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On Behavior Classification in Adversarial Environments [chapter]

Patrick Riley, Manuela Veloso
2000 Distributed Autonomous Robotic Systems 4  
We present an approach to doing adaptation which relies on classification of the current adversary into predefined adversary classes.  ...  In order for robotic systems to be successful in domains with other agents possibly interfering with the accomplishing of goals, the agents must be able to adapt to the opponents' behavior.  ...  Affecting an effective change in behavior based on the classification. There should be some mapping from adversary classes onto strategies that our agents' may use.  ... 
doi:10.1007/978-4-431-67919-6_35 fatcat:de6mapeyvrf2hfe37g4d35sh3q

Stealing Deep Reinforcement Learning Models for Fun and Profit [article]

Kangjie Chen, Shangwei Guo, Tianwei Zhang, Xiaofei Xie, Yang Liu
2020 arXiv   pre-print
with the environment.  ...  Based on this observation, our methodology first builds a classifier to reveal the training algorithm family of the targeted black-box DRL model only based on its predicted actions, and then leverages  ...  We evaluate the effectiveness of adversarial examples in Atari Pong environment. The target black-box model can use one training algorithm and configurations.  ... 
arXiv:2006.05032v2 fatcat:qa2vtsycnbco7ppzyyv2ngxhwi

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

Mariia Golovianko, Svitlana Gryshko, Vagan Terziyan, Tuure Tuunanen
2021 Procedia Computer Science  
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

Modeling adversarial intent for interactive simulation and gaming: the fused intent system

Eugene Santos, Jr., Bruce McQueary, Lee Krause, Dawn A. Trevisani
2008 Modeling and Simulation for Military Operations III  
The Fused Intent System (FIS) aims to address these deficiencies by providing an environment that answers 'what' the adversary is doing, 'why' they are doing it, and 'how' they will react to coalition  ...  In this paper, we describe our approach to FIS which includes adversarial 'softfactors' such as goals, rationale, and beliefs within a computational model that infers adversarial intent and allows the  ...  Subsystem based on a general classification of events by the Observable Inference Subsystem.  ... 
doi:10.1117/12.782203 fatcat:cm7ufec3evhy7kbivhltqml3zi

Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation

Zhong Meng, Jinyu Li, Yifan Gong, Biing-Hwang Juang
2018 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
It learns to handle the speaker and environment variability inherent in and restricted to the speech signal in the target domain without proactively addressing the robustness to other likely conditions  ...  and simultaneously, to min-maximize the condition classification loss.  ...  Here, one condition refers to one particular speaker or one acoustic environment.  ... 
doi:10.1109/icassp.2018.8461682 dblp:conf/icassp/MengLGJ18 fatcat:qj2osqass5anti5s5q7h54irti

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications [article]

Lauren J. Wong, William H. Clark IV, Bryse Flowers, R. Michael Buehrer, Alan J. Michaels, William C. Headley
2020 arXiv   pre-print
A major driver for the usage of deep machine learning in the context of wireless communications is that little, to no, a priori knowledge of the intended spectral environment is required, given that there  ...  sensing applications such as signal detection, estimation, and classification (termed here as Radio Frequency Machine Learning, or RFML).  ...  It should also be noted that while this section focuses primarily on attacks on signal classification, the adversarial attacks can be more broadly applied to other RFML tasks.  ... 
arXiv:2010.00432v1 fatcat:mxnvorh5wrfwzmxg4ezpbj4xve

Design of intentional backdoors in sequential models [article]

Zhaoyuan Yang, Naresh Iyer, Johan Reimann, Nurali Virani
2019 arXiv   pre-print
However, current published research on trojan attacks mainly focuses on classification problems, which ignores sequential dependency between inputs.  ...  In contrast to adversarial examples, backdoor or trojan attacks embed surgically modified samples with targeted labels in the model training process to cause the targeted model to learn to misclassify  ...  Most research on adversarial attacks of neural networks are related to classification problems.  ... 
arXiv:1902.09972v1 fatcat:f44cjjodmra3be5umshlczzkxa

Models and Framework for Adversarial Attacks on Complex Adaptive Systems [article]

Vahid Behzadan, Arslan Munir
2017 arXiv   pre-print
Furthermore, we propose a comprehensive set of schemes for classification of attacks and attack surfaces in CAS, complemented with examples of practical attacks.  ...  We also discuss potential mitigation techniques, and remark on future research directions in analysis and design of secure complex adaptive systems.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the NSF.  ... 
arXiv:1709.04137v1 fatcat:risynvwcrffbddmogtwg5cmcli

Design Pattern Classifiers under Attack for Security Evaluation using Multimodal System

Rupali Baliram Navalkar, Prof. Rajeshri R Shelke
2017 International Journal of Trend in Scientific Research and Development  
Reported results show that security evaluation can provide a more complete understanding of the classifier's behaviour in adversarial environments, and lead to better design choices.  ...  Pattern classification theory and design methods to adversarial settings.  ...  Reported results show that security evaluation can provide a more complete understanding of the classifier's behavior in adversarial environments, and lead to better design choices.  ... 
doi:10.31142/ijtsrd97 fatcat:monqkmon4rdldlqjhdglayk4mu

Adversarial Behavior in Multi-agent Systems [chapter]

Martin Rehák, Michal Pěchouček, Jan Tožička
2005 Lecture Notes in Computer Science  
By basing ourselves on the valid and accepted results from economics, law and conflict theory, we propose a consistent definition of adversariality in the multi-agent systems and discuss the characteristics  ...  Adversariality of the agents with respect to the multi-agent system can be a serious issue in the design of open multi-agent systems.  ...  Similar classification was done in [12] , but focused on interaction between different types of agents rather than on definition of types of behavior and didn't use the conflict theory.  ... 
doi:10.1007/11559221_47 fatcat:zqwununadvcwjcnjmkf3v6dl6m

Design of secure and robust cognitive system for malware detection [article]

Sanket Shukla
2022 arXiv   pre-print
Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images.  ...  Results demonstrate that this technique is successful in differentiating classes of malware based on the features extracted.  ...  Then they monitored the behavior of all the malware in a sandbox environment which generated a behavioral report.  ... 
arXiv:2208.02310v1 fatcat:q5bsmv7jnncx7izydpdqjvkkl4

'Security Theater': On the Vulnerability of Classifiers to Exploratory Attacks [chapter]

Tegjyot Singh Sethi, Mehmed Kantardzic, Joung Woo Ryu
2017 Lecture Notes in Computer Science  
However, classifiers operating in adversarial domains are vulnerable to evasion attacks by an adversary, who is capable of learning the behavior of the system by employing intelligently crafted probes.  ...  Classification accuracy in such domains provides a false sense of security, as detection can easily be evaded by carefully perturbing the input samples.  ...  In an adversarial environment, the accuracy of classification has little significance if the deployed classifier can be easily evaded by an intelligent adversary [11] .  ... 
doi:10.1007/978-3-319-57463-9_4 fatcat:tzo3x4r5rbbchfgitbk776abtq

Path-finding in dynamic environments with PDDL-planners

Vladimir Estivill-Castro, Jonathan Ferrer-Mestres
2013 2013 16th International Conference on Advanced Robotics (ICAR)  
We show here that, provided that the adversaries follow a deterministic behavior, PDDLplanners can also be used in dynamic environments where uncontrollable adversaries may obstruct paths at some time  ...  The output of these classical planners is a plan as sequence of actions for the controllable robots in the environment.  ...  In the first scenario we keep one adversary fixed, while another does move under a deterministic behavior (specified by a LLFSM).  ... 
doi:10.1109/icar.2013.6766456 dblp:conf/icar/Estivill-Castro13 fatcat:j4bbbjzclngkfdijkwdv4l4xyy

Obfuscation of Malicious Behaviors for Thwarting Masquerade Detection Systems Based on Locality Features

Jorge Maestre Vidal, Marco Antonio Sotelo Monge
2020 Sensors  
In recent years, dynamic user verification has become one of the basic pillars for insider threat detection.  ...  Consequently, it is assumed that masqueraders are unaware of the protected environment within the targeted organization, so it is expected that they move in a more erratic manner than legitimate users  ...  same features presented in the reference repository. • The classification algorithms were applied upon the adversarial datasets and the variation on accuracy results was measured to cross-validate the  ... 
doi:10.3390/s20072084 pmid:32272806 fatcat:kzwek2ag7jaqxehrvyepu3fnte

Modeling Friends and Foes [article]

Pedro A. Ortega, Shane Legg
2018 arXiv   pre-print
How can one detect friendly and adversarial behavior from raw data?  ...  Detecting whether an environment is a friend, a foe, or anything in between, remains a poorly understood yet desirable ability for safe and robust agents.  ...  Acknowledgments We thank Marc Lanctot, Bernardo Pires, Laurent Orseau, Victoriya Krakovna, Jan Leike, Neil Rabinowitz, and David Balduzzi for comments on an earlier manuscript.  ... 
arXiv:1807.00196v1 fatcat:6wxvuirqx5ftrb7g33gd6hgrv4
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