In recent years,the number of parameters of deep neural networks(DNNs)has been increasing rapidly.The training of DNNs is typically computation-intensive.As a result,many users leverage cloud computing and outsource t...In recent years,the number of parameters of deep neural networks(DNNs)has been increasing rapidly.The training of DNNs is typically computation-intensive.As a result,many users leverage cloud computing and outsource their training procedures.Outsourcing computation results in a potential risk called backdoor attack,in which a welltrained DNN would performabnormally on inputs with a certain trigger.Backdoor attacks can also be classified as attacks that exploit fake images.However,most backdoor attacks design a uniformtrigger for all images,which can be easilydetectedand removed.In this paper,we propose a novel adaptivebackdoor attack.We overcome this defect and design a generator to assign a unique trigger for each image depending on its texture.To achieve this goal,we use a texture complexitymetric to create a specialmask for eachimage,which forces the trigger tobe embedded into the rich texture regions.The trigger is distributed in texture regions,which makes it invisible to humans.Besides the stealthiness of triggers,we limit the range of modification of backdoor models to evade detection.Experiments show that our method is efficient in multiple datasets,and traditional detectors cannot reveal the existence of a backdoor.展开更多
Intrusion detection systems are increasingly using machine learning.While machine learning has shown excellent performance in identifying malicious traffic,it may increase the risk of privacy leakage.This paper focuse...Intrusion detection systems are increasingly using machine learning.While machine learning has shown excellent performance in identifying malicious traffic,it may increase the risk of privacy leakage.This paper focuses on imple-menting a model stealing attack on intrusion detection systems.Existing model stealing attacks are hard to imple-ment in practical network environments,as they either need private data of the victim dataset or frequent access to the victim model.In this paper,we propose a novel solution called Fast Model Stealing Attack(FMSA)to address the problem in the field of model stealing attacks.We also highlight the risks of using ML-NIDS in network security.First,meta-learning frameworks are introduced into the model stealing algorithm to clone the victim model in a black-box state.Then,the number of accesses to the target model is used as an optimization term,resulting in minimal queries to achieve model stealing.Finally,adversarial training is used to simulate the data distribution of the target model and achieve the recovery of privacy data.Through experiments on multiple public datasets,compared to existing state-of-the-art algorithms,FMSA reduces the number of accesses to the target model and improves the accuracy of the clone model on the test dataset to 88.9%and the similarity with the target model to 90.1%.We can demonstrate the successful execution of model stealing attacks on the ML-NIDS system even with protective measures in place to limit the number of anomalous queries.展开更多
As deep learning models have made remarkable strides in numerous fields,a variety of adversarial attack methods have emerged to interfere with deep learning models.Adversarial examples apply a minute perturbation to t...As deep learning models have made remarkable strides in numerous fields,a variety of adversarial attack methods have emerged to interfere with deep learning models.Adversarial examples apply a minute perturbation to the original image,which is inconceivable to the human but produces a massive error in the deep learning model.Existing attack methods have achieved good results when the network structure is known.However,in the case of unknown network structures,the effectiveness of the attacks still needs to be improved.Therefore,transfer-based attacks are now very popular because of their convenience and practicality,allowing adversarial samples generated on known models to be used in attacks on unknown models.In this paper,we extract sensitive features by Grad-CAM and propose two single-step attacks methods and a multi-step attack method to corrupt sensitive features.In two single-step attacks,one corrupts the features extracted from a single model and the other corrupts the features extracted from multiple models.In multi-step attack,our method improves the existing attack method,thus enhancing the adversarial sample transferability to achieve better results on unknown models.Our method is also validated on CIFAR-10 and MINST,and achieves a 1%-3%improvement in transferability.展开更多
Adversarial examples revealed the weakness of machine learning techniques in terms of robustness,which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning.Existing re...Adversarial examples revealed the weakness of machine learning techniques in terms of robustness,which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning.Existing researches covered the methodologies of adversarial example generation,the root reason of the existence of adversarial examples,and some defense schemes.However practical attack against real world systems did not appear until recent,mainly because of the difficulty in injecting a artificially generated example into the model behind the hosting system without breaking the integrity.Recent case study works against face recognition systems and road sign recognition systems finally abridged the gap between theoretical adversarial example generation methodologies and practical attack schemes against real systems.To guide future research in defending adversarial examples in the real world,we formalize the threat model for practical attacks with adversarial examples,and also analyze the restrictions and key procedures for launching real world adversarial example attacks.展开更多
文摘In recent years,the number of parameters of deep neural networks(DNNs)has been increasing rapidly.The training of DNNs is typically computation-intensive.As a result,many users leverage cloud computing and outsource their training procedures.Outsourcing computation results in a potential risk called backdoor attack,in which a welltrained DNN would performabnormally on inputs with a certain trigger.Backdoor attacks can also be classified as attacks that exploit fake images.However,most backdoor attacks design a uniformtrigger for all images,which can be easilydetectedand removed.In this paper,we propose a novel adaptivebackdoor attack.We overcome this defect and design a generator to assign a unique trigger for each image depending on its texture.To achieve this goal,we use a texture complexitymetric to create a specialmask for eachimage,which forces the trigger tobe embedded into the rich texture regions.The trigger is distributed in texture regions,which makes it invisible to humans.Besides the stealthiness of triggers,we limit the range of modification of backdoor models to evade detection.Experiments show that our method is efficient in multiple datasets,and traditional detectors cannot reveal the existence of a backdoor.
基金supported by Grant Nos.U22A2036,HIT.OCEF.2021007,2020YFB1406902,2020B0101360001.
文摘Intrusion detection systems are increasingly using machine learning.While machine learning has shown excellent performance in identifying malicious traffic,it may increase the risk of privacy leakage.This paper focuses on imple-menting a model stealing attack on intrusion detection systems.Existing model stealing attacks are hard to imple-ment in practical network environments,as they either need private data of the victim dataset or frequent access to the victim model.In this paper,we propose a novel solution called Fast Model Stealing Attack(FMSA)to address the problem in the field of model stealing attacks.We also highlight the risks of using ML-NIDS in network security.First,meta-learning frameworks are introduced into the model stealing algorithm to clone the victim model in a black-box state.Then,the number of accesses to the target model is used as an optimization term,resulting in minimal queries to achieve model stealing.Finally,adversarial training is used to simulate the data distribution of the target model and achieve the recovery of privacy data.Through experiments on multiple public datasets,compared to existing state-of-the-art algorithms,FMSA reduces the number of accesses to the target model and improves the accuracy of the clone model on the test dataset to 88.9%and the similarity with the target model to 90.1%.We can demonstrate the successful execution of model stealing attacks on the ML-NIDS system even with protective measures in place to limit the number of anomalous queries.
基金Supported by the Key R&D Projects in Hubei Province(2022BAA041 and 2021BCA124)the Open Foundation of Engineering Research Center of Cyberspace(KJAQ202112002)。
文摘As deep learning models have made remarkable strides in numerous fields,a variety of adversarial attack methods have emerged to interfere with deep learning models.Adversarial examples apply a minute perturbation to the original image,which is inconceivable to the human but produces a massive error in the deep learning model.Existing attack methods have achieved good results when the network structure is known.However,in the case of unknown network structures,the effectiveness of the attacks still needs to be improved.Therefore,transfer-based attacks are now very popular because of their convenience and practicality,allowing adversarial samples generated on known models to be used in attacks on unknown models.In this paper,we extract sensitive features by Grad-CAM and propose two single-step attacks methods and a multi-step attack method to corrupt sensitive features.In two single-step attacks,one corrupts the features extracted from a single model and the other corrupts the features extracted from multiple models.In multi-step attack,our method improves the existing attack method,thus enhancing the adversarial sample transferability to achieve better results on unknown models.Our method is also validated on CIFAR-10 and MINST,and achieves a 1%-3%improvement in transferability.
基金partially sponsored by Shanghai Sailing Program No.18YF1402200。
文摘Adversarial examples revealed the weakness of machine learning techniques in terms of robustness,which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning.Existing researches covered the methodologies of adversarial example generation,the root reason of the existence of adversarial examples,and some defense schemes.However practical attack against real world systems did not appear until recent,mainly because of the difficulty in injecting a artificially generated example into the model behind the hosting system without breaking the integrity.Recent case study works against face recognition systems and road sign recognition systems finally abridged the gap between theoretical adversarial example generation methodologies and practical attack schemes against real systems.To guide future research in defending adversarial examples in the real world,we formalize the threat model for practical attacks with adversarial examples,and also analyze the restrictions and key procedures for launching real world adversarial example attacks.
