Description: The term ‘false negative’ refers to an error in data reporting where a test result incorrectly indicates the absence of a condition that is actually present. In the context of cybersecurity, a false negative can occur when a security system fails to identify an attack or threat, leading to a false sense of security. This type of error is critical as it can allow attackers to operate undetected, compromising the integrity and confidentiality of data. False negatives are particularly problematic in systems that rely on accurate anomaly detection, as they may result in the omission of significant security events. In the realm of artificial intelligence and machine learning, false negatives can arise in classification models where the model fails to correctly identify a positive class. Minimizing false negatives is a key objective in the design of security systems and the implementation of detection algorithms, as a high number of these errors can undermine the effectiveness of the security measures in place.
