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Future InternetDuring the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems.
Muhammad Imran +2 more
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Journal of Cybersecurity and Privacy, 2022 Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection.
Andrew McCarthy +3 more
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A Systematic Review of Adversarial Machine Learning and Deep Learning Applications
Al-Iraqia Journal for Scientific Engineering ResearchThe review delves into creating an understandable framework for machine learning in robotics. It stresses the significance of machine learning in materials science and robotics highlighting how it can transform industries by boosting efficiency and ...
Tabarak Ali Abdalkareem +2 more
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Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review
IEEE Access, 2020 Cyber-security is the practice of protecting computing systems and networks from digital attacks, which are a rising concern in the Information Age. With the growing pace at which new attacks are developed, conventional signature based attack detection ...
Nuno Martins +3 more
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Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
Journal of Cybersecurity and Privacy, 2021 As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought.
Pavlos Papadopoulos +5 more
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Journal of Information and Technology, 2023 Serangan adversarial pada citra digital merupakan ancaman serius bagi penggunaan teknologi machine learning dalam berbagai aplikasi kehidupan sehari-hari. Teknik Fast Gradient Sign Method (FGSM) telah terbukti efektif dalam melakukan serangan pada model
Putu Widiarsa Kurniawan S +2 more
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SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL
Tạp chí Khoa học Đại học Đà LạtArtificial intelligence (AI) has found applications across various sectors and industries, offering numerous advantages to human beings. One prominent area where AI has made significant contributions is in machine learning models.
Thanh Son Phan +3 more
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Enhancing adversarial robustness of quantum neural networks by adding noise layers
New Journal of Physics, 2023 The rapid advancements in machine learning and quantum computing have given rise to a new research frontier: quantum machine learning. Quantum models designed for tackling classification problems possess the potential to deliver speed enhancements and ...
Chenyi Huang, Shibin Zhang
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AutoBayes: Automated Bayesian Graph Exploration for Nuisance- Robust Inference
IEEE Access, 2021 Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning.
Andac Demir +3 more
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A survey of practical adversarial example attacks
Cybersecurity, 2018 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.
Lu Sun, Mingtian Tan, Zhe Zhou
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