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‎_pages/home.md‎

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<!-- We are grateful for funding from Leiden University, [NWO](www.nwo.nl) ([Vidi talent scheme](http://www.nwo.nl/en/research-and-results/programmes/Talent+Scheme) and the [Frontiers in Nanoscience program](https://www.universiteitleiden.nl/en/research/research-projects/science/frontiers-of-nanoscience-nanofront)), and from an [ERC starting grant](https://erc.europa.eu/funding/starting-grants). -->
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<a href="https://www.utdallas.edu/" target="_blank"><img src="{{ site.url }}{{ site.baseurl }}/images/logopic/utd_line.jpg" style="width: 650px" alt="The University of Texas at Dallas"></a>
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<!-- <a href="https://www.nsf.gov/" target="_blank"><img src="{{ site.url }}{{ site.baseurl }}/images/logopic/nsf.jfif" style="width: 200px" alt="National Science Foundation"></a> -->
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<!-- <a href="https://cqn-erc.org/" target="_blank"><img src="{{ site.url }}{{ site.baseurl }}/images/logopic/cqn.png" style="width: 200px" alt="Center for Quantum Networks"></a> -->
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‎_pages/home_saved.md‎

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---
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title: "SysNetS Lab - Home"
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layout: homelay
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excerpt: "SysNetS Lab @ UTD"
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sitemap: true
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permalink: /
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---
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System and Network Security (SysNetS) lab is part of the Department of Computer Science at the<a href="https://www.utdallas.edu/" target="_blank"> University of Texas at Dallas <i class="fa fa-external-link"></i></a> lead by PI: <a href="https://www.imtiazkarim.net/" target="_blank"> Imtiaz Karim <i class="fa fa-external-link"></i></a>.
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<div markdown="0" id="carousel" class="carousel slide" data-ride="carousel" data-interval="4000" data-pause="hover" >
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<!-- Menu -->
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<a href="{{ site.url }}{{ site.baseurl }}/publications"><img src="{{ site.url }}{{ site.baseurl }}/images/slider7001400/5g.gif" alt="5G" /></a>
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<a href="{{ site.url }}{{ site.baseurl }}/publications"><img src="{{ site.url }}{{ site.baseurl }}/images/slider7001400/timesafe.png" alt="TIMESAFE" /></a>
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<a href="{{ site.url }}{{ site.baseurl }}/publications"><img src="{{ site.url }}{{ site.baseurl }}/images/slider7001400/spec5g.png" alt="SPEC5G" /></a>
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<a href="{{ site.url }}{{ site.baseurl }}/publications"><img src="{{ site.url }}{{ site.baseurl }}/images/slider7001400/FBSDetector.png" alt="Fake Base Station Detection" /></a>
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SysNetS Lab focuses on ensuring the security and privacy of wireless communication protocols (e.g., cellular networks-4G/5G, Bluetooth, VoWiFi, vehicular, WiFi, and IoT) with respect to their design and implementation. The aim is to develop tools that systematically analyze real-world systems and widely used protocols using formal verification, program analysis, machine learning, natural language processing, and software testing techniques. Furthermore, with the advent of the next generation of networks (6G and beyond), the lab's future goal is to ensure the resilience (reliability, adaptability, and security) of future network generations and develop protocols and systems that are robust and secure by design (see [Research](research)).
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**We are looking for passionate Ph.D. Master's and Bachelor's students at UTD to join the team** [(more info)]({{ site.url }}{{ site.baseurl }}/vacancies) **!**
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<!-- We are grateful for funding from Leiden University, [NWO](www.nwo.nl) ([Vidi talent scheme](http://www.nwo.nl/en/research-and-results/programmes/Talent+Scheme) and the [Frontiers in Nanoscience program](https://www.universiteitleiden.nl/en/research/research-projects/science/frontiers-of-nanoscience-nanofront)), and from an [ERC starting grant](https://erc.europa.eu/funding/starting-grants). -->
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<a href="https://www.utdallas.edu/" target="_blank"><img src="{{ site.url }}{{ site.baseurl }}/images/logopic/utd_line.jpg" style="width: 600px" alt="The University of Texas at Dallas"></a>
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<!-- <a href="https://www.nsf.gov/" target="_blank"><img src="{{ site.url }}{{ site.baseurl }}/images/logopic/nsf.jfif" style="width: 200px" alt="National Science Foundation"></a> -->
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<!-- <a href="https://cqn-erc.org/" target="_blank"><img src="{{ site.url }}{{ site.baseurl }}/images/logopic/cqn.png" style="width: 200px" alt="Center for Quantum Networks"></a> -->
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‎_pages/research.md‎

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## Secure Protocol Design and Defense:
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### TIMESAFE: Timing Interruption Monitoring and Security Assessment for Fronthaul Environments (arXiv 2024)
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5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthual (FH) connection between cellular baseband and radio unit equipment. Crucially, synchronization over the FH is pivotal for reliable 5G services. In recent years, there has been a push to move these links to an Ethernet-based packet network topology, leveraging existing standards and ongoing research for Time-Sensitive Networking (TSN). However, TSN standards, such as Precision Time Protocol (PTP), focus on performance with little to no concern for security. This increases the exposure of the open FH to security risks. Attacks targeting synchronization mechanisms pose significant threats, potentially disrupting 5G networks and impairing connectivity.
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5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthual (FH) connection between cellular baseband and radio unit equipment. Crucially, synchronization over the FH is pivotal for reliable 5G services. In recent years, there has been a push to move these links to an Ethernet-based packet network topology, leveraging existing standards and ongoing research for Time-Sensitive Networking (TSN). However, TSN standards, such as Precision Time Protocol (PTP), focus on performance with little to no concern for security. This increases the exposure of the open FH to security risks. Attacks targeting synchronization mechanisms pose significant threats, potentially disrupting 5G networks and impairing connectivity.
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In this paper, we demonstrate the impact of successful spoofing and replay attacks against PTP synchronization. We show how a spoofing attack is able to cause a production-ready O-RAN and 5G-compliant private cellular base station to catastrophically fail within 2 seconds of the attack, necessitating manual intervention to restore full network operations. To counter this, we design a Machine Learning (ML)-based monitoring solution capable of detecting various malicious attacks with over 97.5% accuracy.
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## Gotta Detect ’Em All: Fake Base Station and Multi-Step Attack Detection in Cellular Networks (Usenix Security 2025A)
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Fake base stations (FBSes) pose a significant security threat by impersonating legitimate base stations (BSes). Though efforts have been made to defeat this threat, up to this day, the presence of FBSes and the multi-step attacks (MSAs) stemming from them can lead to unauthorized surveillance, interception of sensitive information, and disruption of network services. Therefore, detecting these malicious entities is crucial to ensure the security and reliability of cellular networks. Traditional detection methods often rely on additional hardware, predefined rules, signal scanning, changes to protocol specifications, or cryptographic mechanisms that have limitations and incur huge infrastructure costs in accurately identifying FBSes. In this paper, we develop FBSDetector–an effective and efficient detection solution that can reliably detect FBSes and MSAs from layer-3 network traces using machine learning (ML) at the user equipment (UE) side.
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![]({{ site.url }}{{ site.baseurl }}/images/respic/FBSDetector.png){: style="width: 950px; float: left; margin: 5px 15px 0px 0px;"}
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To develop FBSDetector, we create FBSAD and MSAD, the first-ever high-quality and large-scale datasets incorporating instances of FBSes and 21 MSAs. These datasets capture the network traces in different real-world cellular network scenarios (including mobility and different attacker capabilities) incorporating legitimate BSes and FBSes. Our novel ML framework, specifically designed to detect FBSes in a multi-level approach for packet classification using stateful LSTM with attention and trace level classification and MSAs using graph learning, can effectively detect FBSes with an accuracy of 96% and a false positive rate of 2.96%, and recognize MSAs with an accuracy of 86% and a false positive rate of 3.28%. We deploy FBSDetector as a real-world solution to protect end-users through a mobile app and validate it in real-world environments. Compared to the existing heuristic-based solutions that fail to detect FBSes, FBSDetector can detect FBSes in the wild in real time.
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**Protocol noncompliance checking**
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‎images/respic/FBSDetector.png‎

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