The incident follows a number of alleged cyber attacks by foreign actors, such as the targeting of a range of government and private-sector organisations in Australia. In a statement earlier on Wednesday, the NZX blamed Tuesday’s attack on overseas hackers, saying that it had “experienced a volumetric DDoS attack from offshore via its network service provider, which impacted NZX network connectivity”. It said the attack had affected NZX websites and the markets announcement platform, causing it to call a trading halt at 3.57pm. It said the attack had been “mitigated” and that normal market operations would resume on Wednesday, but this subsequent attack has raised questions about security. A DDoS attack aims to overload traffic to internet sites by infecting large numbers of computers with malware that bombards the targeted site with requests for access. Prof Dave Parry, of the computer science department at Auckland University of Technology, said it was a “very serious attack” on New Zealand’s critical infrastructure. He warned that it showed a “rare” level of sophistication and determination, and also flagged security issues possibly caused by so many people working from home.
"There are things we know we don't know, and there are things we don't know we don't know." And what I'm trying to explain is the practitioners point of view. Now, when I come to you and I tell you, you know what, "You don't know this, Peter. You don't know this. And if you do this, you would be making a lot of money." You will say, "Who are you to tell me that?" So I need to build confidence first. So the first part of the discussion starts from telling you what you already know. So when you do use the data, the idea is — and to create a report, and that's what reports are for. Look at how organizations make decisions — what they do is they get a report and they take a decision on that report. But 95% of the time, I know that people who are making that decision or are reading that report know the answer in the report. That's why they're comfortable with the report, right? So let's look at a board meeting where the board has a hunch that this quarter they're going to make 25% increase in their sales. They have the hunch. Now, that is where they're going to get a report which will save 24% or 29%, it will be in the ballpark range. So there's no unknown. But if I'm only telling you what you already know,
When trained on huge data sets, machine learning algorithms often ferret out subtle correlations between data points that would have gone unnoticed to human analysts. These patterns enable them to make forecasts and predictions that are useful most of the time for their designated purpose, even if they’re not always logical. For instance, a machine-learning algorithm that predicts customer behavior might discover that people who eat out at restaurants more often are more likely to shop at a particular kind of grocery store, or maybe customers who shop online a lot are more likely to buy certain brands. “All of those correlations between different variables of the economy are ripe for use by machine learning models, which can leverage them to make better predictions. But those correlations can be ephemeral, and highly context-dependent,” David Cox, IBM director at the MIT-IBM Watson AI Lab, told Gizmodo. “What happens when the ground conditions change, as they just did globally when covid-19 hit? Customer behavior has radically changed, and many of those old correlations no longer hold. How often you eat out no longer predicts where you’ll buy groceries, because dramatically fewer people eat out.”
With the rise of IoT, edge computing is rapidly gaining popularity as it solves the issues the IoT has when interacting with the cloud. If you picture all your smart devices in a circle, the cloud is centralised in the middle of them; edge computing happens on the edge of that cloud. Literally referring to geographic location, edge computing happens much nearer a device or business, whatever ‘thing’ is transmitting the data. These computing resources are decentralised from data centres; they are on the ‘edge’, and it is here that the data gets processed. With edge computing, data is scrutinised and analysed at the site of production, with only relevant data being sent to the cloud for storage. This means much less data is being sent to the cloud, reducing bandwidth use, privacy and security breaches are more likely at the site of the device making ‘hacking’ a device much harder, and the speed of interaction with data increases dramatically. While edge and cloud computing are often seen as mutually exclusive approaches, larger IoT projects frequently require a combination of both. Take driverless cars as an example.
Analogous ideas regarding the primacy of the business strategy are also expressed by other authors, who argue that EA and IT planning efforts in organizations should stem directly from the business strategy. Bernard states that “the idea of Enterprise Architecture is that of integrating strategy, business, and technology”. Parker and Brooks argue that the business strategy and EA are interrelated so closely that they represent “the chicken or the egg” dilemma. These views are supported by Gartner whose analysts explicitly define EA as “the process of translating business vision and strategy into effective enterprise change”. Moreover, Gartner analysts argue that “the strategy analysis is the foundation of the EA effort” and propose six best practices to align EA with the business strategy. Unsurprisingly, similar views are also shared by academic researchers, who analyze the integration between the business strategy and EA modeling of the business strategy in the EA context. To summarize, in the existing EA literature the business strategy is widely considered as the necessary basis for EA and for many authors the very concepts of business strategy and EA are inextricably coupled
In modern microservice architecture, we can categorize microservices into two main groups based on their interaction and communication. The first group of microservices acts as external-facing microservices, which are directly exposed to consumers. They are mainly HTTP-based APIs that use conventional text-based messaging payloads (JSON, XML, etc.) that are optimized for external developers, and use Representational State Transfer (REST) as the de facto communication technology. REST’s ubiquity and rich ecosystem play a vital role in the success of these external-facing microservices. OpenAPI provides well-defined specifications for describing, producing, consuming, and visualizing these REST APIs. API management systems work well with these APIs and provide security, rate limiting, caching, and monetizing along with business requirements. GraphQL can be an alternative for the HTTP-based REST APIs but it is out of scope for this article. The other group of microservices are internal and don’t communicate with external systems or external developers. These microservices interact with each other to complete a given set of tasks. Internal microservices use either synchronous or asynchronous communication.
One thing that you need to be aware of when jumping from .NET Framework to .NET Core, is a faster roll-out of new versions. That includes shorter support intervals too. With .NET Framework, 10 years of support wasn't unseen, where .NET Core 3 years seem like the normal interval. Also, when picking which version of .NET Core you want to target, you need to look into the support level of each version. Microsoft marks certain versions with long time support (LTS) which is around 3 years, while others are versions in between. Stable, but still versions with a shorter support period. Overall, these changes require you to update the .NET Core version more often than you have been used to or accept to run on an unsupported framework version. ... The upgrade path isn't exactly straight-forward. There might be some tools to help with this, but I ended up migrating everything by hand. For each website, I took a copy of the entire repo. Then deleted all files in the working folder and created a new ASP.NET Core MVC project. I then ported each thing one by one. Starting with copying in controllers, views, and models and making some global search-replace patterns to make it compile.
Many of the most impressive and successful corporate pivots of the past decade have taken the form of changes of activity — continuing with the same strategic path but fundamentally changing the activities used to pursue it. Think Netflix transitioning from a DVD-by-mail business to a streaming service; Adobe and Microsoft moving from software sales models to monthly subscription businesses; Walmart evolving from physical retail to omnichannel retail; and Amazon expanding into physical retailing with its Whole Foods acquisition and launch of Amazon Go. Further confusing the situation for decision makers is the ill-defined relationship between innovation and change. Most media commentary focuses on one specific form of innovation: disruptive innovation, in which the functioning of an entire industry is changed through the use of next-generation technologies or a new combination of existing technologies. (For example, the integration of GPS, smartphones, and electronic payment systems — all established technologies — made the sharing economy possible.) In reality, the most common form of innovation announced by public companies is digital transformation initiatives designed to enhance execution of the existing strategy by replacing manual and analog processes with digital ones.
A crucial factor standing in the way of the acceleration towards Open Banking has been the delay to API development. These APIs are the technology that TPPs rely on to migrate their services and customer base to remain PSD2 compliant. One of the contributing factors was that the RTS, which apply to PSD2, left room for too many different interpretations. This ambiguity caused banks to slip behind and delay the creation of their APIs. This delay hindered European TPPs in migrating their services without losing their customer base, particularly outside the UK, where there has been no regulatory extension and where the API framework is the least advanced. Levels of awareness of the new regulations and changes to how customers access bank accounts and make online payments are very low among consumers and merchants. This leads to confusion and distrust of the authentication process in advance of the SCA roll-out. Moreover, because the majority of customers don’t know about Open Banking yet, they aren’t aware of the benefits. Without customer awareness and demand it may be very hard for TPPs to generate interest and uptake for their products.
One of the lessons drawn by both sides was how inexpensive it was for the red team to have an impact on the election process. There was no need to "spend" a zero-day or invest in novel exploits. Manipulating social media is a known tactic today, while robocalls are cheap-to-free. Countering the red team's tactics relied on coordination between the various government authorities and ensuring communication redundancy between agencies. Anticipating disinformation plans that might lead to unrest also worked well for the blue team, as red team efforts to bring violence to polling places were put down before they bore fruit. The red team also tried to interfere with voting by mail; they hacked a major online retailer to send more packages through the USPS than normal, and used label printers to put bar codes with instructions for resetting sorting machines on a small percentage of those packages. While there was some slowdown, there was no significant disruption of the mail around the election.
Quote for the day:
"It's hard to get the big picture when you have a small frame of reference." -- Joshing Stern