This thesis proposes adaptable error detection technique for improving the availability of embedded systems, and in particular Automated Teller Machines (ATMs). The principles associated with immune-inspired techniques are exploited for detecting unforseen errors during run-time, since traditional techniques for error detection are usually limited to the knowledge available during design-time. Furthermore, the adaptable error detectors can be used to predict system failure well before it happens in order to improve overall system availability and/or maintainability. This thesis introduces a framework for realising adaptable error detection (AED), and demonstrates the effectiveness of an artificial immune system (AIS) as a technique for its implementation. Using data obtained from ATMs, the effectiveness of the AIS technique is evaluated based on the efficacy at detecting the incipience of failures. From the early awareness of impending failures, appropriate actions, such as error recovery or operator warning, can be initiated to prevent the deviaton of system's operations from correct service delivery. Alternatively, the foreknowledge of an imminent failure may quicken system repair with the effect that the downtime of the system is reduced and the system's availability is enhanced. The outcome of the investigations showed that the implemented AED could detect the antecedents to failure. The effects of the continuous learning feature were demonstrated in terms of: (1) a continual update of error detectors depending on new run-time bheaviours, and (2) an improvement in the detection capability by anticipating potential failures. Based on these results, I concluded that the adaptable error detection technique proposed is a step towards enhancing the availability of ATMs.