diff --git a/EET1/MLCS_conference/main.tex b/EET1/MLCS_conference/main.tex
index 969ba35..370441f 100644
--- a/EET1/MLCS_conference/main.tex
+++ b/EET1/MLCS_conference/main.tex
@@ -91,7 +91,7 @@ Anon. Anonymous
 
     Side-channel emissions provide an independent and extrinsic source of information about the system, purely based on the physical by-product of its activities.
     Leveraging side-channel information, we propose a physics-based \gls{ids} as an additional layer of protection for embedded systems.
-    The physic-based \gls{ids} uses machine-learning-based power analysis to monitor and assess the behaviour and integrity of network equipment. 
+    The physics-based \gls{ids} uses machine-learning-based power analysis to monitor and assess the behaviour and integrity of network equipment. 
 
     The \gls{ids} successfully detects three different classes of attacks on an HP Procurve Network Switch 5406zl: (i)~firmware manipulation with \numprint[\%]{99} accuracy, (ii)~brute-force SSH login attempts with \numprint[\%]{98} accuracy, and (iii)~hardware tampering with \numprint[\%]{100} accuracy.
     The machine-learning models require a small number of power traces for training and still achieve a high accuracy for attack detection.