Results 1 to 10 of about 1,185,392 (332)
Quantum adversarial machine learning [PDF]
Physical Review Research, 2020 Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations. It plays a vital
Sirui Lu, Lu-Ming Duan, Dong-Ling Deng
doaj +6 more sources
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning [PDF]
Pattern Recognition, 2018 Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity.
Biggio, Battista, Roli, Fabio
core +4 more sources
Future Internet, 2023 Concerns about cybersecurity and attack methods have risen in the information age. Many techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs), that help achieve security goals, such as detecting malicious attacks ...
Afnan Alotaibi, Murad A. Rassam
doaj +2 more sources
Detection of GPS Spoofing Attacks in UAVs Based on Adversarial Machine Learning Model. [PDF]
Sensors (Basel)Advancements in wireless communication and automation have revolutionized mobility systems, notably through autonomous vehicles and unmanned aerial vehicles (UAVs).
Alhoraibi L, Alghazzawi D, Alhebshi R.
europepmc +2 more sources
A System-Driven Taxonomy of Attacks and Defenses in Adversarial Machine Learning. [PDF]
IEEE Trans Emerg Top Comput Intell, 2020 Machine Learning (ML) algorithms, specifically supervised learning, are widely used in modern real-world applications, which utilize Computational Intelligence (CI) as their core technology, such as autonomous vehicles, assistive robots, and biometric ...
Sadeghi K, Banerjee A, Gupta SKS.
europepmc +2 more sources
Adversarial-Aware Deep Learning System Based on a Secondary Classical Machine Learning Verification Approach [PDF]
Sensors, 2023 Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes.
Mohammed Alkhowaiter +4 more
doaj +2 more sources
Adversarial Machine Learning on Social Network: A Survey
Frontiers in Physics, 2021 In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation.
Sensen Guo +5 more
doaj +2 more sources
Adversarial Machine Learning in Wireless Communications Using RF Data: A Review [PDF]
IEEE Communications Surveys and Tutorials, 2022 Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success ...
Damilola Adesina +3 more
openalex +3 more sources
Attack and Defense in Cellular Decision-Making: Lessons from Machine Learning
Physical Review X, 2019 Machine-learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signaling, like in early immune recognition.
Thomas J. Rademaker +2 more
doaj +2 more sources
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
doaj +2 more sources