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Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning
Information Systems Frontiers, 2020Since the inception of Andoroid OS, smartphones sales have been growing exponentially, and today it enjoys the monopoly in the smartphone marketplace.
Hemant Rathore +3 more
semanticscholar +1 more source
Selective Audio Adversarial Example in Evasion Attack on Speech Recognition System
IEEE Transactions on Information Forensics and Security, 2020Deep neural networks (DNNs) are widely used for image recognition, speech recognition, and other pattern analysis tasks. Despite the success of DNNs, these systems can be exploited by what is termed adversarial examples.
Hyun Kwon, Hyun Kwon, H. Yoon, D. Choi
semanticscholar +1 more source
Evasion Attacks with Adversarial Deep Learning Against Power System State Estimation
IEEE Power & Energy Society General Meeting, 2020Cyberattacks against critical infrastructures, including power systems, are increasing rapidly. False Data Injection Attacks (FDIAs) are among the attacks that have been demonstrated to be effective and have been getting more attention over the last ...
Ali Sayghe +2 more
semanticscholar +1 more source
Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey
IEEE Communications Surveys and Tutorials, 2023Network-based Intrusion Detection System (NIDS) forms the frontline defence against network attacks that compromise the security of the data, systems, and networks.
Ke He, Dan Dongseong Kim, M. R. Asghar
semanticscholar +1 more source
2022
The legal framework of the United States is commonly referred to as an adversarial system. Based on English common law, it established normative procedures that mediate conflicts between opposing parties, with a goal of minimizing the impact of governmental actions on entrenched personal rights.
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The legal framework of the United States is commonly referred to as an adversarial system. Based on English common law, it established normative procedures that mediate conflicts between opposing parties, with a goal of minimizing the impact of governmental actions on entrenched personal rights.
openaire +1 more source
Heterogeneous Multidomain Recommender System Through Adversarial Learning
IEEE Transactions on Neural Networks and Learning Systems, 2023To solve the user data sparsity problem, which is the main issue in generating user preference prediction, cross-domain recommender systems transfer knowledge from one source domain with dense data to assist recommendation tasks in the target domain with sparse data.
Wenhui Liao +4 more
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Adversarial Attack against LSTM-based DDoS Intrusion Detection System
IEEE International Conference on Tools with Artificial Intelligence, 2020Nowadays, machine learning is a popular method for DDoS detection. However, machine learning algorithms are very vulnerable under the attacks of adversarial samples.
Weiqing Huang +3 more
semanticscholar +1 more source
IEEE Transactions on Power Systems, 2019
This paper proposes a fully data-driven approach for PMU-based pre-fault dynamic security assessment (DSA) with incomplete data measurements. The generative adversarial network (GAN), which is an emerging unsupervised deep learning technique based on two
Chao Ren, Yan Xu
semanticscholar +1 more source
This paper proposes a fully data-driven approach for PMU-based pre-fault dynamic security assessment (DSA) with incomplete data measurements. The generative adversarial network (GAN), which is an emerging unsupervised deep learning technique based on two
Chao Ren, Yan Xu
semanticscholar +1 more source
Information gathering in adversarial systems
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures - SPAA '03, 2003In this paper we consider the problem of routing packets to a single destination in a dynamically changing network, where both the network and the packet injections are under adversarial control. Routing packets to a single destination is also known as information gathering.
Kishore Kothapalli, Christian Scheideler
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Asymmetry in Coevolving Adversarial Systems
2016 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), 2016Asymmetries in adversarial systems arise from differences in the "situations" of attackers and defenders, for instance corresponding to differences in information access or cost/benefit tradeoffs. While numerous studies have shown that asymmetry is important, less has been done to rigorously characterize its impact or specify methods by ...
Richard Colbaugh, Kristin Glass
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