  The Intent in Android plays an important role in inter-process and
intra-process communications. The implicit Intent that an application could
accept are declared in its manifest and are amongst the easiest feature to
extract from an apk. Implicit Intents could even be extracted online and in
real-time. So far neither the feasibility of developing an Intrusion Detection
System solely on implicit Intent has been explored, nor are any benchmarks
available of a malware classifier that is based on implicit Intent alone. We
demonstrate that despite Intent is implicit and well declared, it can provide
very intuitive insights to distinguish malicious from non-malicious
applications. We conducted exhaustive experiments with over 40 different
end-to-end Deep Learning configurations of Auto-Encoders and
Multi-Layer-Perceptron to create a benchmark for a malware classifier that
works exclusively on implicit Intent. Using the results from the experiments we
create an intrusion detection system using only the implicit Intents and
end-to-end Deep Learning architecture. We obtained an area-under-curve
statistic of 0.81, and accuracy of 77.2% along with false-positive-rate of 0.11
on Drebin dataset.
