The Robustness of Machine Learning Models Using MLSecOps: A Case Study On Delivery Service Forecasting

Saputra, Adi; Suryani, Erma; Rakhmawati, Nur Aini

doi:10.1109/icts58770.2023.10330833

2023 14th International Conference on Information &Amp; Communication Technology and System (ICTS) 2023

DOI: 10.1109/icts58770.2023.10330833

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The Robustness of Machine Learning Models Using MLSecOps: A Case Study On Delivery Service Forecasting

Adi Saputra,

Erma Suryani,

Nur Aini Rakhmawati

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2024

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Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions

Mengara,

Avila,

Falk

2024

IEEE Access

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Deep neural network (DNN) classifiers are potent instruments that can be used in various security-sensitive applications. Nonetheless, they are vulnerable to certain attacks that impede or distort their learning process. For example, backdoor attacks involve polluting the DNN learning set with a few samples from one or more source classes, which are then labelled as a target class set by an attacker. Even if the DNN is trained on clean samples with no backdoors, this attack will still be successful if a backdoor pattern exists in the training data. Backdoor attacks are difficult to spot and can be used to make the DNN behave maliciously depending on the target selected by the attacker. In this study, we survey the literature and highlight the latest advances in backdoor attack strategies and defense mechanisms. We finalize the discussion on challenges and open issues, as well as future research opportunities.

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Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions

Mengara,

Avila,

Falk

2024

IEEE Access

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show abstract

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The Robustness of Machine Learning Models Using MLSecOps: A Case Study On Delivery Service Forecasting

Cited by 1 publication

References 20 publications

Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions

Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions

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