Merge pull request #177 from OWASP/develop

merge: develop

tab_glossary.md

@@ -56,23 +56,31 @@ tags: related-tag
 
 ## A {#a}
 
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+
+[Adversarial attack](#adversarial_attack)  
+Type of attack which seeks to trick machine learning models into misclassifying inputs by maliciously tampering with input data
 
 ## B {#b}
 
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 ## C {#c}
 
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+[Classification](#classification)  
+Process of arranging things in groups which are distinct from each other, and are separated by clearly determined lines of demarcation
 
 ## D {#d}
 
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+[Data labeling](#data_labeling)  
+Process of assigning tags or categories to each data point in a dataset
+
+[Data poisoning](#data_poisoning)  
+Type of attack that inject poisoning samples into the data
 
 ## E {#e}
 
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+[Ensemble](#ensemble)  
+See: [Model Ensemble](#model_ensemble)
 
 ## F {#f}
 
@@ -88,7 +96,11 @@ tags: related-tag
 
 ## I {#i}
 
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+[Intrusion Detection Systems (IDS)](#ids)  
+Security service that monitors and analyzes network or system events for the purpose of finding, and providing real-time or near real-time warning of, attempts to access system resources in an unauthorized manner.
+
+[Intrusion Prevention System (IPS)](#ips)  
+System that can detect an intrusive activity and can also attempt to stop the activity, ideally before it reaches its targets.
 
 ## J {#j}
 
@@ -104,15 +116,26 @@ tags: related-tag
 
 ## M {#m}
 
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+[MLOps](#mlops)  
+The selection, application, interpretation, deployment, and maintenance of machine learning models within an AI-enabled system
+
+[Model](#model)  
+Detailed description or scaled representation of one component of a larger system that can be created, operated, and analyzed to predict actual operational characteristics of the final produced component
+
+[Model ensemble](#model_ensemble)  
+Art of combining a diverse set of learners (individual models) together to improvise on the stability and predictive power of the model
 
 ## N {#n}
 
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 ## O {#o}
 
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+[Obfuscation](#obfuscation)  
+Defense mechanism in which details of the model or training data are kept secret by adding a large amount of valid but useless information to a data store 
+
+[Overfitting](#overfitting)  
+Overfitting is when a statistical model begins to describe the random error in the data rather than the relationships between variables. This occurs when the model is too complex
 
 ## P {#p}
 
@@ -124,19 +147,22 @@ tags: related-tag
 
 ## R {#r}
 
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+[Regularisation](#regularisation)  
+Controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting
 
 ## S {#s}
 
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+[Spam](#spam)  
+The abuse of electronic messaging systems to indiscriminately send unsolicited bulk messages
 
 ## T {#t}
 
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 ## U {#u}
 
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+[Underfitting](#underfitting)  
+Underfitting is when a data model is unable to capture the relationship between the input and output variables accurately, generating a high error rate on both the training set and unseen data
 
 ## V {#v}