Xenia Mountrouidou

Senior Security Researcher

Cyber adAPT

Xenia Mountrouidou is a Senior Security Researcher at Cyber adAPT with versatile experience in academia and industry. She has over 10 years of research experience in network security, machine learning, and data analytics for computer networks. She enjoys writing Python scripts to automate boring things, finding interesting patterns with machine learning algorithms, and researching novel intrusion detection techniques. Her research interests revolve around network security, Internet of Things, intrusion detection, and machine learning.

She has authored scholarly papers in the areas of network security and machine learning. She has presented her work at academic and industry conferences such as USENIX Security, IEEE Big Data, BSides Security, and Interop.

Download my resumé .

Interests

Network Security
Intrusion Detection
Machine Learning
IoT
Telemetry

Education

PhD in Computer Science, 2008

North Carolina State University

Skills

Network Security

Python

Machine Learning

Experience

Senior Security Researcher

Cyber adAPT

August 2022 – Present Texas

Responsibilities include:

Research in Network Security and IoT
Development of machine learning PoCs with security applications
Research in intrusion detection

Senior Consultant

Network to Code

May 2020 – August 2022 New York

Responsibilities include:

Develop software for network automation workflows
Create CI/CD pipelines
Telemetry

Assistant Professor of Computer Science

College of Charleston

August 2016 – May 2020 Charleston

Taught computer security, data structures, and software engineering. Research in network security.

Accomplishments

Cisco Certified DevNet Associate

Cisco Jul 2023 – Jul 2020

See certificate

Cisco Certified DevNet Specialist - Core

Cisco Jul 2021 – Jul 2024

See certificate

Red Hat Delivery Specialist - Automation

Red Hat Aug 2020 – Aug 2022

See certificate

Projects

Decoding cybersecurity data with ML and AI

The goal of this project is to invent creative ways of deciphering cybersecurity data with ML and AI. First, exploratory data analysis is used with traditional Python libraries and Generative AI models to get insights on black box data, visualize it and tell its story. Then several traditional ML techniques for feature engineering are used to derive useful data characteristics for detecting malicious behavior. Finally, ML classification and language models are fine tuned using the generated features for detection of anomalies in network traffic.

IoT Security Metrics

We cannot improve what we cannot measure (Kelvin). How does one quantify the absence of an adversary, a fault, or a weakness in a system? How can we define objective, repeatable, and reproducible metrics that evaluate the security of a network? This task is more complex at a time that the face of networking is changing by interconnecting devices such as webcameras, locks, and lights. These exciting and difficult questions are part of my work in quantitative security evaluation. I analyze data, create predictive models, and stream telemetry with modern open source tools.

Featured Publications

Thomas Setzler, Xenia Mountrouidou

January, 2021 In IEEE CCNC 2021

IoT Metrics and Automation for Security Evaluation

Internet of Things (IoT) devices are ubiquitous, with web cameras, smart refrigerators, and digital assistants appearing in homes, offices, and public spaces. However, these devices are lacking in security measures due to their low time to market and insufficient funding for security research and development. In order to improve the security of IoTs, we have defined novel security metrics based on generic IoT characteristics. Furthermore, we have developed automation for experimentation with IoT devices that results to repeatable and reproducible calculations of security metrics within a realistic IoT testbed. Our results demonstrate that repeatable IoT security measurements are feasible with automation. They prove quantitatively intuitive hypotheses. For example, an large number of inbound / outbound network connections contributes to higher probability of compromise or measuring password strength leads to a robust estimation of IoT security.

Casey Wilson, Xenia Mountrouidou, Anna Little

January, 2021 In IEEE Cyberhunt 2019

Worth the wait? Time window feature optimization for intrusion detection

Time as a variable for generating features has been widely overlooked in Intrusion Detection System (IDS) research. Computer and network attacks are time series, where time is an important factor that may affect feature generation, and as a result, classification. Nevertheless, there has been little exploration on how to calibrate time for IDSs and attack classification techniques. In this paper we explore time windows as a technique for generating more effective and descriptive features for attack classification. We suggest a framework for feature generation and selection that uses Recursive Feature Elimination (RFE) and time window exploration. Our initial results when applying this framework indicate that there is up to 47% improvement of F1 scores in attack classification when attack features are generated over a variety of time windows, compared to a single, global time window. We find that features calculated over longer lengths of time may be more useful for detecting attacks than over shorter lengths of time. Our methods seem to be most effective at detecting DDoS attacks, particularly those that occur over medium or long durations of time.