Synthetic data can be defined as data not collected from real-world events. Today, specific algorithms are available to generate realistic synthetic data used as a training dataset. Deep Generative Networks/Models can learn the distribution of training data to generate new data points with some variations. While it is not always possible to learn the models’ exact distribution, algorithms can come close. ... The big players already have a stronghold on data and have created monopolies or ‘data-opolies’. Synthetic data generation models can address this power imbalance. Secondly, the rising number of cyberattacks, especially after the pandemic, has raised privacy and security concerns. The situation is especially worrying when huge amounts of data are stored in one place. By creating synthetic data, organisations can mitigate this risk. Thirdly, whenever datasets are created, they reflect real-world biases, resulting in the over-representation or under-representation of certain sections of society. The machine learning algorithms based on such datasets amplify such biases resulting in further discrimination. Synthetic data generation can fill in the holes and help in creating unbiased datasets.
While malicious extensions are an issue with all browsers, it's especially significant with Chrome because of how widely used the browser is, Maor says. It's hard to say what proportion of the overall Chrome extensions currently available are malicious. It's important to note that just a relatively small number of malicious extensions are needed to infect millions of Internet users, he says. One case in point was Awake Security's discovery last June of over 100 malicious Google Chrome extensions that were being used as part of a massive global campaign to steal credentials, take screenshots, and carry out other malicious activity. Awake Security estimated that there were at least 32 million downloads of the malicious extensions. In February 2020, Google removed some 500 problematic Chrome extensions from its official Chrome Web Store after being tipped off to the problem by security researchers. Some 1.7 million users were believed affected in that incident. In a soon-to-be-released report, Cato says it analyzed five days of network data collected from customer networks to see if it could identify evidence of extensions communicating with command-and-control servers.
Despite conventional wisdom, open-source solutions are, by their nature, neither more nor less secure than proprietary third-party solutions. Instead, a combination of factors, such as license selection, developer best practices and project management rigor, establish a unique risk profile for each OSS solution.The core risks related to open source include: Technical risks, including general quality of service defects and security vulnerabilities; Legal risks, including factors related to OSS license compliance as well as potential intellectual property infringements; Security risks, which begin with the nature of OSS acquisition costs. The total cost of acquisition for open source is virtually zero, as open-source adopters are never compelled to pay for the privilege of using it. Unfortunately, one critical side effect of this low burden of acquisition is that many open-source assets are either undermanaged or altogether unmanaged once established in an IT portfolio. This undermanagement can easily expose both quality and security risks because these assets are not patched and updated as frequently as they should be. Finally, vendor lock-in can still be a risk factor, given the trend among vendors to add proprietary extensions on top of an open-source foundation (open core).
To consider and look for the unknown information about a system, you need to have justice. In Stoicism, this stands for “showing kindness and generosity in our relationships with others”. And because you don’t know everything, you need other people to help you out. Gathering information is also about creativity, so you have to gather inspiration from past experience, and with your colleagues must be able to connect dots that weren’t connected before. Once I even stated, “The knowledge (and information) you gather as a tester about the software can be an interesting input for new software products and innovations”. But as a Stoic, stay humble ;-). After gathering all the information you need, you should use your wisdom (“based on reasoning and judgment”) to come to conclusions so that you can answer the question “Is this software ready to be used?” Although our customers are the best testers, we as testers are in the position that we are (or at least should be) able to answer the question at every step in the software development: if the software is going to production, what can happen? The information you put on the table for your stakeholder should be based on facts.
The typical data analyst role is consulting-centric, as can be seen from the Indeed job spec example. What they are preoccupied with for the most part is wrangling data from Excel spreadsheets and SQL databases, extracting insightful conclusions via retrospective analyses, A/B tests, and generally providing evidence-based business advice. The last point illustrates why reporting routines with visualisation tools such as Tableau are as pivotal as pivoted tables. Data modelling on the other hand is often limited to basic supervised learning or its stats equivalent: regression analysis. ... To be fair, data scientists are for that reason expected to be more than analytical wizards. They are supposed to be builders who employ advanced programming to create pipelines that predict and recommend in production environments with near perfect accuracy. Compared with analysts, who’re like investigative reporters, they are a lot more product development than consulting oriented. Although it’s also required of a data scientist to provide data-led commercial advice. Some say the title was coined to manifest that the role was a confluence of three fields: maths/statistics, computer science and domain expertise.
If you're like most technology leaders, the closest you get to the actual technology you select and manage is creating PowerPoint decks that tell others about the performance, maintenance and updating of that technology. There's nothing fundamentally wrong with this of course; you can be a fantastic leader of a construction firm without having swung a hammer, or a cunning military strategist who has never rucked over a hill or fired a weapon. However, hands-on time with the fundamental building blocks of your domain can make you a better leader, just as the architect who spends time in the field and understands the materials and building process makes him or her more effective at creating better structures. ... Think of a home lab as the technology equivalent of the scientist's laboratory. It's a place where you can experiment with new technologies, attempt to interconnect various services in novel ways and quickly clean things up when you're done. While you might be picturing a huge rack of howling servers, fortunately for us you can now create the equivalent of a small data center on a single piece of physical equipment.
What went wrong then, and how is IOTA going to fix it -- besides introducing a new wallet? Schiener focused on some key technical decisions that proved wrong, and are being retracted. IOTA wanted to be quantum-proof, and that's why it used a "special cryptography," as Schiener put it. IOTA's cryptography only allowed, for example, to utilize an address once. Reusing an address could lead to a loss of funds. Another questionable decision was choosing to use ternary, rather than binary encoding for data. That was because, according to Schiener, the hypothesis of the future was that ternary is a much better and more efficient way to encode data. The problem is, as he went on to add, that this also needs ternary hardware to work. There are more, having to do with the way the ledger is created. It's still a DAG, but it has different algorithms. Schiener said that over the last one and a half years, IOTA has been reinvented and rewritten from the ground up. This new phase of the project is Chrysalis, which is this new network upgrade. With Chrysalis, IOTA is also moving toward what it calls Coordicide.
One of the main guiding principles behind Burp’s new crawler is that we should always attempt to navigate around the web applications behaving as much as possible like a human user.. This means only clicking on links that can actually be seen in the DOM and strictly following the navigational paths around the application (not randomly jumping from page to page).Before we had browser-powered scanning, the crawler (and the old spider) essentially pretended to be a web browser. It was responsible for constructing the requests that are sent to the server, parsing the HTML in the responses, and looking for new links that could be followed in the raw response we observed being sent to the client. This model worked well in the Web 1.0 world, where generally the DOM would be fully constructed on the server before being sent over the wire to the browser. You could be fairly certain that the DOM observed in an HTTP response was almost exactly as it would be displayed in the browser. As such, it was relatively easy to observe all the possible new links that could be followed from there.Things start to break down with this approach in the modern world.
The first was the rise of the internet. Constantly improving speeds and widespread access meant hundreds of millions of consumers were suddenly able to access digital services. The second was the rise of the smartphone. This hardware transformed consumer behaviour beyond recognition. Apps and other software products providing significant upfront value made smartphones indispensable — just think of Shopify, Google Maps and Uber. The third driver which paved the way for fintech providers’ success was the financial crisis in 2008. Not only did this bring the traditional banking system to the brink of collapse, but consumers were far less trusting of the big banks thereafter. The new breed of financial services providers was not tied down by legacy infrastructures and, with smaller teams and flexible IT infrastructures, they were more agile. And this allowed them to easily circumnavigate the new regulatory and compliance requirements that were introduced in the wake of the financial downturn. Fintech providers sought to solve problems the banks could not. Or at least to do what the banks do, but better.
As the world began relying on these new digital capabilities, new risks and challenges were introduced. Organizations that were well-equipped to extend visibility and control to this new way of working found themselves in a far better situation than those that were scrambling to completely reengineer their security capabilities. The ones that had built an empowered and proactive security team, backed by robust processes and supported by effective technology, were able to adapt and overcome. Organizations that were locked into a rigid operational model, overly reliant on vendor platforms or lacking a defined set of processes to support their new reality, struggled to keep pace. ... Since the pandemic began, we have seen an increased emphasis and shift toward zero trust and security access service edge (SASE) principles. With strong identity and access management capabilities, insights into services and APIs, and visibility into remote endpoint devices, security teams can put themselves in position for rapid and effective responses — even within this unique virtual setting. Access to sensitive and confidential data is the new perimeter for an organization's cybersecurity posture.
Quote for the day:
"A tough hide with a tender heart is a goal that all leaders must have." -- Wayde Goodall