Results 261 to 270 of about 1,173,800 (295)

Persistent and Evasive Attacks Uncovered

Infosecurity, 2011
APTs – and more recently AETs – have divided industry experts in opinion and often been used to scaremonger.
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ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion

IEEE International Conference on Computer Vision, 2023
Adversarial camouflage has garnered attention for its ability to attack object detectors from any viewpoint by covering the entire object’s surface. However, universality and robustness in existing methods often fall short as the transferability aspect ...
Naufal Suryanto, Yongsu Kim, Harashta Tatimma Larasati, Hyoeun Kang, Thi-Thu-Huong Le, Yoonyoung Hong, Hunmin Yang, Se-Yoon Oh, Howon Kim   +8 more
semanticscholar   +1 more source

Evasion Attack in Show and Tell Model

2020 22nd International Conference on Advanced Communication Technology (ICACT), 2020
Recently, deep learning technology has been applied to various fields with high performance and various services. Image recognition is also used in various fields with high performance by incorporating deep learning technology. However, deep learning technology is vulnerable to evasion attacks that cause the model to be misclassified by modulating the ...
Dongseop Lee, Hyunjin Kim, Jaecheol Ryou
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Domain invariant feature extraction against evasion attack

International Journal of Machine Learning and Cybernetics, 2017
In the security application, an attacker might violate the data stationary assumption that is a common assumption in the most machine learning techniques. This problem named as the domain shift problem arises when training (source) and test (target) data follow different distributions.
Zeinab Khorshidpour, Jafar Tahmoresnezhad, Sattar Hashemi, Ali Hamzeh   +3 more
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pFedDef: Characterizing evasion attack transferability in federated learning

Softw. Impacts, 2023
Taejin Kim, Shubhranshu Singh, Nikhil Madaan, Carlee Joe-Wong   +3 more
semanticscholar   +1 more source

Complex-based optimization strategy for evasion attack

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2017
Machine learning has been widely used in security related applications, such as spam filter, network intrusion detection. In machine learning process, the test set and the training set usually have the same probability distribution and through the information of learning the training set, the malicious samples in the machine learning algorithm can ...
Shu Li, Yun Li
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GZOO: Black-Box Node Injection Attack on Graph Neural Networks via Zeroth-Order Optimization

IEEE Transactions on Knowledge and Data Engineering
The ubiquity of Graph Neural Networks (GNNs) emphasizes the imperative to assess their resilience against node injection attacks, a type of evasion attacks that impact victim models by injecting nodes with fabricated attributes and structures.
Hao Yu, Ke Liang, Dayu Hu, Wenxuan Tu, Chuan Ma, Sihang Zhou, Xinwang Liu   +6 more
semanticscholar   +1 more source

Automatic Evasion of Machine Learning-Based Network Intrusion Detection Systems

IEEE Transactions on Dependable and Secure Computing
Network intrusion detection systems (IDS) are often considered effective to thwart cyber attacks. Currently, state-of-the-art (SOTA) IDSs are mainly based on machine learning (ML) including deep learning (DL) models, which suffer from their own security ...
Haonan Yan, Xiaoguang Li, Wenjing Zhang, Rui Wang, Hui Li, Xingwen Zhao, Fenghua Li, Xiao-La Lin   +7 more
semanticscholar   +1 more source

Evasion Attacks and Countermeasures in Deep Learning-Based Wi-Fi Gesture Recognition

IEEE Transactions on Mobile Computing
Deep learning-based Wi-Fi sensing has received massive interest thanks to the prevalence of Wi-Fi technology. While deep learning techniques provide promising results in Wi-Fi sensing, there are only very few studies on the vulnerabilities against Wi-Fi ...
Guolin Yin, Junqing Zhang, Xinping Yi, Xuyu Wang   +3 more
semanticscholar   +1 more source

Spatio-temporal Graph-Based Generation and Detection of Adversarial False Data Injection Evasion Attacks in Smart Grids

IEEE Transactions on Artificial Intelligence
Smart power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim to address simple traditional false data injection attacks (FDIAs).
Abdulrahman Takiddin, Muhammad Ismail, Rachad Atat, E. Serpedin   +3 more
semanticscholar   +1 more source