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Because of the direct access to cash and customer data, automated teller machines (ATMs) are the target of manifold attacks and fraud. To counter this problem, modern ATMs utilize specialized hardware security systems that are designed to detect particular types of attacks and manipulation. However, such systems do not provide any protection against future attacks that are unknown at design time. In this paper, we propose an approach that is able to detect known as well as unknown attacks on ATMs and that does not require additional security hardware. The idea is to utilize automatic model generation techniques to learn patterns of normal behavior from the status information of standard devices comprised in an ATM; a significant deviation from the learned behavior is an indicator of a fraud attempt. We cast the identification of ATM fraud as a sequencebased anomaly detection problem, and we describe three specific methods that implement our approach. An empirical evaluation using a real-world data set that has been recorded on a public ATM within a time period of nine weeks shows promising results and underlines the practical applicability of the proposed approach.