“Machine learning is a critical component to developing Artificial Intelligence for IoT security,” says Uday Veeramachaneni, co-founder and CEO at PatternEx. “The problem is that the IoT’s will be distributed massively and if there is an attack you have to react in real-time.” Most systems relying on machine learning and behavior analysis will gather information about the network and connected devices and subsequently seek everything that is out of normal. The problem with this primitive method is that it produces too many false alarms and false positives. The approach suggested by PatternEx is to develop a solution that incorporates machine learning and augments it with human analyst insight for greater attack detection.
As with any potentially transformative new technology, distributed ledgers raise a number of questions for policy makers and regulators at both national and international levels. Regulators are certainly closely analysing and monitoring distributed ledger developments and, for now, appear cautiously optimistic about its potential, especially because of the potential that distributed ledgers could actually help to improve regulatory compliance tracking and reporting. But, guess what?: most authorities are taking a "wait and see" approach. Blockchain and distributed ledger technology is not without its challenges, including scalability and latency, lack of mainstream understanding, lack of readiness in some sectors to rely exclusively on data in digital form, over-reliance on out-dated legacy systems which would need to be overhauled before distributed ledger technology could be implemented.
Let’s make a Bluetooth-related toothbrush that comes with a smartphone app. Now the “smart” toothbrush helps Oral-B do a improved task in protecting dental well being by “focusing, tracking, motivating and sensing”. The toothbrush is smarter, but the business product is not. The related solution supposedly generates extra worth for buyers, but all the other things of the business product continue being the same. The worth is nevertheless shipped by way of a toothbrush unit, captured by sales by way of retail channels access to the retail shelf-room is nevertheless the essential competitive edge. Not a great deal business product innovation here. ... Sceptics, of course, will ask, “Who wants builders to extend the toothbrush?” But moms of youthful kids will see a sea of opportunity here
This is the second set of charges against Google by the commission. On April 15 last year, it announced a “statement of objections” against the search giant in an investigation into charges that its Internet search in Europe favored its own comparison shopping product. The commission announced on the same day an investigation into Google’s conduct with regard to the Android operating system that would look, among other things, into whether Google had illegally hindered the development and market access of rival mobile applications or services by requiring or providing incentives to smartphone and tablet manufacturers to exclusively pre-install Google’s own applications or services.
"A recurring theme in the IoT space is the immaturity of technologies and services and of the vendors providing them. Architecting for this immaturity and managing the risk it creates will be a key challenge for organizations exploiting the IoT. In many technology areas, lack of skills will also pose significant challenges." In the coming years, IoT will look completely different than it does today. IoT is a greenfield market. New players, with new business models, approaches, and solutions, can appear out of nowhere and overtake incumbents. But business is the key market. While there is talk about wearable devices and connected homes, the real value and immediate market for IoT is with businesses and enterprises.
Across the emerging fintech landscape, the customers most susceptible to cherry-picking are millennials, small businesses, and the underbanked—three segments particularly sensitive to costs and to the enhanced consumer experience that digital delivery and distribution afford. For instance, Alipay, the Chinese payments service (a unit of e-commerce giant Alibaba), makes online finance simpler and more intuitive by turning savings strategies into a game and comparing users’ returns with those of others. It also makes peer-to-peer transfers fun by adding voice messages and emoticons. From an incumbent’s perspective, emerging fintechs in corporate and investment banking (including asset and cash management) appear to be less disruptive than retail innovators are.
Random forests may require more data but they almost always come up with a pretty robust model. And deep learning algorithms... well, they require "relatively" large datasets to work well, and you also need the infrastructure to train them in reasonable time. Also, deep learning algorithms require much more experience: Setting up a neural network using deep learning algorithms is much more tedious than using an off-the-shelf classifiers such as random forests and SVMs. On the other hand, deep learning really shines when it comes to complex problems such as image classification, natural language processing, and speech recognition. Another advantage is that you have to worry less about the feature engineering part.
The question before consumers and the courts today is three-fold: What kinds of valuabledata is the IoT generating; who should have access to and control over that data; and who can be legally compelled to share that information with law enforcement. In the recent Apple encryption case, the FBI went directly to the manufacturer of a product to gain access to digitized information residing on that device. In our digitally connected future before us, will law enforcement simply bypass end users like you and me and compel companies to turn on our Nest cameras, unlock our August Smart Locks or tune in to our Echos? The Apple encryption case and its predecessors have broad implications for the entire tech community — not just those building smartphones and running data centers. The way in which we’ll interact with technology in the future has been turned on its head.
To really understand what a container is in the world of software, we need to understand what goes into making one. And that's what this article is explains. In the process we’ll talk about containers vs containerisation, linux containers (including namespaces, cgroups and layered filesystems), then we’ll walk through some code to build a simple container from scratch, and finally talk about what this all really means. ... Caching is what makes Docker images so much more effective than vmdks or vagrantfiles. It lets us ship the deltas over some common base images rather than moving whole images around. It means we can afford to ship the entire environment from one place to another. It’s why when you `docker run whatever` it starts close to immediately even though whatever described the entirety of an operating system image.
Money isn’t just a motive; money is the enabler. Cybercriminals whose crimes make money can invest in new attacks, invest in defeating countermeasures, and invest in developing new targets. Until recently, attacks on critical infrastructure and the Internet of Things have also been rarely-realized theoretical concerns. There are many hackers who would think that bringing down a power station with a cyberattack is cool, but making that happen would require a group effort to build the necessary hacker tool chain. Ransomware delivers both the motive and the resources to make that happen. And once that ransomware-funded tool chain exists, it will be launched for many other purposes, ranging from idle curiosity to political vengeance.
Quote for the day:
"If a cluttered desk is a sign of a cluttered mind, of what, then, is an empty desk a sign of?" -- Albert Einstein