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With the increase in cyber security attacks, organizations tend to use an intrusion detection system (IDS) based on machine learning. Through the years, IDS based on machine learning has shown their effectiveness in protecting one against attacks. Aside from the machine learning nature being a black-box, there is a possibility of adversaries that can mess up the classification model. Using machine learning in critical aspects such as the medical field and intrusion detection system can result in disastrous impacts on organizations if it is vulnerable to adversary attacks. This paper proposes a new defense approach based on denoising auto-encoder (DAE) to protect IDS from adversarial attacks. To verify the efficacy of the proposed defense mechanism in mitigating adversarial attacks, two datasets were used. The experimental results show that the proposed defense mechanism proves validity against four white-box attacks and one black-box attack. The system's accuracy under adversarial attack elevates from around 68% to 90% and 97% under normal conditions on the first dataset. Similarly, on the second dataset, the models' accuracy increases from 64 to 85% under normal conditions and adversarial attacks. [ABSTRACT FROM AUTHOR]

Securely handling the biometric information of an individual is still a major challenge in many applications. For this reason, many cancelable techniques are present which were proposed with an aim to provide security, but adversarial attacks like a similarity-based attack on popular methods have recently been reported in the literature. In this paper, we propose a random walk-based method for cancelable template generation (2-d random walk and circular random walk). The novelty of the proposed method is to generate a secure, distinct cancelable template from fingerprint data. Further, the proposed scheme is immune to different security attacks. It also ensures the randomness of the generated cancelable templates. Our proposed scheme achieves comparable performance in terms of average genuine acceptance rate of 97.02 % , average false acceptance rate of 0.13 % for different fingerprint data. Moreover, our proposed scheme has FAR (attack) of 11.11 % , which is very low compared to the state-of-the-art method (such as BioHashing 62.5%). [ABSTRACT FROM AUTHOR]