  As the use of Deep Neural Networks (DNNs) becomes pervasive, their
vulnerability to adversarial attacks and limitations in handling unseen classes
poses significant challenges. The state-of-the-art offers discrete solutions
aimed to tackle individual issues covering specific adversarial attack
scenarios, classification or evolving learning. However, real-world systems
need to be able to detect and recover from a wide range of adversarial attacks
without sacrificing classification accuracy and to flexibly act in {\bf unseen}
scenarios. In this paper, UNICAD, is proposed as a novel framework that
integrates a variety of techniques to provide an adaptive solution.
  For the targeted image classification, UNICAD achieves accurate image
classification, detects unseen classes, and recovers from adversarial attacks
using Prototype and Similarity-based DNNs with denoising autoencoders. Our
experiments performed on the CIFAR-10 dataset highlight UNICAD's effectiveness
in adversarial mitigation and unseen class classification, outperforming
traditional models.
