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Although deep neural networks perform well in many areas,research shows that deep neural networks are vulnerable to attacks from adversarial examples.There are many algorithms for attacking neural networks,but the attack speed of most attack algorithms is slow.Therefore,the rapid generation of adversarial examples has gradually become the focus of research in the area of adversarial examples.AdvGAN is an algorithm that uses the network to attack another network,which can generate adversarial samples extremely faster than other methods.However,when carrying out a targeted attack,AdvGAN needs to train a network for each target,so the efficiency of the attack is low.In this article,we propose a multi-target attack network(MTA) based on the generative adversarial network,which can complete multi-target attacks and quickly generate adversarial examples by training only once.Experiments show that MTA has a higher success rate for targeted attacks on the CIFAR10 and MNIST datasets than AdvGAN.We have also done adversarial sample transfer experiments and attack experiments under defense.The results show that the transferability of the adversarial examples generated by MTA is stronger than other multi-target attack algorithms,and our MTA method also has a higher attack success rate under defense.

Industrial security issues have always been an important and urgent issue globally.Industrial control protocols are widely used in the communication between industrial control system(ICS) components.Their security is related to the safe and stable operation of the entire system,and there is an urgent need to ensure the security of all industrial control protocols.The network protocol fuzzing plays an important role in ensuring the security and reliability of ICS.Traditional fuzzing methods can improve the security testing of industrial control protocols,and many of which have practical applications.However,most traditional fuzzing methods rely heavily on specifications of industrial control protocols,making the test process costly,time-consuming,cumbersome and boring.If the norm does not exist,the task is difficult to carry out.This paper proposes an intelligent and automatic protocol fuzzing method based on pointer-generation networks(PGN),and gives a series of performance indicators.On the basis of this method,an intelligent and automatic fuzzing framework based on PGNFuzz for application is designed,which can be used for various industrial control protocols.Several typical industrial control protocols such as Modbus and EtherCAT are used to test the validity and efficiency of our framework.Experiment results show that our method is superior to other general purpose fuzzers(GPF) and other deep learning based fuzzing methods in terms of convenience,effectiveness and efficiency.