The distributed denial-of-service (DDoS) which knocked KrebsOnSecurity offline for days cost owners of devices unwittingly involved in the attack upwards of $300,000, researchers suggest. The DDoS attack took place in 2016 and was made possible through the Mirai botnet, a network of enslaved Internet of Things (IoT) devices including routers, surveillance cameras, and smart home systems. Non-existent or poor security practices, including the use of hardcoded and factory passwords, allowed the operators of the botnet to scour the web for the means to hook up and enslave these devices, providing the bandwidth necessary to launch an attack able to smash the KrebsOnSecurity domain and prevent legitimate traffic from getting through. The access disruption was an annoyance for visitors and a severe headache for Akamai, which used to host the renowned security expert's blog pro bono. The cost of the attack to the cloud security provider in fending off the 620 Gbps DDoS assault, which could have eventually reached millions of dollars, led to Google's Project Shield offering to take on the blog.
Most high-performance expert systems rely primarily on an ability to represent surface knowledge about associations between observable evidence or data, on the one hand, and hypotheses or classifications of interest, on the other. Although the present generation of practical systems shows that this architectural style can be pushed quite far, the limitations of current systems motivate a search for representations that would allow expert systems to move beyond the prevalent "symptom-disease" style. One approach that appears promising is to couple a rule-based or associational system module with some other computational model of the phenomenon or domain of interest. According to this approach, the domain knowledge captured in the second model would be selected to complement the associational knowledge represented in the first module. Simulation models have been especially attractive choices for the complementary representation because of the causal relations embedded in them (Brown & Burton, 1975; Cuena, 1983).
In the past few years, we observed the emergence of wearable technologies which have mostly been facilitated via fitness and well-being applications, although their true future potential lays in the disrupting force they are placing on the healthcare sector. As a matter of fact, nowadays, thanks to FitBit, Apple smart-watches and Nike connected shoes, we have the ability to track lots of information from our sleeping patterns to extensive body vitals. Nevertheless, the big question is: how do we use this information? To better understand the trends in healthcare and their application, a good example to examine is the work accomplished by Dutch company, Sensara. Sensara is a spin-off European research project: they offer a subscription service for the monitoring of silver consumers in their homes.
Infectious diseases are studied to understand their spreading mechanisms, to evaluate control strategies and to predict the risk and course of future outbreaks. Because people only interact with a small number of individuals, and because the structure of these interactions matters for spreading processes, the pairwise relationships between individuals in a population can be usefully represented by a network. Although the underlying processes of transmission are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumors in an online social network. We study simulated simple and complex epidemics on synthetic networks and on two empirical networks, a social / contact network in an Indian village and an online social network in the U.S. Our goal is to learn simultaneously about the spreading process parameters and the source node (first infected node) of the epidemic, given a fixed and known network structure, and observations about state of nodes at several points in time. Our inference scheme is based on approximate Bayesian computation (ABC), an inference technique for complex models with likelihood functions that are either expensive to evaluate or analytically intractable. ABC enables us to adopt a Bayesian approach to the problem despite the posterior distribution being very complex. Our method is agnostic about the topology of the network and the nature of the spreading process. It generally performs well and, somewhat counter-intuitively, the inference problem appears to be easier on more heterogeneous network topologies, which enhances its future applicability to real-world settings where few networks have homogeneous topologies.
Most of us have heard of Google's well-publicized moonshots: Self-driving cars, smart contact lenses, internet-beaming balloons, and more. While those products and services sound amazing, most of us can't use them just yet. But the company actually has a bunch of other ones that are incredibly useful that you might not even know existed. For example, did you know Google has a massive free font library? Here are some of the under-the-radar services Google offers.