Machine learning in adversarial environments.

Whenever machine learning is used to prevent illegal or unsanctioned activity and there is an economic incentive, adversaries will attempt to circumvent the protection provided. ... The four papers in this special issue provide a standard taxonomy of the types of attacks that can be expected in an adversarial framework, demonstrate how to design classifiers that are robust to deleted ... Introduction Machine learning techniques are increasingly used in environments where adversaries consciously act to limit or prevent accurate performance. ...

doi:10.1007/s10994-010-5207-6 fatcat:653z5mltdffnjf6kumh6ss4g4a

Pattern recognition systems are increasingly being used in adversarial environments like network intrusion detection, spam filtering and biometric authentication and verification systems, in which an adversary ... Their extension to adversarial settings is thus mandatory, to safeguard the security and reliability of pattern recognition systems in adversarial environments. ... In [2] some general issues about the security of machine learning systems in adversarial environments were discussed, and a taxonomy of attacks against them was developed. ...

doi:10.1007/s13042-010-0007-7 fatcat:mlfrbkm2pbfhxpk5fykddvbjom

In most of our experimental results, deep learning shows better resilience against the adversarial samples in comparison to machine learning. ... Classification of encrypted traffic can become more challenging in the presence of adversarial attacks that target the learning algorithms. ... of deep learning and machine learning in an adversarial-free and adversarial attack environment for each dataset, respectively. ...

arXiv:2105.14564v1 fatcat:w5rak5pa7neypfgtox5lzpqcai

This paper has proposed a mitigation method for adversarial attacks against proposed 6G machine learning models for the millimeter-wave (mmWave) beam prediction with adversarial learning. ... In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. The predictive algorithms will be used in 6G problems. ... vulnerable for adversarial machine learning attacks? ...

arXiv:2103.07268v1 fatcat:kmgv2c7ivzfz7g236u7bv46edi

In recent times, federated machine learning has been very useful in building intelligent intrusion detection systems for IoT devices. ... Further, our approach has been very successful in boosting the global model accuracy, up to 99% even in the presence of 40% adversaries. ... In this paper, we discussed the unsupervised clustering algorithm based approach in detecting adversaries in a federated machine learning-based IoT environment. ...

arXiv:2102.10799v1 fatcat:zjrru5vsevgt7o5bjmjdn3fhlm

Open Access

Most of the existing Adversarial Machine Learning approaches are focused on static images, and little work has been done in studying how to deal with 3D environments and how a 3D object should be altered ... In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in realistic virtual worlds ... Index Terms-Adversarial Machine Learning, Virtual Environments, Neural Networks, Computer Vision. I. ...

arXiv:2109.08465v1 fatcat:geszaihnnjhcvjpfyddosjmcyy

INDEX TERMS Adversarial detection, user-feedback-process, active machine learning, monitoring industrial feedback. ... Therefore, the authors posit the importance of detecting adversarial attacks in active learning strategy. ... They would also like to acknowledges the opinions, findings, and conclusions expressed in this article are purely of the authors. ...

doi:10.1109/access.2021.3063002 fatcat:kprni64f4fbu7ll5byymbmdg6q

DOAJ

Indeed, machine learning has become so intertwined with security that the technical community's ability to apply machine learning securely will likely be crucial to future environments. ... The Machine Learning in Adversarial Settings Patrick McDaniel, Nicolas Papernot, and Z. ... In 1982, on the occasion of its thirtieth anniversary, the IEEE Computer Society established the Computer Entrepreneur Award to recognize and honor the technical managers and entrepreneurial leaders who ...

doi:10.1109/msp.2016.51 fatcat:qbrhhmdqvnejba5f62nvrwujtu

Finally, we propose directions in technologies for secure learning and suggest lines of investigation into secure techniques for learning in adversarial environments. ... However, as more applications employ machine learning techniques in adversarial decision-making situations, increasingly powerful attacks become possible against machine learning systems. ... This work was supported in part by the Team for Research in Ubiquitous Secure Technology (TRUST), which receives support from the National Science Foundation ( ...

doi:10.1145/1456377.1456382 dblp:conf/ccs/BarrenoBCJNRST08 fatcat:4uk7kufh4zevfgxkvhz7t4qvm4

With the development of artificial intelligence, machine learning algorithms and deep learning algorithms are widely applied to attack detection models. ... In this paper, we present a method based on reinforcement learning (called RLXSS), which aims to optimize the XSS detection model to defend against adversarial attacks. ... The performance in terms of accuracy, recall, and F1 was superior to the traditional machine learning algorithms ADTree and AdaBoost. ...

doi:10.3390/fi11080177 fatcat:c5fcaqq3jjghfiblzvyyu63xqi

DOAJ Szczepanski

The stealthy nature causes difficulty and delay in detection of the attacks. In this paper, adversarial machine learning in the domain of CBM is introduced. ... Condition-based maintenance (CBM) strategies exploit machine learning models to assess the health status of systems based on the collected data from the physical environment, while machine learning models ... The obtained results in this paper reveal that understanding the applicability of adversarial machine learning attacks in CBM systems is necessary in order to develop more robust machine learning-based ...

arXiv:2101.12097v1 fatcat:ri26oskbdvahjifhxuaemmcyo4

Open Access

We leverage the insights from the interviews and we enumerate the gaps in perspective in securing machine learning systems when viewed in the context of traditional software security development. ... The goal of this paper is to engage researchers to revise and amend the Security Development Lifecycle for industrial-grade software in the adversarial ML era. ... the rise of adversarial machine learning. ...

arXiv:2002.05646v3 fatcat:i5xgtxpurneo5pxr6nuwvp6vly

Multiple Versions

In this paper, we argue that machine learning techniques are not ready for malware detection in the wild. ... Finally, we outline potential research directions in machine learning for malware detection. ... This because it is difficult to operate and deploy machine learning for malware detection in a production environment or the performance in a production environment is disturbing (e.g., high false positives ...

arXiv:1905.07573v1 fatcat:o2nv3rrua5gpzng3sm2zyxbwla

In this paper, we present a cyber-physical environment as an adversarial learning ecosystem for cloning image classification skills. ... In this paper, we present a cyber-physical environment as an adversarial learning ecosystem for cloning image classification skills. ... It would allow collective intelligence (integrated digital customers and humans) interacting with real services and products via their digital twins in cyber-physical environments. ...

doi:10.1016/j.procs.2021.01.155 fatcat:74qe4vvcurbftco53z45baupoq

Open Access

Applications and middleware in pervasive systems frequently rely on machine learning to provide adaptivity and customization that results in a seamless user experience despite operating in a dynamic environment ... Machine learning algorithms in pervasive systems frequently train on data that could be manipulated by a malicious 3rd party. ... ACKNOWLEDGEMENTS The material in this paper was supported through CAE Cybersecurity Grant H98230-15-1-0284. ...

doi:10.1109/percomw.2016.7457061 dblp:conf/percom/LagesseBP16 fatcat:parhvcb6mvahznnn4yjoezfuei

Machine learning in adversarial environments

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Multiple classifier systems for robust classifier design in adversarial environments

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Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks [article]

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Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case [article]

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Clustering Algorithm to Detect Adversaries in Federated Learning [article]

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Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects [article]

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Active Machine Learning Adversarial Attack Detection in the User Feedback Process

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Machine Learning in Adversarial Settings

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Open problems in the security of learning

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RLXSS: Optimizing XSS Detection Model to Defend Against Adversarial Attacks Based on Reinforcement Learning

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Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities [article]

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Adversarial Machine Learning – Industry Perspectives [article]

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The Curious Case of Machine Learning In Malware Detection [article]

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Towards digital cognitive clones for the decision-makers: adversarial training experiments

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Securing pervasive systems against adversarial machine learning

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