Update index.html

index.html

@@ -118,7 +118,22 @@ <h4>Schedule and Materials</h4>
 		    </td>
                 </tr>
 
-
+
+
+
+		     <!-- Row 3.5 -->
+                <tr>
+                    <td></td>
+                    <td>Machine Learning Attack Surface</td>
+                    <td>covered within adversarial examples, training data poisoning, memberhip inference, and model extraction</td>
+                    <td>
+			<ol>
+			  		<li><a href="https://oaklandsok.github.io/papers/papernot2018.pdf" target="_blank"> Papernot et al., SoK: Security and Privacy in Machine Learning</a></li>
+			  		<li><a href="https://people.eecs.berkeley.edu/~adj/publications/paper-files/SecML-MLJ2010.pdf" target="_blank">Barreno et al., The security of machine learning</a></li>
+			  		<li><a href="https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2023.pdf" target="_blank">Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations</a></li>
+
+			</ol>
+		    </td>                </tr>
 
 		    <!-- Row 4 -->
                 <tr>
@@ -132,10 +147,7 @@ <h4>Schedule and Materials</h4>
 			</td>
                     <td>
 			<ol>
-			  		<li><a href="https://oaklandsok.github.io/papers/papernot2018.pdf" target="_blank"> Papernot et al., SoK: Security and Privacy in Machine Learning</a></li>
-			  		<li><a href="https://people.eecs.berkeley.edu/~adj/publications/paper-files/SecML-MLJ2010.pdf" target="_blank">Barreno et al., The security of machine learning</a></li>
-			  		<li><a href="https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2023.pdf" target="_blank">Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations</a></li>
-					<li><a href="https://arxiv.org/pdf/1312.6199.pdf" target="_blank">Szegedy et al., Intriguing properties of neural networks</a></li>
+			  		<li><a href="https://arxiv.org/pdf/1312.6199.pdf" target="_blank">Szegedy et al., Intriguing properties of neural networks</a></li>
 			  		<li><a href="https://arxiv.org/pdf/1602.02697.pdf" target="_blank">Papernot et al., Practical Black-Box Attacks against Machine Learning</a></li>
 			  		<li><a href="https://arxiv.org/pdf/1707.08945.pdf" target="_blank">Eykholt et al., Robust Physical-World Attacks on Deep Learning Visual Classification</a></li>
 					<li><a href="https://arxiv.org/pdf/1412.6572.pdf" target="_blank">Goodfellow et al., Explaining and Harnessing Adversarial Examples</a></li>