature-Based Swarm Drone Authentication With Leader Selection in 5G Networks

  IEEE Access · 2022 · 27 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Computer Security

  Drones are imperative for the 5G architecture as a mobile source to expand network coverage and support seamless services, particularly through enabling device-to-device (D2D) communication. Such deployment of drones in D2D settings raises various security threats in drone communication. While the existing D2D communication security standard within the 4G cellular architecture may address some of these issues, the standard includes heavy traffic toward the network core servers. If this security standard is to be adopted in the 5G D2D security services with the same traffic load, it may negatively impact the 5G network performance. Therefore, this paper proposes a lightweight proxy signature-based authentication mechanism for a swarm of drones compatible with the 5G D2D standard mechanisms. This paper proposes a distributed delegation-based authentication mechanism to reduce the traffic overhead toward the 5G core network. In this scheme, the legitimate drones are authorized as proxy delegated signers to perform authentication on behalf of the core network. Furthermore, we propose a mechanism to elect and relocate a new leader relay drone from the existing drone swarm. We implemented the proposed authentication algorithm in the 5G D2D-based communication package over NS-3 while performing the computational calculations on a RaspberryPi3 device to mimic the drone calculation process and delays. The performance of the proposed authentication shows a promising reduction in the authentication time and shows lightweight and reliable compatibility.

  PublisherOA PDFDOI

• Reliable and Secure Drone-assisted MillimeterWave Communications

  2021-03-19

  dissertationOpen access1st authorCorresponding

  The next generation of mobile networks and wireless communication, including the fifth-generation (5G) and beyond, will provide a high data rate as one of its fundamental requirements. Providing high data rates can be accomplished through communication over high-frequency bands such as the Millimeter-Wave(mmWave) one. However, mmWave communication experiences short-range communication, which impacts the overall network connectivity. Improving network connectivity can be accomplished through deploying Unmanned Ariel Vehicles(UAVs), commonly known as drones, which serve as aerial small-cell base stations. Moreover, drone deployment is of special interest in recovering network connectivity in the aftermath of disasters. Despite the potential advantages, drone-assisted networks can be more vulnerable to security attacks, given their limited capabilities. This security vulnerability is especially true in the aftermath of a disaster where security measures could be at their lowest. This thesis focuses on drone-assisted mmWave communication networks with their potential to provide reliable communication in terms of higher network connectivity measures, higher total network data rate, and lower end-to-end delay. Equally important, this thesis focuses on proposing and developing security measures needed for drone-assisted networks’ secure operation. More specifically, we aim to employ a swarm of drones to have more connection, reliability, and secure communication over the mmWave band. Finally, we target both the cellular 5Gnetwork and Ad hoc IEEE802.11ad/ay in typical network deployments as well as in post-disaster circumstances.

  PublisherOA PDFDOI

• A Proxy Signature-Based Drone Authentication in 5G D2D Networks

  2021 · 22 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Computer network

  5G is the beginning of a new era in cellular communication, bringing up a highly connected network with the incorporation of the Internet of Things (IoT). To flexibly operate all the IoT devices over a cellular network, Device-to-Device (D2D) communication standard was developed. However, IoT devices such as drones utilizing 5G D2D services could be a perfect target for malicious attacks as they pose several safety threats if they are compromised. Furthermore, there will be heavy traffic with an increased number of IoT devices connected to the 5G core. Therefore, we propose a lightweight, fast, and reliable authentication mechanism compatible with the 5G D2D ProSe standard mechanisms. Specifically, we propose a distributed authentication with a delegation-based scheme instead of the repeated access to the 5G core network key management functions. Hence, a legitimate drone is authorized by the core network via offering a proxy signature to authenticate itself to other drones. We implemented the proposed protocol in ns-3 that supports 5G D2D-based communication. We also conducted computational calculations on the RaspberryPi3 IoT device to mimic the drone calculation process and delays. The results demonstrate that the proposed protocol is lightweight and reliable.

  PublisherOA PDFDOI

Frequent coauthors

• Ahmed S. Ibrahim

  Florida International University

  10 shared

• Kemal Akkaya

  Florida International University

  7 shared

• Yahya Mohasseb

  Arab Academy for Science, Technology, and Maritime Transport

  5 shared

• Tamer ElBatt

  American University in Cairo

  5 shared

• Arupjyoti Bhuyan

  Idaho National Laboratory

  4 shared

• Mohamed Azab

  Virginia Military Institute

  4 shared

• Jeffrey H. Reed

  Virginia Tech

  3 shared

• Karim G. Seddik

  American University in Cairo

  3 shared

Education

• PhD Student, ECE

  Florida International University

  2021

• MSc., Wireless Technology

  Nile University

  2015

• BSc., Electrical Communications and Electronics

  Alexandria University Faculty of Engineering

  2013