Overview of Projects

The primary objective of this project is (i) to develop and evaluate machine learning (ML) algorithm that can detect interfering and anomalous users’ signals that fully/partially-overlap in time and frequency domains (ii) to use homomorphic encryption-based training of the ML algorithm for preserving privacy of users’ data. The methodology includes:

• Hypothesis Development: The students will first create a synthetic dataset in which 2-3 type of RF signals (e.g., 4G/5G, spread spectrum as anomaly, and radar) will be generated using standard-compliant MATLAB toolboxes and arranged in time-frequency domains so as to represent interference scenarios (full/partial overlaps, hidden anomaly signals). Thereafter, they will use state-of-the-art neural networks (e.g., Alexnet NN, etc.) to detect interference and anomalous RF signals. Next, the homomorphic encryption-based training will be explored and applied to this NN. It leverages homomorphic encryption, which allows computations to be performed directly on encrypted data without needing to decrypt it first, thereby ensuring reverse engineering back to the users’ RF signals becomes increasingly difficult.
• Experimental Design and Real-world Dataset Collection: Students will learn how to use software-defined radios (SDRs) to transmit and receive the above-mentioned RF signals (e.g., National Instrument’s USRP X310) using my previously designed open-source application programming interface (API) for RF dataset collection in collaboration with National Instruments and Northeastern University. They will generate a variety of fully/partially overlapping and hidden signal scenarios. These experiments will be structured to rigorously test the hypotheses. This real-world data will inherently capture wireless channel and transceiver impairments in the received signals.
• Analysis: The real-world collected data will be used to analyze the effectiveness of proposed solution in detecting interfering and anomaly users’ signals in shared spectrum context while preserving the privacy of spectrum data. Since this dataset will capture real-world artifacts, it will enable assessing the viability of the proposed cybersecurity solution in a real shared spectrum context.

Students will develop the following research skills through this project: (i) reading and understanding research papers, (ii) designing experiments, (iii) collecting datasets, (iv) writing new codes and adapting existing codes as per requirement, (v) analyzing results, and (vi) writing technical papers. To help them in learning these skills, there will be weekly meetings to brainstorm ideas, discuss issues faced by students, the strategies to overcome these issues, and student presentations (at a later stage). This will ensure that the students will develop critical thinking and will be able to progress and complete the project in the stipulated amount of time. The project will help student in acquiring technical programming skills in Python/MATLAB for implementing privacy-preserving NN for detecting interference and anomalous RF transmissions in shared spectrum applications. The students will get a hands-on experience in using SDRs and the RF dataset collection APIs to transmit and receive RF signals. This will enable them to collect real-world datasets not just for this project, but also in their future projects. Students will also get familiarized with different representations of RF signals, e.g. spectrograms, power spectral densities, etc., which will enable them to visually understand how signals and their interference will look in the time-frequency domains. These skills will enable students to carry out research effectively.

In this project, the students will majorly contribute to developing interference and anomaly detection algorithm in shared spectrum use-cases while preserving privacy of users’ data used in these algorithms. The students will generate synthetic dataset as an initial start point, design algorithm using Alexnet NN, write code-base in Python for including homomorphic encryption-based training, analyzing results on synthetic dataset. After this, students will design a real-world experiment using SDRs and collect dataset for the interfering and anomaly signals in shared spectrum cases. Next, they will analyze results of the designed algorithm on real dataset, draw conclusions, present those results in meeting, and write research paper to wrap up the project. Throughout the project, students will comprehensively document each and every steps. The main advantage of this project is that it will develop experimental, technical, and analytical skills of the student. The hands-on experience in using SDRs to set up experiments involving RF communication signals can be used in any other project related to using ML in next-generation wireless communication networks. The student will primarily work independently on focused aspects of the project, such as algorithm development and data analysis, while also collaborating with other team members to ensure seamless integration into the overall project. Regular group meetings, facilitated through Microsoft Teams/Webex, will offer valuable opportunities for the student to enhance collaboration skills by engaging with peers in related research areas. This balanced approach enables the student to develop deep expertise in a specific area while broadening their technical knowledge through networking and teamwork.

One specific topic on which an REU student would work on is adversarial attacks on vision-language navigation systems for robots. Research objectives include (i) perform a comparative evaluation of various prompt injection attacks on vision-language navigation systems (ii) evaluate the drop in performance of robot navigation systems as a result on adversarial attacks (iii) determine the effectiveness of defense strategies such as ensemble models on adversarial attacks. Students will use

state-of-the-art vision-language navigation systems available as open-source code such as NavGPT, LM-Nav, and VL-Map as a starting point for code development. Students will then implement various adversarial attacks such as prompt injection and adversarial inputs in addition to defense strategies such as ensemble models. Students will develop a hypothesis on the research objectives, design experiments to evaluate the hypotheses, write code and gather data to analyze the outcome of adversarial attacks as well as the effectiveness of defense strategies.

Students’ involvement in research activities will be focused on development of research skills such as experimental design, data analysis, and scientific writing. Experimental design and critical thinking skills will be taught and reinforced through weekly research discussions and brainstorming sessions. Scientific writing and communication skills will be honed through paper reading assignments, weekly presentations, and technical report writing. The research tasks assigned will allow the students to grow in technical abilities important in computer science research such as programming, package management, and data analysis. Students will be guided on using specific software tools such as ROS for robotic code development, PyTorch for deep learning, and Github for version control.

The REU students will mainly contribute to security algorithm design, data analysis, and development of a defense system for alleviating adversarial attacks on AI robotic systems. Roles and responsibilities include developing navigation models using Python and PyTorch on multiple platforms such as the Matterport 3D simulator and a physical Ghost Robotics Vision 60 unit. In addition, the students will use a test set to calculate overall algorithm performance metrics, including navigation error, success rate, and trajectory length. Even though students will mainly work independently on focused research thrusts, students will have the opportunity to develop collaboration skills through meetings with other students working on similar areas. Group meetings will be held on a regular basis and group work will be coordinated through Microsoft Teams. This allows students to acquire depth in a particular research area while maintaining breadth of technical knowledge by networking with peers.

An REU student will focus on one or more of the following research objectives:

1. Multimodal SDN Data Analysis:
   Collect and analyze SDN telemetry from an emulated SDN testbed (e.g., Mininet with an OpenFlow controller), including flow statistics, packet-in events, and rule updates.
2. AI-Based Intrusion Detection:
   Implement and compare baseline flow-based intrusion detection models with multimodal models that incorporate control-plane signals. Evaluate how detection performance changes under unseen attacks and traffic drift.
3. Explainability and Trust Analysis:
   Apply attribution or feature-importance methods to analyze how AI models make security decisions, identifying which network behaviors drive alerts and false positives.
4. Safe Response Exploration (Optional Extension):
   Study how detection confidence can inform safer responses, such as monitoring versus rate limiting, without destabilizing the SDN controller.

Students will formulate hypotheses, design experiments, analyze results, and document findings in a technical report or poster suitable for undergraduate research venues.

Through this project, students will develop:

• Research skills in experimental design, hypothesis testing, and data analysis
• Technical skills in Python programming, SDN simulation, and machine learning
• Experience with tools such as Mininet, OpenFlow controllers, PyTorch, and GitHub
• Scientific communication skills through paper reading, presentations, and report writing

Weekly mentoring meetings and group discussions will reinforce critical thinking and research ethics.

REU students will primarily contribute to:

• Designing and running SDN security experiments
• Implementing AI-based detection models
• Analyzing detection accuracy, false positives, and robustness
• Documenting results and presenting findings

Students will work independently on focused research tasks while participating in group meetings coordinated through Microsoft Teams, enabling collaboration and peer learning. This structure allows students to gain depth in SDN security research while developing teamwork and professional research skills.

This project focuses on evaluating the effectiveness of recent results on medium access control (MAC) algorithms that are designed to withstand jamming attacks in wireless networks. Utilizing MATLAB, the project involves setting up a simulated wireless network environment where different types of jamming attacks can be created and their impact on MAC algorithms can be evaluated. The REU students will learn several jamming-resistant MAC algorithms. Using MATLAB, the students will simulate a wireless network environment where they can implement and test various types of jamming strategies (i.e., constant, random, and reactive). Within this simulation, the students will implement the contention resolution algorithms. Data will be collected on key performance metrics, such as throughput, latency, packet delivery ratio, and collision rates. They will use statistical tools to analyze this data, looking to identify which algorithms are most effective at mitigating the impact of jamming. The culmination of their work will be a comprehensive report that details their methodologies, findings, and conclusions.

Students will develop a range of research skills. They will hone their abilities in experimental design by setting up and configuring simulations, allowing them to understand the impact of attacks on wireless communication.  Data analysis skills will be enhanced as students learn to gather, process, and interpret large sets of data to evaluate the effectiveness of different algorithms, using statistical methods to discern patterns and outcomes. Students will also gain experience with scientific writing through the preparation of a project report that conveys the technical content in a clear and structured manner. Critical thinking will be emphasized as they must assess the results, theorize on the implications, and suggest improvements or new areas of study based on their findings. The students will also develop technical programming skills for scripting in simulation environments using MATLAB. This includes setting up and conducting network simulations, where they'll configure parameters, implement jamming scenarios, and analyze network behaviors. Additionally, students will gain expertise in statistical analysis by employing MATLAB and R to analyze data from their simulations. Skills in statistical testing and data visualization will be important for evaluating the effectiveness of various algorithms and describing the main findings.  Finally, these findings will be presented in a document LaTeX for academic and technical reporting.

A primary objective of this project is to ensure that each student gains a comprehensive understanding across all relevant areas, including algorithm design, simulation of wireless communications, understanding of jamming strategies, data analysis, and technical writing. Students will be grouped to specialize in different aspects of jamming-resistant MAC algorithms, allowing them to delve deeply into specific algorithmic details. Additionally, as simulation designers, they will model wireless communications using an abstract time-slotted channel model that incorporates simple ternary feedback (indicating no transmission, a single transmission, or multiple transmissions) and established path loss effects. Different groups will explore and implement various path loss models to understand their impact on communication. Furthermore, each group will analyze the effectiveness of these algorithms using statistical methods, but individual students will take responsibility for analyzing and documenting the results for distinct jamming strategies and performance metrics. This structured approach ensures that while each student focuses on and is responsible for specific tasks, they also gain skills across a broad spectrum of the project’s scope.

As described above, students will work collaboratively in groups to explore different aspects of the project, such as algorithm design and simulation setup, and data analysis. Within these groups, individual students will take responsibility for specific tasks, like implementing different path loss models, analyzing distinct jamming strategies, and calculating performance metrics. Regular group meetings will facilitate the integration of individual work. This approach ensures that while students develop specialized skills independently, they also contribute to the overall project goals.

This project focuses on the design, implementation, and evaluation of IDSs for connected vehicles and EV charging infrastructure. Using simulation and data-driven analysis tools, students will model vehicular and charging network environments and study how cyberattacks manifest in network traffic and system behavior. Students will explore representative attack scenarios relevant to connected transportation systems, including denial-of-service attacks, spoofing, protocol abuse, and data manipulation targeting EV charging equipment and vehicle-to-infrastructure (V2I) communications. These attacks will be injected into simulated network environments that model interactions between vehicles, EV charging stations (EVSE), backend management systems, and supporting communication networks.

Within this environment, students will implement and evaluate intrusion detection techniques, including statistical anomaly detection and classical machine learning–based IDS methods. Key performance metrics, such as detection accuracy, false positive rate, detection latency, and computational overhead, will be collected and analyzed. Statistical tools will be used to compare the effectiveness of different IDS approaches under varying attack intensities, traffic conditions, and infrastructure configurations.

Students will develop a broad set of research and technical skills relevant to cybersecurity and intelligent transportation systems. They will gain experience in experimental design by constructing and configuring networked simulations that model realistic connected-vehicle and EV-charging environments. Through this process, students will develop an understanding of how cyber intrusions impact both network behavior and physical system operation. They will apply statistical analysis techniques to evaluate detection performance and identify trends across different attack and traffic scenarios. Programming skills will be developed through scripting and simulation, including implementing IDS algorithms, attack vectors, and performance evaluation pipelines.

Students will also gain experience in scientific and technical writing by preparing a structured project report using LaTeX, emphasizing clarity, rigor, and reproducibility. Critical thinking will be emphasized throughout the project as students interpret results, assess limitations of existing IDS approaches, and propose improvements or future research directions for securing connected vehicles and EV charging infrastructure.

A primary objective of this project is to ensure that each student gains a comprehensive understanding of intrusion detection in connected vehicular and charging environments, including network behavior, attack mechanisms, detection algorithms, and performance evaluation.  As simulation designers, each student will independently model specific communication paths within the connected-vehicle and EV-charging ecosystem, such as vehicle-to-charger, charger-to-backend, and vehicle-to-infrastructure interactions. Using abstracted network and protocol representations, students will configure realistic traffic patterns, system constraints, and operational assumptions. By varying parameters such as traffic load, protocol behavior, and deployment scenarios, each student will assess how these factors influence intrusion detection performance in their assigned subsystem.

Each student will also assume primary responsibility for a well-defined research task, such as implementing a particular attack class, evaluating a specific IDS performance metric (e.g., detection latency or false positives), or analyzing the security behavior of a distinct component of the EV charging infrastructure.

Students will build and analyze models of rumor spreading on social and communication networks, using graph and diffusion models to understand how malicious content travels. They will experiment with real or synthetic datasets that mimic phishing campaigns, scam messages, or coordinated rumor bursts, and design algorithms to flag suspicious patterns or high-risk propagation paths. Students will also investigate intervention mechanisms, such as warning signals, throttling strategies, or trusted-information injections, and evaluate how well these approaches slow or stop public information hacks.

Students are expected to attend weekly research meetings, maintain well-documented and reproducible code, and collaborate respectfully with other team members. Each student will take ownership of a focused subproblem. For example, building a rumor-spreading simulator, implementing a detection pipeline for phishing-style messages, or testing intervention strategies in simulated networks. By the end of the program, students will prepare a written report and public presentation that summarize their findings and reflect on the broader security and ethical implications of rumor spreading in everyday online environments.

Students are expected to attend weekly research meetings, maintain well-documented and reproducible code, and collaborate respectfully with other team members. Each student will take ownership of a focused subproblem. For example, building a rumor-spreading simulator, implementing a detection pipeline for phishing-style messages, or testing intervention strategies in simulated networks. By the end of the program, students will prepare a written report and public presentation that summarize their findings and reflect on the broader security and ethical implications of rumor spreading in everyday online environments.