Crypto mining attacks in containerized environments are not new.

Researchers have found different kinds of crypto mining activities running inside misconfigured containers.

Microsoft, in April, had disclosed large-scale crypto-mining attacks against Kubernetes clusters which were discovered by Azure Security Center, thus helping users protect Kubernetes clusters from security threats.

We have seen how Microsoft is very proactive in monitoring security threats. For cloud security, Azure Security Center (ASC) monitors and protects thousands of Kubernetes clusters running on top of Azure Kubernetes Service (AKS). Azure Security Center routinely searches for and does research on new attack vectors against Kubernetes workloads.

Recently, a new campaign was seen lately by ASC, which targeted Kubeflow, a machine learning toolkit for Kubernetes. Kubeflow is an open-source project, which began as a project for running TensorFlow workloads on Kubernetes.

Developers generally exhibit a strong affinity (usually paired with an equally strong hatred) for certain frameworks, libraries, and tools.

Which ones do they love, dread, and want the most?

Stack Overflow, as part of its enormous, annual Developers Survey, asked that very question, and the answers provide some interesting insights into how developers work.

Some 65,000 developers responded to the survey, and the sheer size of that sample makes these breakdowns a bit more interesting to parse. For example, although game developers might have strong opinions about Unreal Engine and Unity 3D (which placed high on the following lists), those aren’t used at all by the bulk of developers concerned with A.I. and machine learning, who have strong feelings about TensorFlow that many other developers might not share. In other words, given the high degree of specialization involved in many frameworks, libraries, and tools, it seems problematic to declare any of them the ‘most loved’ or ‘most dreaded’ among developers overall.

The outburst of COVID-19 cases reported globally has severely grown, impacting the day-to-day life of both organizations and individuals across the world.

It is now imperative to understand how long the pandemic might last and find effective ways to flatten the progression of COVID-19 cases and try to return to a new normal.

Research literature covers various statistical models (such as Gamma distribution, Negative Binomial distributions) and epidemiological models (such as SIR, SEIR) that are used to make predictions about the number of people infected with contagious diseases such as Ebola, SARS, MERS. However, the research on transmission rate, incubation period and other parameters that go into mathematical modelling of the spread of COVID-19 is still at a nascent stage with most of it yet to be peer-reviewed.