Per-host DDoS mitigation by direct-control reinforcement learning

DDoS attacks plague the availability of online services today, yet like many cybersecurity problems are evolving and non-stationary. Normal and attack patterns shift as new protocols and applications are introduced, further compounded by burstiness and seasonal variation. Accordingly, it is difficult to apply machine learning-based techniques and defences in practice. Reinforcement learning (RL) may overcome this detection problem for DDoS attacks by managing and monitoring consequences; an agent’s role is to learn to optimise performance criteria (which are always available) in an online manner. We advance the state-of-the-art in RL-based DDoS mitigation by introducing two agent classes designed to act on a per-flow basis, in a protocol-agnostic manner for any network topology. This is supported by an in-depth investigation of feature suitability and empirical evaluation. Our results show the existence of flow features with high predictive power for different traffic classes, when used as a basis for feedback-loop-like control. We show that the new RL agent models can offer a significant increase in goodput of legitimate TCP traffic for many choices of host density

Revisiting the Classics: Online RL in the Programmable Dataplane

Data-driven networking is becoming more capable and widely researched, partly driven by the efficacy of Deep Reinforcement Learning (DRL) algorithms. Yet the complexity of both DRL inference and learning force these tasks to be pushed away from the dataplane to hosts, harming latency-sensitive applications. Online learning of such policies cannot occur in the dataplane, despite being useful techniques when problems evolve or are hard to model.We present OPaL—On Path Learning—the first work to bring online reinforcement learning to the dataplane. OPaL makes online learning possible in constrained SmartNIC hardware by returning to classical RL techniques—avoiding neural networks. Our design allows weak yet highly parallel SmartNIC NPUs to be competitive against commodity x86 hosts, despite having fewer features and slower cores. Compared to hosts, we achieve a 21 × reduction in 99.99th tail inference times to 34 µs, and 9.9 × improvement in online throughput for real-world policy designs. In-NIC execution eliminates PCIe transfers, and our asynchronous compute model ensures minimal impact on traffic carried by a co-hosted P4 dataplane. OPaL’s design scales with additional resources at compile-time to improve upon both decision latency and throughput, and is quickly reconfigurable at runtime compared to reinstalling device firmware

Substrate-guided optimization of the syringolins yields potent proteasome inhibitors with activity against leukemia cell lines

Natural products that inhibit the proteasome have been fruitful starting points for the development of drug candidates. Those of the syringolin family have been underexploited in this context. Using the published model for substrate mimicry by the syringolins and knowledge about the substrate preferences of the proteolytic subunits of the human proteasome, we have designed, synthesized, and evaluated syringolin analogs. As some of our analogs inhibit the activity of the proteasome with second-order rate constants 5-fold greater than that of the methyl ester of syringolin B, we conclude that the substrate mimicry model for the syringolins is valid. The improvements in in vitro potency and the activities of particular analogs against leukemia cell lines are strong bases for further development of the syringolins as anti-cancer drugs.National Institutes of Health (U.S.) (Grant AI-16892

Seiðr: Dataplane Assisted Flow Classification Using ML

Real-time, high-speed flow classification is fundamental for network operation tasks, including reactive and proactive traffic engineering, anomaly detection and security enhancement. Existing flow classification solutions, however, do not allow operators to classify traffic based on fine-grained, temporal dynamics due to imprecise timing, often rely on sampled data, or only work with low traffic volumes and rates. In this paper, we present Seiðr, a classification solution that: (i) uses precision timing, (ii) has the ability to examine every packet on the network, (iii) classifies very high traffic volumes with high precision. To achieve this, Seiðr exploits the data aggregation and timestamping functionality of programmable dataplanes. As a concrete example, we present how Seiðr can be used together with Machine Learning algorithms (such as CNN, k -NN) to provide accurate, real-time and high-speed TCP congestion control classification, separating TCP BBR from its predecessors with over 88–96% accuracy and F1-score of 0.864-0.965, while only using 15.5 MiB of memory in the dataplane

Carbon Dynamics During the Formation of Sea Ice at Different Growth Rates

Controlled laboratory experiments have shed new light on the potential importance of brine rejection during sea-ice formation for carbon dioxide sequestration in the ocean. We grew ice in an experimental seawater tank (1 m3) under abiotic conditions at three different air temperatures (−40°C, −25°C, −15°C) to determine how different ice growth rates affect the allocation of carbon to ice, water, or air. Carbonate system parameters were determined by discrete sampling of ice cores and water, as well as continuous measurements by multiple sensors deployed mainly in the water phase. A budgetary approach revealed that of the initial total inorganic carbon (TIC) content of the water converted to ice, only 28–29% was located in the ice phase by the end of the experiments run at the warmest temperature, whereas for the coldest ambient temperature, 46–47% of the carbon remained in the ice. Exchange with air appeared to be negligible, with the majority of the TIC remaining in the under-ice water (53–72%). Along with a good correlation between salinity and TIC in the ice and water samples, these observations highlight the importance of brine drainage to TIC redistribution during ice formation. For experiments without mixing of the under-ice water, the sensor data further suggested stronger stratification, likely related to release of denser brine, and thus potentially larger carbon sequestration for ice grown at a colder temperature and faster growth rate

Study protocol for a randomised controlled trial of invasive versus conservative management of primary spontaneous pneumothorax

INTRODUCTION: Current management of primary spontaneous pneumothorax (PSP) is variable, with little evidence from randomised controlled trials to guide treatment. Guidelines emphasise intervention in many patients, which involves chest drain insertion, hospital admission and occasionally surgery. However, there is evidence that conservative management may be effective and safe, and it may also reduce the risk of recurrence. Significant questions remain regarding the optimal initial approach to the management of PSP

Rational Design of Selective and Bioactive Inhibitors of the Mycobacterium Tuberculosis Proteasome

The 20S core particle of the proteasome in Mycobacterium tuberculosis (Mtb) is a promising, yet unconventional, drug target. This multimeric peptidase is not essential, yet degrades proteins that have become damaged and toxic via reactions with nitric oxide (and/or the associated reactive nitrogen intermediates) produced during the host immune response. Proteasome inhibitors could render Mtb susceptible to the immune system, but they would only be therapeutically viable if they do not inhibit the essential 20S counterpart in humans. Selective inhibitors of the Mtb 20S were designed and synthesized on the bases of both its unique substrate preferences and the structures of substrate-mimicking covalent inhibitors of eukaryotic proteasomes called syringolins. Unlike the parent syringolins, the designed analogues weakly inhibit the human 20S (Hs 20S) proteasome and preferentially inhibit Mtb 20S over the human counterpart by as much as 74-fold. Moreover, they can penetrate the mycobacterial cell envelope and render Mtb susceptible to nitric oxide-mediated stress. Importantly, they do not inhibit the growth of human cell lines in vitro and thus may be starting points for tuberculosis drug development.National Institutes of Health (U.S.) (Grant AI-16892

Population health and nurse education – time to step-up

Highlights•Contemporary trends in population health threaten the sustainability of current approaches to care delivery.•Health care professionals inevitably confront social injustices in their day-to-day work.•Nurses are ideally placed to make a critical impact on the health of populations.•Nurse educators need to create curricula which meaningfully integrate population health.•We outline three exemplars of innovative pedagogical approaches to spark the thinking of educators as to how they can enable nurses to make connections between practice and population health

Variability in RT-qPCR assay parameters indicates unreliable SARS-CoV-2 RNA quantification for wastewater surveillance

Due to the coronavirus disease 2019 (COVID-19) pandemic, wastewater surveillance has become an important tool for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within communities. In particular, reverse transcription-quantitative PCR (RT-qPCR) has been used to generate large datasets aimed at detecting and quantifying SARS-CoV-2 RNA in wastewater. Although RT-qPCR is rapid and sensitive, there is no standard method yet, there are no certified quantification standards, and experiments are conducted using different assays, reagents, instruments, and data analysis protocols. These variations can induce errors in quantitative data reports, thereby potentially misleading interpretations, and conclusions. We review the SARS-CoV-2 wastewater surveillance literature focusing on variability of RT-qPCR data as revealed by inconsistent standard curves and associated parameters. We find that variation in these parameters and deviations from best practices, as described in the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines suggest a frequent lack of reproducibility and reliability in quantitative measurements of SARS-CoV-2 RNA in wastewater

Mental health, physical symptoms and biomarkers of stress during prolonged exposure to Antarctica’s extreme environment

The Antarctic environment is characterized by many of the same extreme stressors as long-duration space flight (LDSE), thereby providing a useful earth-based analog for examining changes in and predictors of mental health over time. At coastal (n = 88) and inland (n = 22) Antarctic stations we tracked mental health symptoms across a nine-month period including winter-over using the Mental Health Checklist (MHCL; Bower et al., 2019). Our monthly assessment battery also examined changes in physical complaints, biomarkers of stress, and the use of different emotion regulation strategies. MHCL positive adaptation scores showed linear decreases whereas MHCL poor self-regulation scores and severity of physical symptoms increased across the study period. During-mission use of emotion regulation strategies and dehydroepiandrosterone (DHEA) levels predicted end-of-study MHCL scores, whereas trait-based psychological measures collected at the start of the mission showed little predictive utility. Results suggest that interventions and counter measures aimed at enhancing positive affect/emotion during prolonged exposure to extreme environments may be useful in reducing psychological risk