Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking

For exhaustive formal verification, industrial-scale cyber-physical systems (CPSs) are often too large and complex, and lightweight alternatives (e.g., monitoring and testing) have attracted the attention of both industrial practitioners and academic researchers. Falsification is one popular testing method of CPSs utilizing stochastic optimization. In state-of-the-art falsification methods, the result of the previous falsification trials is discarded, and we always try to falsify without any prior knowledge. To concisely memorize such prior information on the CPS model and exploit it, we employ Black-box checking (BBC), which is a combination of automata learning and model checking. Moreover, we enhance BBC using the robust semantics of STL formulas, which is the essential gadget in falsification. Our experiment results suggest that our robustness-guided BBC outperforms a state-of-the-art falsification tool.Comment: Accepted to HSCC 202

Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack

In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required communication load on the band-limited network. We define a general Byzantine attack on the event-triggered multi-agent network system and characterize its negative effects on synchronization. The Byzantine agents are capable of intelligently falsifying their data and manipulating the underlying communication graph by altering their respective control feedback weights. We introduce a decentralized detection framework and analyze its steady-state and transient performances. We propose a way of identifying individual Byzantine neighbors and a learning-based method of estimating the attack parameters. Lastly, we propose learning-based control approaches to mitigate the negative effects of the adversarial attack

Philosophy and the practice of Bayesian statistics

A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework.Comment: 36 pages, 5 figures. v2: Fixed typo in caption of figure 1. v3: Further typo fixes. v4: Revised in response to referee

Further support for the role of heroism in human mate choice

This is an accepted manuscript of an article published by the American Psychological Association in Evolutionary Behavioral Sciences on 03-09-2020. The accepted version of the publication may differ from the final published version, accessible at https://psycnet.apa.org/doi/10.1037/ebs0000230.Although evidence suggests that altruistic behavior can act as a mating signal, little research has explored the role of heroism in mate choice. Previous research has focused on women only, ignoring the role of heroism in male mate choice. Here, we extended and replicated previous research on the role of heroism in human mate choice. Participants (N=276) rated how desirable targets were for a short-term and long-term relationship, which varied in heroism. The findings showed men and women reported higher desirability for heroic targets for long-term compared to short-term relationships, although this pattern was more prominent in women. These findings add support to the role of heroism in mate choice by exploring the role of heroism in male and female mate choice

Neuroimaging Research: From Null-Hypothesis Falsification to Out-of-sample Generalization

International audienceBrain imaging technology has boosted the quantification of neurobiological phenomena underlying human mental operations and their disturbances. Since its inception, drawing inference on neurophysiological effects hinged on classical statistical methods, especially, the general linear model. The tens of thousands variables per brain scan were routinely tackled by independent statistical tests on each voxel. This circumvented the curse of dimensionality in exchange for neurobiologically imperfect observation units, a challenging multiple comparisons problem, and limited scaling to currently growing data repositories. Yet, the always-bigger information granularity of neuroimaging data repositories has lunched a rapidly increasing adoption of statistical learning algorithms. These scale naturally to high-dimensional data, extract models from data rather than prespecifying them, and are empirically evaluated for extrapolation to unseen data. The present paper portrays commonalities and differences between long-standing classical inference and upcoming generalization inference relevant for conducting neuroimaging research

The Falsification Adaptive Set in Linear Models with Instrumental Variables that Violate the Exogeneity or Exclusion Restriction

For the classical linear model with an endogenous variable estimated by the method of instrumental variables (IVs) with multiple instruments, Masten and Poirier (2021) introduced the falsification adaptive set (FAS). When a model is falsified, the FAS reflects the model uncertainty that arises from falsification of the baseline model. It is the set of just-identified IV estimands, where each relevant instrument is considered as the just-identifying instrument in turn, whilst all other instruments are included as controls. It therefore applies to the case where the exogeneity assumption holds and invalid instruments violate the exclusion assumption only. We propose a generalized FAS that reflects the model uncertainty when some instruments violate the exogeneity assumption and/or some instruments violate the exclusion assumption. This FAS is the set of all possible just-identified IV estimands where the just-identifying instrument is relevant. There are a maximum of $k_{z}2^{k_{z}-1}$ such estimands, where $k_{z}$ is the number of instruments. If there is at least one relevant instrument that is valid in the sense that it satisfies the exogeneity and exclusion assumptions, then this generalized FAS is guaranteed to contain $\beta$ and therefore to be the identified set for $\beta$