Presentation Attacks (PAs) are a common spoofing mechanism in biometric authentications, especially iris-based models. The detection of these attacks is useful for distinguishing whether a sensor is presented with a live biometric or impersonated biometric through a recording, printout or spoof In recent studies, Convolutional Neural Networks have shown exceptional performance in detecting these attacks. In this paper, we propose an attention-based iris PA detection (PAD) termed d-CBAM that uses a convolution block attention mechanism introduced between the dense blocks of DenseNet. The core of this work is inspired by the use of DenseNet as a feature extractor i.e, feature maps from the last dense block are taken and passed to the attention maps. We have tested d-CBAM on the benchmark Clarkson, Notre Dame and NDCLD15 datasets, over which d-CBAM has shown better results in comparison to some traditional PAD solutions such as DenseNet, Spoof Net and Meta-Fusion. The error metrics (APCER and BPCER) were also noted to be competitive with the state-of-the-art.