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<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">This is an announcement of Xi Jiang¡¯s MS Presentation</span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">===============================================</span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">Candidate:</span></b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> Xi Jiang</span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> </span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">Date:</span></b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> November 17, 2023 </span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> </span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">Time:</span></b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> 9am</span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> </span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">Location:</span></b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> JCL 298</span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> </span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">M.S. Paper Title</span></b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">: AC-DC: Adaptive Ensemble Classification for Network
Traffic Identification</span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> </span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">Abstract:</span></b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> Accurate and efficient network traffic classification is
important for many network management tasks, from traffic prioritization to anomaly detection. Although classifiers using pre-computed flow statistics (e.g., packet sizes, inter-arrival times) can be efficient, they often result in lower accuracy compared
with techniques based on raw traffic. Conversely, past work on representation learning-based classifiers applied to network traffic captures has been shown to be highly accurate, yet slow and requiring considerable additional system resources (e.g., memory),
due to the substantial costs in feature preprocessing. In this paper, we explore this trade-off and develop the Adaptive Constraint-Driven Classification (ACDC) framework to efficiently curate a pool of classifiers with different target requirements, aiming
to provide comparable classification performance to complex packet-capture classifiers while adapting to varying network traffic load. AC-DC uses an adaptive scheduler that tracks current system resource availability and traffic rates to determine a balanced
classifier and batch size to maximize classification performance given system and processing constraints. Our evaluation shows that AC-DC improves classification performance by over 18% compared to classifiers that rely on flow statistics alone; when benchmarked
against state-of-the-art packet-capture classifiers, AC-DC results in a worst-case drop in F1-Score of only 12.3% while processing traffic </span><span style="font-family:"Cambria Math",serif;color:#212121;mso-ligatures:none">¡</span><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">50x
faster.</span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> </span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">Advisors:</span></b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> Nick Feamster</span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> </span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none">Committee Members:</span></b><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"> Nick Feamster, Grant Ho, Sanjay Krishnan<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif;color:#212121;mso-ligatures:none"><o:p> </o:p></span></p>
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