Supervised learning: classifying/learning a single discrete value Assume that I have a single feature dataset X comprising of say a 1000 samples.  The feature is an integer that may take up any value upto 2^32. Among these, one particular value alone is considered malicious. The target/lable vector flags out the malicious value as 1 and others as 0.  What would be the right approach to train a model that could learn this single value and classify it as 1 and all else as 0?
I don't really see this as a learning problem really, if it's the same malicious value you have to watch out for..

Only one "positive" sample (corresponding to the malicious value) would be enough to find the malicious integer.

Have I misunderstood something in your question?