The lack of open-access databases for auditory suspicious events' detection and classification algorithms is a hurdle for researchers while evaluating and comparing the performance of suspicious sound event detection and recognition systems. This paper introduces two databases called DASE and bi-DASE. DASE contains nine of the most common acoustic suspicious events with a total of 3105 sound event recordings of glass breaking, dog barking, scream, gunshot, explosion, alarm, sirens, door slams, and footsteps. Bi-DASE database is prepared by mixing two auditory suspicious events from DASE to generate 25 possible real-life scenarios. Both databases are divided into two sets as a training set and testing set. ANOVA and regression analysis are performed in regard to commonly used features, energy, 12 MFCCs, pitch range, spectral centroid frequency, spectral spread, spectral flatness, tonality, harmonics, zero-crossing rate, and fundamental frequency. All features are found to be significantly important based on their p-values for both databases, which are evaluated by designing two baseline systems using support vector machines and k-nearest neighbors algorithm. Experimental results are presented. The databases are available for free to the research community upon request.