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• W4206683875 abstract "With the huge services provided by machine learning systems in our daily life, the attacks on these services are increasing every day. The attackers are trying to distort the functionality of these services and change their real duty by falsifying the function using the principle of intoxication. The poisoned system gives the unauthorized person the right to enter and exit the system as a legal person at anytime and anywhere. This could degrade the credibility of systems built using intelligent technologies. The paper extensively introduces the mechanisms of a data poisoning attack. Data poisoning attacks target systems based on machine learning technology, with explanations of the attack mechanisms targeting data sources and the intelligence model during either the training or testing phases. Defense methods presented by researchers in this field have also been described by defense strategies presented in the literature. The risks and effects caused by this attack are also described, and what are the future solutions that give opportunities for researchers working in this field to avoid and repel this attack perfectly." @default.
• W4206683875 created "2022-01-25" @default.
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• W4206683875 date "2021-01-01" @default.
• W4206683875 modified "2023-10-12" @default.
• W4206683875 title "Threats on Machine Learning Technique by Data Poisoning Attack: A Survey" @default.
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• W4206683875 doi "https://doi.org/10.1007/978-981-16-8059-5_36" @default.
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