  Due to the vast array of Android applications, their multifarious functions
and intricate behavioral semantics, attackers can adopt various tactics to
conceal their genuine attack intentions within legitimate functions. However,
numerous feature engineering based methods suffer from a limitation in mining
behavioral semantic information, thus impeding the accuracy and efficiency of
Android malware detection. Besides, the majority of existing feature
engineering based methods are weakly interpretive and fail to furnish
researchers with effective and readable detection reports. Inspired by the
success of the Large Language Models (LLMs) in natural language understanding,
we propose AppPoet, a LLM-assisted multi-view system for Android malware
detection. Firstly, AppPoet employs a static method to comprehensively collect
application features and formulate various observation views. Subsequently, it
steers the LLM to produce function descriptions and behavioral summaries for
views via our meticulously devised multi-view prompt engineering technique to
realize the deep mining of view semantics. Finally, we collaboratively fuse the
multi-view information to efficiently and accurately detect malware through a
deep neural network (DNN) classifier and then generate the heuristic diagnostic
reports. Experimental results demonstrate that our method achieves a detection
accuracy of 97.15% and an F1 score of 97.21%, which is superior to the baseline
method Drebin and its variant. Furthermore, the case study evaluates the
effectiveness of our generated diagnostic reports.
