This paper introduces AMAL, an operational automated and behavior-based malware analysis and labeling (classification and clustering) system that addresses many limitations and shortcomings of the existing academic and industrial systems. AMAL consists of two subsystems, AutoMal and MaLabel. AutoMal provides tools to collect low granularity behavioral artifacts that characterize malware usage of the file system, memory, network, and registry, and does that by running malware samples in virtualized environments. On the other hand, MaLabel uses those artifacts to create representative features, use them for building classifiers trained by manually-vetted training samples, and use those classifiers to classify malware samples into families similar in behavior. AutoMal also enables unsupervised learning, by implementing multiple clustering algorithms for samples grouping. An evaluation of both AutoMal and MaLabel based on medium-scale (4,000 samples) and largescale datasets (more than 115,000 samples)—collected and analyzed by AutoMal over 13 months—show AMAL’s effectiveness in accurately characterizing, classifying, and grouping malware samples. MaLabel achieves a precision of 99.5% and recall of 99.6% for certain families’ classification, and more than 98% of precision and recall for unsupervised clustering. Several benchmarks, costs estimates and measurements highlight and support the merits and features of AMAL.