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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. 

The history of astronomy has shown that advances in sensing methods open up new windows to the Universe and often lead to unexpected discoveries. Quantum sensor networks in combination with traditional astronomical observations are emerging as a novel modality for multimessenger astronomy. Here we develop a generic analysis framework that uses a data-driven approach to model the sensitivity of a quantum sensor network to astrophysical signals as a consequence of beyond-the-standard model (BSM) physics. The analysis method evaluates correlations between sensors to search for BSM signals coincident with astrophysical triggers, such as black hole mergers, supernovae, or fast radio bursts. Complementary to astroparticle approaches that search for particlelike signals (e.g., weakly interacting massive particles), quantum sensors are sensitive to wavelike signals from exotic quantum fields. This analysis method can be applied to networks of different types of quantum sensors, such as atomic clocks, matter-wave interferometers, and nuclear clocks, which can probe many types of interactions between BSM fields and standard model particles. We use this analysis method to carry out the first direct search utilizing a terrestrial network of precision quantum sensors for BSM fields emitted during a black hole merger. Specifically, we use the global network of optical magnetometers for exotic physics (GNOME) to perform a search for exotic low-mass field (ELF) bursts generated in coincidence with a gravitational-wave signal from a binary black hole merger (GW200311_115853) detected by LIGO/Virgo on the March 11, 2020. The associated gravitational wave heralds the arrival of the ELF burst that interacts with the spins of fermions in the magnetometers. This enables GNOME to serve as a tool for multimessenger astronomy. Our search found no significant events and, consequently, we place the first lab-based limits on combinations of ELF production and coupling parameters. 

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.