Software Engineering for Secure AI-based Systems (SESAIS) Research Lab

The Software Engineering for Secure AI-based Systems (SESAIS) Research Lab, directed by Dr. Damiano Torre at the University of Washington Tacoma, conducts interdisciplinary research at the intersection of software engineering, artificial intelligence, cybersecurity, and autonomous systems. The lab focuses on designing secure, reliable, trustworthy, and resilient AI-driven software systems capable of operating safely in dynamic and adversarial environments.

SESAIS investigates next-generation cybersecurity and software engineering challenges affecting autonomous platforms, including drones, connected vehicles, IoT ecosystems, cyber-physical systems, and distributed AI infrastructures. Our work spans areas such as intrusion detection systems, AI robustness and trustworthiness, privacy-preserving machine learning, federated learning security, secure software architectures, real-time monitoring of autonomous systems, and empirical evaluation of AI-enabled software technologies.

A major goal of the lab is to develop practical and scientifically rigorous solutions that improve the dependability of AI-based systems deployed in safety-critical and mission-critical environments. Through collaborations with academic researchers, industry leaders, and government organizations, SESAIS contributes to advancing the foundations of secure intelligent systems while addressing emerging threats in increasingly interconnected and autonomous digital ecosystems.

Research Icon Research Areas

News Icon News

SESAIS Lab at 2026 SET Research Showcase: Xiaoling Wei and Linda Miao were selected to present their posters — Poster 6 (Xiaoling Wei) and Poster 10 (Linda Miao). In addition, Xiaoling was chosen to deliver a lightning talk.
SESAIS Lab at ESEM 2025: Presented two recent research papers — Toward Real-Time Intrusion Detection for Autonomous Vehicles: A Vision for Deep Learning-Based Security Frameworks (Vision Paper) and Toward Enhancing Privacy Preservation of a Federated Learning CNN Intrusion Detection System in IoT: Method and Empirical Study (Journal-First Paper). Also had the honor of meeting UML co-creator Grady Booch during his keynote.
SESAIS Lab Undergraduate Research Openings: Download flyer — 2 positions (for two quarters) for UW Tacoma CSS undergraduates; Q1: TCSS 499, Q2: paid ~5 hours/week at $21.57/hr.
End of the Quarter Colloquium – Summer 2025: Noah Ogilvie presented the results of his research on developing an intrusion detection system (IDS) for autonomous drones using publicly available datasets.
The paper "Toward Enhancing Privacy Preservation of a Federated Learning CNN Intrusion Detection System in IoT: Method and Empirical Study" has been accepted to the Journal First Track at the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) 2025.
The paper "Toward Real-Time Intrusion Detection for Autonomous Vehicles: A Vision for Deep Learning-Based Security Frameworks" has been accepted to the Vision and Emerging Results Track at ESEM 2025.
End of the Quarter Colloquium – Spring 2025: Jacob Klymenko presented the results of his research on security attacks targeting autonomous drones.

Research Icon Students Involvement

The SESAIS Lab actively engages students in cutting-edge research projects. I am seeking highly motivated students to join our research group through the Ph.D. program in Computer Science and Systems at the University of Washington Tacoma. Ideal candidates should hold a bachelor's degree and have a strong interest in AI-driven cybersecurity research. If you are interested, please email me your CV and a brief research statement. Additionally, motivated undergraduate and master's students at UW Tacoma are warmly invited to participate in ongoing research projects under my supervision.

Current Students:

Xiaoling Wei, PhD CSS. Research Topic: Security of Autonomous Systems (TBD).

Kannika Armstrong, Master CSS. Research Topic: Optimizing Performance and Efficiency of Lattice-Based Post-Quantum Cryptography Algorithms in Resource-Constrained UAV Environments.

Chris Biju, Master CSS. Research Topic: Red-Team vs Blue-Team Evaluation Framework for AI-Based Autonomous Platforms.

Siddharth Jagota, Master CSS. Research Topic: Federated, Privacy-Aware Intrusion Detection for Heterogeneous Cloud and Edge Systems.

Linyue Duan, Master CSS. Research Topic: Empirical Study of Configuration, Testing, and Regression in Drone/Car Digital Twins.

Daniil Nahliuk, Undergraduate ECE. Research Topic: Lightweight, HE-Friendly Model Compression for Federated IDS on Resource-Constrained Autonomous Drones.

Linda Miao, Undergraduate CSS. Research Topic: Software-Engineering Benchmarks for Explainable Intrusion Detection Systems in Autonomous Platforms.

Graduated Students:

Jacob Klymenko, Undergraduate CSS. Research Topic: Security Attacks on Autonomous Drones. (2024-2025)

Noah Ogilvie, Undergraduate CSS. Research Topic: Development of an Intrusion Detection System for Autonomous Drones Using Public Datasets. (2024-2025)

Collaboration Icon Academic and Industry Collaboration

Below is a list of faculty members and idustry induviduals with whom there is ongoing research collaboration:

Davide Fucci, Associate Professor, Blekinge Institute of Technology, Sweden.

Mohammad Jasim, Assistant Professor, University of Washington Tacoma, USA.

Claudio Menghi, Asssociate Professor, University of Bergamo, Italy.

Amirpasha Javid, Director Research Partnerships, Quanser Consulting Inc., Canada.