  As algorithmic trading and electronic markets continue to transform the
landscape of financial markets, detecting and deterring rogue agents to
maintain a fair and efficient marketplace is crucial. The explosion of large
datasets and the continually changing tricks of the trade make it difficult to
adapt to new market conditions and detect bad actors. To that end, we propose a
framework that can be adapted easily to various problems in the space of
detecting market manipulation. Our approach entails initially employing a
labelling algorithm which we use to create a training set to learn a weakly
supervised model to identify potentially suspicious sequences of order book
states. The main goal here is to learn a representation of the order book that
can be used to easily compare future events. Subsequently, we posit the
incorporation of expert assessment to scrutinize specific flagged order book
states. In the event of an expert's unavailability, recourse is taken to the
application of a more complex algorithm on the identified suspicious order book
states. We then conduct a similarity search between any new representation of
the order book against the expert labelled representations to rank the results
of the weak learner. We show some preliminary results that are promising to
explore further in this direction
