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Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted network traffic. Second, we review state-of-the-art techniques and methodologies in traffic analysis. Our aim is to provide insights into current practices and future directions in encrypted traffic analysis and classification, especially machine-learning-based analysis. 

Earth can act as a transducer to convert ultralight bosonic dark matter (axions and hidden photons) into an oscillating magnetic field with a characteristic pattern across its surface. Here we describe the first results of a dedicated experiment, the Search for Noninteracting Particles Experimental Hunt, that aims to detect such dark-matter-induced magnetic-field patterns by performing correlated measurements with a network of magnetometers in relatively quiet magnetic environments (in the wilderness far from human-generated magnetic noise). Our experiment constrains parameter space describing hidden-photon and axion dark matter with Compton frequencies in the 0.5–5.0 Hz range. Limits on the kinetic-mixing parameter for hidden-photon dark matter represent the best experimental bounds to date in this frequency range. 

In the context of insiders, preventive security measures have a high likelihood of failing because insiders ought to have sufficient privileges to perform their jobs. Instead, in this paper, we propose to treat the insider threat by a detective measure that holds an insider accountable in case of violations. However, to enable accountability, we need to create causal models that support reasoning about the causality of a violation. Current security models (e.g., attack trees) do not allow that. Still, they are a useful source for creating causal models. In this paper, we discuss the value added by causal models in the security context. Then, we capture the interaction between attack trees and causal models by proposing an automated approach to extract the latter from the former. Our approach considers insider-specific attack classes such as collusion attacks and causal-model-specific properties like preemption relations. We present an evaluation of the resulting causal models’ validity and effectiveness, in addition to the efficiency of the extraction process. 

Autonomous vehicles are equipped with multiple high-resolution sensors and cameras for an accurate local view of their surroundings. Equally important, they will need to exchange such high data-rate among each other for a wider view of their environments. The use of high-bandwidth millimeter-wave (mmWave) spectrum bands in vehicular communications can satisfy such demand for high data-rate exchange. Before attempting to design any mmWave vehicular communication system, there is a need to fully understand the propagation characteristics of such mmWave mobile environment. In this paper, we leverage the ray tracing capabilities in the WinProp software suite and study the propagation characteristics of mmWave channels in vehicular communications. In doing so, we present the implementation of the Vehicle-to-Infrastructure (V2I) communication scenario in WinProp. Via simulation results, we are able to show that approximately 20 dB degradation of signal strength can happen within 5 seconds. 

Abstract Human exposure to pathogenic viruses in environmental waters results in a significant global disease burden. Current microbial water quality monitoring approaches, mainly based on fecal indicator bacteria, insufficiently capture human health impacts posed by pathogenic viruses in water. The emergence of the ‘microbiome era’ and high-throughput metagenome sequencing has led to the discovery of novel human-associated viruses, including both pathogenic and commensal viruses in the human microbiome. The discovery of novel human-associated viruses is often followed by their detection in wastewater, highlighting the great diversity of human-associated viruses potentially present in the water environment. Novel human-associated viruses provide a rich reservoir to develop viral water quality management tools with diverse applications, such as regulating wastewater reuse and monitoring agricultural and recreational waters. Here, we review the pathway from viral discovery to water quality monitoring tool, and highlight select human-associated viruses identified by metagenomics and subsequently detected in the water environment (namely Bocavirus, Cosavirus, CrAssphage, Klassevirus, and Pepper Mild Mottle Virus). We also discuss research needs to enable the application of recently discovered human-associated viruses in water quality monitoring, including investigating the geographic distribution, environmental fate, and viability of potential indicator viruses. Examples suggest that recently discovered human pathogens are likely to be less abundant in sewage, while other human-associated viruses (e.g., bacteriophages or viruses from food) are more abundant but less human-specific. The improved resolution of human-associated viral diversity enabled by metagenomic tools provides a significant opportunity for improved viral water quality management tools. 

Abstract Numerous observations suggest that there exist undiscovered beyond‐the‐standard‐model particles and fields. Because of their unknown nature, these exotic particles and fields could interact with standard model particles in many different ways and assume a variety of possible configurations. Here, an overview of the global network of optical magnetometers for exotic physics searches (GNOME), the ongoing experimental program designed to test a wide range of exotic physics scenarios, is presented. The GNOME experiment utilizes a worldwide network of shielded atomic magnetometers (and, more recently, comagnetometers) to search for spatially and temporally correlated signals due to torques on atomic spins from exotic fields of astrophysical origin. The temporal characteristics of a variety of possible signals currently under investigation such as those from topological defect dark matter (axion‐like particle domain walls), axion‐like particle stars, solitons of complex‐valued scalar fields (Q‐balls), stochastic fluctuations of bosonic dark matter fields, a solar axion‐like particle halo, and bursts of ultralight bosonic fields produced by cataclysmic astrophysical events such as binary black hole mergers are surveyed.