Results 1 to 10 of about 1,143,792 (206)
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
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 +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
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
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
Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning [PDF]
Algorithms, 2021 Cyber security is used to protect and safeguard computers and various networks from ill-intended digital threats and attacks. It is getting more difficult in the information age due to the explosion of data and technology.
Shruti Patil +6 more
openalex +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
Adversarial Machine Learning in Text Processing: A Literature Survey
IEEE Access, 2022 Machine learning algorithms represent the intelligence that controls many information systems and applications around us. As such, they are targeted by attackers to impact their decisions.
Izzat Alsmadi +11 more
doaj +2 more sources
eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics
IEEE Access, 2022 Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and
Ivan Vaccari +4 more
doaj +2 more sources