Cyber attacks threaten economic interests, critical infrastructure, and public health and safety. To counter this, entities adopt cyber threat hunting, a proactive approach that involves formulating hypotheses and searching for attack patterns within organisational networks. Automating cyber threat hunting presents challenges, particularly in generating hypotheses, as it is a manually created and confirmed process, making it time-consuming. To address these challenges, we introduce APThreatHunter, an automated threat hunting solution that generates hypotheses with minimal human intervention, eliminating analyst bias and reducing time and cost. This is done by presenting possible risks based on the system's current state and a set of indicators to indicate whether any of the detected risks are happening or not. We evaluated APThreatHunter using real-world Android malware samples, and the results revealed the practicality of using automated planning for goal hypothesis generation in cyber threat hunting activities.

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