Processes such as chaos engineering, load testing and manual quality assurance can uncover situations where an API is failing to handle unexpected situations. Deploying your API to a cloud provider with a compelling SLA instead of your own hardware and network shifts the burden of infrastructure resiliency to a service, freeing your time to build features for your customers. A comprehensive suite of automated tests isn’t always sufficient to provide a robust API. Edge cases, unexpected code branches and other unplanned behavior may be triggered by requests that were not considered when writing the test suite. Traditional automated tests should be complimented by fuzz testing to help uncover hidden execution paths. ... It is expected that most APIs are built on layers of open source libraries and frameworks. Software composition analysis is a necessity to stay on top of zero-day vulnerabilities by identifying vulnerable dependencies as soon as they are discovered. OWASP guidance is a must-have—directing API developers to implement attack mitigation strategies such as CORS and CSRF protection. Application logic must be well tested for authorization and authentication.
Like many established ransomware operators, the gang behind Prometheus has adopted a very professional approach to dealing with its victims — including referring to them as "customers," PAN said. Members of the group communicate with victims via a customer service ticketing system that includes warnings on approaching payment deadlines and notifications of plans to sell stolen data via auction if the deadline is not met. "New ransomware gangs like Prometheus follow the same TTPs as big players [such as] Maze, Ryuk, and NetWalker because it is usually effective when applied the right way with the right victim," Santos says. "However, we do find it interesting that this group sells the data if no ransom is paid and are very vocal about it." From samples provided by the Prometheus ransomware gang on their leak site, the group appears to be selling stolen databases, emails, invoices, and documents that include personally identifiable information. "There are marketplaces where threat actors can sell leaked data for a profit, but we currently don't have any insight on how much this information could be sold in a marketplace," Santos says
Google’s engineers trained a reinforcement learning algorithm on a dataset of 10,000 chip floor plans of varying quality, some of which had been randomly generated. Each design was tagged with a specific “reward” function based on its success across different metrics like the length of wire required and power usage. The algorithm then used this data to distinguish between good and bad floor plans and generate its own designs in turn. As we’ve seen when AI systems take on humans at board games, machines don’t necessarily think like humans and often arrive at unexpected solutions to familiar problems. When DeepMind’s AlphaGo played human champion Lee Sedol at Go, this dynamic led to the infamous “move 37” — a seemingly illogical piece placement by the AI that nevertheless led to victory. Nothing quite so dramatic happened with Google’s chip-designing algorithm, but its floor plans nevertheless look quite different to those created by a human. Instead of neat rows of components laid out on the die, sub-systems look like they’ve almost been scattered across the silicon at random.
The first step is to immerse yourself within your training and specialty and have the confidence to be a key thought leader in the space. Do the extra research, spend the time to learn all of the new information and data in your field to truly understand the opportunity within. "I have been fortunate to be involved with several top academic institutions during my training. While the training was fantastic, there were areas that I felt could be improved for the ultimate outcome of increased access to high-quality healthcare," says Dr. Bajaj. "Thankfully, this vision has resulted in great outcomes and happy patients." ... "Ready. Fire. Aim!" as Dr. Bajaj puts it, "Time was not waiting for me to be fully prepared. Sometimes you have to take the leap." In entrepreneurship, there are no guarantees, which is quite different from some of the career paths that we have trained for our entire academic life. Guaranteed salary, retirement plans, and annual bonuses are far from promised in your own business, and it is important to adapt accordingly. Everything will not go according to plan, and it is important to find comfort with that. As long the launchpad for growth has been established - patience is the biggest challenge, not security.
The goal of cybersecurity used to be protecting data and people's privacy, Summit said. There has been a major shift in that thinking. "It's one thing to lose a patient's data, which is extremely important to protect, but when you start interrupting" people's ability to travel or the food supply chain, "you have a whole different level of problems … It's not just about protecting data but your operations. That's where major changes are starting to occur." Summit added that he has long said if companies were making cybersecurity a high priority long before now, "we wouldn't be in this position" and facing government scrutiny. The cybersecurity field is "incredibly dynamic," Hatter said, and CISOs don't have the luxury of planning out three to five years. "We want to create and deploy a strategy that's sound and solid. But market forces demand; we recalibrate what we do and COVID-19 was a great example of that." CISOs now have to have as resilient a strategy as possible but be prepared to make changes. Managed security service providers can help, Summit said, but CISOs are still feeling overwhelmed.
Virtually any interaction with their environment can cause them to collapse like a house of cards and lose their quantum correlations – a process called decoherence. If this happens before an algorithm finishes running, the result is a mess, not an answer. (You would not get much work done on a laptop that had to restart every second.) In general, the more qubits a quantum computer has, the harder they are to keep quantum; even today’s most advanced quantum processors still have fewer than 100 physical qubits. The solution to imperfect physical qubits is quantum error correction (QEC). By entangling many qubits together in a so-called “genuine multipartite entangled” (GME) state, where every qubit is entangled with every other qubit in that bunch, it is possible to create a composite “logical” qubit. This logical qubit acts as an ideal qubit: the redundancy of the shared information means if one of the physical qubits decoheres, the information can be recovered from the rest of the logical qubit. Developing quantum error-correcting systems requires verifying that the GME states used in logical qubits are present and working as intended, ideally as quickly and efficiently as possible.
Some scientists believe that assembling multiple narrow AI modules will produce higher intelligent systems. For example, you can have a software system that coordinates between separate computer vision, voice processing, NLP, and motor control modules to solve complicated problems that require a multitude of skills. A different approach to creating AI, proposed by the DeepMind researchers, is to recreate the simple yet effective rule that has given rise to natural intelligence. “[We] consider an alternative hypothesis: that the generic objective of maximising reward is enough to drive behaviour that exhibits most if not all abilities that are studied in natural and artificial intelligence,” the researchers write. This is basically how nature works. As far as science is concerned, there has been no top-down intelligent design in the complex organisms that we see around us. Billions of years of natural selection and random variation have filtered lifeforms for their fitness to survive and reproduce. Living beings that were better equipped to handle the challenges and situations in their environments managed to survive and reproduce. The rest were eliminated.
The significant rise in internet-connected devices will consequently have a substantial influence on systems’ network traffic, and current point-to-point technologies using synchronous communication between end-points in IoT-systems are not any longer a sustainable solution. Message queue architectures using the publish-subscribe paradigm are widely implemented in event-based systems. This paradigm uses asynchronous communication between entities and conforms to scalable, high throughput, and low latency systems that are well adapted within the IoT-domain. This thesis evaluates the adaptability of three popular message queue systems in Kubernetes. The systems are designed differently, where e.g. the Kafka system is using a peer-to-peer architecture while STAN and RabbitMQ use a master-slave architecture by applying the Raft consensus algorithm. A thorough analysis of the systems’ capabilities in terms of scalability, performance, and overhead are presented. The conducted tests give further knowledge on how the performance of the Kafka system is affected in multi-broker clusters using multiple number of partitions, enabling higher levels of parallelism for the system.
Researchers have uncovered a 1.2-terabyte database of stolen data, lifted from 3.2 million Windows-based computers over the course of two years by an unknown, custom malware. The heisted info includes 6.6 million files and 26 million credentials, and 2 billion web login cookies – with 400 million of the latter still valid at the time of the database’s discovery. According to researchers at NordLocker, the culprit is a stealthy, unnamed malware that spread via trojanized Adobe Photoshop versions, pirated games and Windows cracking tools, between 2018 and 2020. It’s unlikely that the operators had any depth of skill to pull off their data-harvesting campaign, they added. “The truth is, anyone can get their hands on custom malware. It’s cheap, customizable, and can be found all over the web,” the firm said in a Wednesday posting. “Dark Web ads for these viruses uncover even more truth about this market. For instance, anyone can get their own custom malware and even lessons on how to use the stolen data for as little as $100. And custom does mean custom – advertisers promise that they can build a virus to attack virtually any app the buyer needs.”
As I say, it’s by no means clear what happens next and how ingrained changes will be. It’s plausible, of course, that we largely go back to old habits although that seems unlikely with a groundswell of employees having become accustomed to a different way of life and a different way of working. And it’s worth noting that even evidence of a return to ancient ways of living in the form of crafts, baking and so on are now very much digitally imbued activities. We download apps, consult websites and share ideas on forums when we try out a new recipe, and this sort of binary activity is part of the fabric of life because it is faster, more convenient and more scalable than the older alternatives. But what we need to do is strike the perfect balance between technology-enabled agility and what we want to do with our time. What we will need to manage through change is clear though. Adaptivity, enabled by robust, data-centric digital business designs, will become the watchword of operations. In other words, companies will need to be able to move fast, whatever happens, changing operating models, moving into adjacent markets and generally taking nothing for granted. In the new Age of Uncertainty, legacy systems have to be reassessed in the context of how best to build for agility.
Quote for the day:
"To have long term success as a coach or in any position of leadership, you have to be obsessed in some way." -- Pat Riley