In a world of always-on ubiquitous connectivity, latency and reliability loom over everything, whether you’re talking about self-driving cars or Industry 4.0. These two challenges are driving much of the change that we’ll see in network design over the next few years. If the industry is to realize the promised benefits of IoT, we must increase the ability to support more machine-to-machine communications in near-real time. In applications like autonomous vehicles, latency requirements are on the order of a couple of milliseconds. GSMA, the international association for mobile technologuy, has specified that 5G's latency should be 1 millisecond, which is 50 times better than 4G's current 50 milliseconds. Satisfying these requirements involves a radical rethink about how and where we deploy assets throughout the network. For example, routing and backing up data using a traditional star-type network design will become increasingly unfeasible. The vast amount of traffic and the latency demands would easily overwhelm a north-south data flow.
The password is insecure: a hacker could log into an individual’s bank account and they wouldn’t even know. This is first issue; passwords are easily lost and even more easily stolen, via phishing or malware attacks. Once a cybercriminal has access to the password, they can replay it over and over gain. “Unfortunately, because passwords are free and easy, no one gave design much thinking,” said Mr Eisen. “But, now the cost of passwords is obvious” — they’re the great security vulnerability and largely responsible for the data breaches that pepper news headlines. Historically, security and user experience have been at odds with each other, because everyone believed that making systems less user friendly (longer, more complex passwords, for example) made them more secure — this is a fallacy and hinders adoption rates, making systems, ironically, less secure. “This is not a computer-to-computer interaction with longer keys. These are humans we’re talking about,” continued Mr Eisen.
The vulnerabilities allow attackers to sniff on keyboard traffic, but also inject keystrokes (even into dongles not connected to a wireless keyboard) and take over the computer to which a dongle has been connected. When encryption is used to protect the connection between the dongle and its paired device, the vulnerabilities also allow attackers to recover the encryption key. Furthermore, if the USB dongle uses a "key blacklist" to prevent the paired device from injecting keystrokes, the vulnerabilities allow the bypassing of this security protection system. Marcus Mengs, the researcher who discovered these vulnerabilities, said he notified Logitech about his findings, and the vendor plans to patch some of the reported issues, but not all. According to Mengs, the vulnerabilities impact all Logitech USB dongles that use the company's proprietary "Unifying" 2.4 GHz radio technology to communicate with wireless devices.
Instead of malware, criminals are using phishing attacks to gain access to financial services networks, but not just any attacks. "We're seeing more targeted attacks within financial services instead of kind of the scattershot approach where you send out a phishing attack to everybody in the organization," he explains. The success of phishing attacks on mobile devices in financial services may be part of a larger pattern of risky mobile behavior by those in the industry. According to the report, 42% of the organizations represented had devices with "side-loaded" apps — apps downloaded and installed from sites other than the app stores approved for the device. Covington says, "You start to see the implications of letting employees manage their own device." And those employees are managing their devices in tremendous numbers, he says. Employee-owned devices, used to conduct company business, are targets because of the sensitive data they contain. "There's no doubt in my mind that the criminal side of the equation is after rich data," he says.
The data from the Salesforce report highlights concerns of a potential shortage of tech skills post-Brexit, with over half of business leaders believing the UK is at risk of a tech brain drain. To address this, businesses are now recognising the pivotal role they must play in nurturing tech talent and digital skills in the country. One in four business leaders feel responsibility for doing so lies mainly with private enterprise; Over half (55%) plan to invest more in developing their own tech talent, with the same number pledging to address the skills gap by re-skilling older generations; and And, 51% intending to do more to re-skill people from disadvantaged backgrounds. There are issues that business needs to lead on regardless of what’s happening in the world of politics,” said Paul Smith, EVP and GM, Salesforce UK. “The economy is changing as new technologies emerge.
Big tech companies have already begun to embark on financial ventures, with payment platforms such as Google’s Google Wallet and Google payments, Amazon lending to SME marketplace sellers, Facebook’s partnership with Clear Bank on a product called Charged, a programme that allows financing for advertising, and Apple’s credit card, launched last year with Goldman Sachs and Marcus in the US. ... Big tech is in the position of having a significant “data advantage” over banks or fintechs, with the ability to glean more information about their users than others could hope to achieve. With the tech resources to offer an improved user experience and services that are integrated into their existing platforms, a grasp of artificial intelligence that traditional banks are only just beginning to deploy, sophisticated cloud computing, and an already loyal user base, up to 40% of the revenue currently generated by the US financial industry could move over to Big Tech, according to McKinsey.
"Brain-computer interfaces" can be used both for treating neurological and mental disorders as well as for understanding brain function, and now engineers have developed ways to manipulate these neural circuits with electrical currents, light, ultrasound, and magnetic fields. Remarkably, we can make a finger, arm, or even a leg move just by activating the right neurons in the motor cortex. Similarly, we can activate neurons in the visual cortex to make people see flashes of light. The former allows us to treat neurological conditions such as Parkinson's disease and epilepsy, whereas the latter should eventually allow us to restore vision to the blind. ... We have a real opportunity here to tap into the existing neural circuitry of the blind and augment their visual senses much like Google Glass or the Microsoft HoloLens. For example, make things appear brighter the closer they get, use computer vision to mark safe paths and combine it with GPS to give visual directions, warn users of impending dangers in their immediate surroundings, or even extend the range of "visible" light with the use of an infrared sensor.
One of the biggest confusion factors is all the different terms that are used as synonyms for AI such as machine learning, deep learning, cognitive computing, etc. The list grows daily. Keep in mind, these terms are not interchangeable, but they are often used that way. That doesn’t help anyone trying to figure out AI or how to use it. First of all, AI is a division of computer science using complex instruction sets to perform what appears to be human-like intelligence. These programs are powered by algorithms, and that is the ingredient causing the mystique. Without going into a lot of detail, an algorithm is a set of step-by-step computer instructions that can use data to build models that make predictions based on the data. Remember, we are a long way off from the thinking, talking robots seen in movies and on television. Algorithms are how AI demonstrates being smart, but be aware it’s not intelligent, which is the critical distinction. This type of AI is referred to as Narrow AI or Applied AI. It is said to simulate human thought, but each application can only carry out one specific task with a limited range of functions.
This attack introduces yet another method by Magecart that RiskIQ researchers call a “spray and pray” approach. Because skimmers work only when placed on payment or checkout pages, most Magecart attacks target specific e-commerce sites and attempt to drop a skimmer only on pages with payment forms. However, the ease of compromise that comes from finding S3 buckets misconfigured to allow public access means that even if only a fraction of their skimmer injections return payment data, it will yield a substantial return on investment, the researchers said. “This is a brand new twist on Magecart,” said Yonathan Klijnsma, head threat researcher at RiskIQ. “Although this group chose reach over targeting, they likely ended up getting their skimmer on enough payment pages to make their attack lucrative. They have done their cost-benefit analysis.” The scale of this latest attack illustrates how easy it is for threat actors of any kind to compromise a vast quantity of websites at once with scripts stored in misconfigured S3 buckets.
Working at PayPal on a next generation stream processing platform, we started to notice that many of our users wanted to use stream processing to apply anomaly detection models in real time. After we explored different architectures to create a flexible production grade framework that can scale to real world workloads, eventually we decided to go with a pipeline-based API, inspired by other open source projects like scikit-learn and Spark MLlib. This work has led to the development of Yurita - an open source anomaly detection framework for stream processing. Yurita is based on the new Spark structured streaming framework, and utilizes its processing engine capabilities to reach high scale and performant execution. The name Yurita comes from a traditional Japanese gold panning tool. ... Without knowing what the normal behavior of a metric is, we would be able to use only simple anomaly detection techniques, like rule-based decisions which also require a deep understanding of each specific dataset, and therefore are not scalable from a productivity point of view.
Quote for the day:
"All organizations are perfectly designed to get the results they are now getting. If we want different results, we must change the way we do things." -- Tom Northup