132,780 research outputs found
Adversarial behaviours knowledge area
The technological advancements witnessed by our society in recent decades have brought
improvements in our quality of life, but they have also created a number of opportunities for
attackers to cause harm. Before the Internet revolution, most crime and malicious activity
generally required a victim and a perpetrator to come into physical contact, and this limited
the reach that malicious parties had. Technology has removed the need for physical contact
to perform many types of crime, and now attackers can reach victims anywhere in the world, as long as they are connected to the Internet. This has revolutionised the characteristics of crime and warfare, allowing operations that would not have been possible before. In this document, we provide an overview of the malicious operations that are happening on the Internet today. We first provide a taxonomy of malicious activities based on the attacker’s motivations and capabilities, and then move on to the technological and human elements that adversaries require to run a successful operation. We then discuss a number of frameworks that have been proposed to model malicious operations. Since adversarial behaviours are not a purely technical topic, we draw from research in a number of fields (computer science, criminology, war studies). While doing this, we discuss how these frameworks can be used by researchers and practitioners to develop effective mitigations against malicious online operations.Published versio
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
We derive an algorithm that achieves the optimal (within constants)
pseudo-regret in both adversarial and stochastic multi-armed bandits without
prior knowledge of the regime and time horizon. The algorithm is based on
online mirror descent (OMD) with Tsallis entropy regularization with power
and reduced-variance loss estimators. More generally, we define an
adversarial regime with a self-bounding constraint, which includes stochastic
regime, stochastically constrained adversarial regime (Wei and Luo), and
stochastic regime with adversarial corruptions (Lykouris et al.) as special
cases, and show that the algorithm achieves logarithmic regret guarantee in
this regime and all of its special cases simultaneously with the adversarial
regret guarantee.} The algorithm also achieves adversarial and stochastic
optimality in the utility-based dueling bandit setting. We provide empirical
evaluation of the algorithm demonstrating that it significantly outperforms
UCB1 and EXP3 in stochastic environments. We also provide examples of
adversarial environments, where UCB1 and Thompson Sampling exhibit almost
linear regret, whereas our algorithm suffers only logarithmic regret. To the
best of our knowledge, this is the first example demonstrating vulnerability of
Thompson Sampling in adversarial environments. Last, but not least, we present
a general stochastic analysis and a general adversarial analysis of OMD
algorithms with Tsallis entropy regularization for and explain
the reason why works best
Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN
We propose a novel technique to make neural network robust to adversarial
examples using a generative adversarial network. We alternately train both
classifier and generator networks. The generator network generates an
adversarial perturbation that can easily fool the classifier network by using a
gradient of each image. Simultaneously, the classifier network is trained to
classify correctly both original and adversarial images generated by the
generator. These procedures help the classifier network to become more robust
to adversarial perturbations. Furthermore, our adversarial training framework
efficiently reduces overfitting and outperforms other regularization methods
such as Dropout. We applied our method to supervised learning for CIFAR
datasets, and experimantal results show that our method significantly lowers
the generalization error of the network. To the best of our knowledge, this is
the first method which uses GAN to improve supervised learning
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