Study of scale-free structures in feed-forward neural networks against backdoor attacks

Gu, 2017 Y. Liu, Sh. Ma, Y. Aafer, W. Lee, J. Zhai, W. Wang, X. Zhang, Trojaning attack on neural networks, in: 25nd Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, USA, February, 2018, pp. 18–221. Liu, 2017, Neural trojans, 45 Kaviani, 2020, Defense against neural trojan attacks: A survey, Neurocomputing B. Wang, Y. Yao, Sh. Shan, H. Li, B. Viswanath, H. Zheng, B.Y. Zhao, Neural cleanse: Identifying and mitigating backdoor attacks in neural networks, in: 2019 IEEE Symposium on Security and Privacy, SP, 2019, pp. 707–723. Liu, 2018, Fine-pruning: Defending against backdooring attacks on deep neural networks, 273 S. Kaviani, Insoo Sohn, Application of complex systems in neural networks against Backdoor attacks, in :ICTC 2020 Conference Proceedings and IEEE Xplore. Barabási, 1999, Emergence of scaling in random networks, Science, 286, 509, 10.1126/science.286.5439.509 Erkaymaz, 2014, Performance of small-world feedforward neural networks for the diagnosis of diabetes, Appl. Math. Comput., 311, 22, 10.1016/j.amc.2017.05.010 Kaviani, 2020, Influence of random topology in artificial neural networks: A survey, ICT Express, 6, 145, 10.1016/j.icte.2020.01.002