HTTPS: The Myth of Secure Encrypted Traffic Exposed

February 5, 2019 | 56 views

The S in HTTPS is supposed to mean that encrypted traffic is secure. For attackers, it just means that they have a larger attack surface from which to launch assaults on the applications to exploit the security vulnerabilities. How should organizations respond? Most web traffic is encrypted to provide better privacy and security. By 2018, over 70% of webpages are loaded over HTTPS. Radware expects this trend to continue until nearly all web traffic is encrypted. The major drivers pushing adoption rates are the availability of free SSL certificates and the perception that clear traffic is insecure.

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Instantor helps organisations understand their customers’ true financial capacity. Instantor’s solutions process transactional data from bank accounts, support financial data aggregation and improve credit risk management. Instantor empowers customers to accurately predict the possibilities of defaulting – making credit risk decisions faster, easier, and fairer for all.

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AI TECH

AI's Impact on Improving Customer Experience

Article | July 11, 2022

To enhance the consumer experience, businesses all over the world are experimenting with artificial intelligenace (AI), machine learning, and advanced analytics. Artificial intelligence (AI) is becoming increasingly popular among marketers and salespeople, and it has become a vital tool for businesses that want to offer their customers a hyper-personalized, outstanding experience. Customer relationship management (CRM) and customer data platform (CDP) software that has been upgraded with AI has made AI accessible to businesses without the exorbitant expenses previously associated with the technology. When AI and machine learning are used in conjunction for collecting and analyzing social, historical, and behavioral data, brands may develop a much more thorough understanding of their customers. In addition, AI can predict client behavior because it continuously learns from the data it analyzes, in contrast to traditional data analytics tools. As a result, businesses may deliver highly pertinent content, boost sales, and enhance the customer experience. Predictive Behavior Analysis and Real-time Decision Making Real-time decisioning is the capacity to act quickly and based on the most up-to-date information available, such as information from a customer's most recent encounter with a company. For instance, Precognitive's Decision-AI uses a combination of AI and machine learning to assess any event in real-time with a response time of less than 200 milliseconds. Precognitive's fraud prevention product includes Decision-AI, which can be implemented using an API on a website. Marketing to customers can be done more successfully by using real-time decisioning. For example, brands may display highly tailored, pertinent content and offer to clients by utilizing AI and real-time decisioning to discover and comprehend a customer's purpose from the data they produce in real-time. By providing deeper insights into what has already happened and what can be done to facilitate a sale through suggestions for related products and accessories, AI and predictive analytics are able to go further than historical data alone. This increases the relevance of the customer experience, increases the likelihood that a sale will be made, and increases the emotional connection that the customer has with a brand.

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SOFTWARE

The Evolution of Quantum Computing and What its Future Beholds

Article | July 14, 2022

The mechanism of quantum computers will be entirely different from anything we humans have ever created or constructed in the past. Quantum computers, like classical computers, are designed to address problems in the real world. They process data in a unique way, though, which makes them a much more effective machine than any computer in use today. Superposition and entanglement, two fundamental ideas in quantum mechanics, could be used to explain what makes quantum computers unique. The goal of quantum computing research is to find a technique to accelerate the execution of lengthy chains of computer instructions. This method of execution would take advantage of a quantum physics event that is frequently observed but does not appear to make much sense when written out. When this fundamental objective of quantum computing is accomplished, and all theorists are confident works in practice, computing will undoubtedly undergo a revolution. Quantum computing promises that it will enable us to address specific issues that current classical computers cannot resolve in a timely manner. While not a cure-all for all computer issues, quantum computing is adequate for most "needle in a haystack" search and optimization issues. Quantum Computing and Its Deployment Only the big hyperscalers and a few hardware vendors offer quantum computer emulators and limited-sized quantum computers as a cloud service. Quantum computers are used for compute-intensive, non-latency-sensitive issues. Quantum computer architectures can't handle massive data sizes yet. In many circumstances, a hybrid quantum-classical computer is used. Quantum computers don't use much electricity to compute but need cryogenic refrigerators to sustain superconducting temperatures. Networking and Quantum Software Stacks Many quantum computing software stacks virtualize the hardware and build a virtual layer of logical qubits. Software stacks provide compilers that transform high-level programming structures into low-level assembly commands that operate on logical qubits. In addition, software stack suppliers are designing domain-specific application-level templates for quantum computing. The software layer hides complexity without affecting quantum computing hardware performance or mobility.

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AI TECH

Language Models: Emerging Types and Why They Matter

Article | July 20, 2022

Language model systems, often known as text understanding and generation systems, are the newest trend in business. However, not every language model is made equal. A few are starting to take center stage, including massive general-purpose models like OpenAI's GPT-3 and models tailored for specific jobs. There is a third type of model at the edge that is intended to run on Internet of Things devices and workstations but is typically very compressed in size and has few functionalities. Large Language Models Large language models, which can reach tens of petabytes in size, are trained on vast volumes of text data. As a result, they rank among the models with the highest number of parameters, where a "parameter" is a value the model can alter on its own as it gains knowledge. The model's parameters, which are made of components learned from prior training data, fundamentally describe the model's aptitude for solving a particular task, like producing text. Fine-tuned Language Models Compared to their massive language model siblings, fine-tuned models are typically smaller. Examples include OpenAI's Codex, a version of GPT-3 that is specifically tailored for programming jobs. Codex is both smaller than OpenAI and more effective at creating and completing strings of computer code, although it still has billions of parameters. The performance of a model, like its capacity to generate protein sequences or respond to queries, can be improved through fine-tuning. Edge Language Models Edge models, which are intentionally small in size, occasionally take the shape of finely tuned models. To work within certain hardware limits, they are occasionally trained from scratch on modest data sets. In any event, edge models provide several advantages that massive language models simply cannot match, notwithstanding their limitations in some areas. The main factor is cost. There are no cloud usage fees with an edge approach that operates locally and offline. As significant, fine-tuned, and edge language models grow in response to new research, they are likely to encounter hurdles on their way to wider use. For example, compared to training a model from the start, fine-tuning requires less data, but fine-tuning still requires a dataset.

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SOFTWARE

Low-code and No-code: A Business' New Best Friend

Article | July 5, 2022

Businesses are starting to integrate artificial intelligence (AI) into their workflow in greater numbers as a result of the growth of digital transformation and developments in machine learning (ML). As a result, platforms that need no coding, as well as their low-code counterparts, are becoming more popular. This development is a step toward computer science's long-term objective of automating manual coding. Low-code/no-code AI platforms will be beneficial to businesses in more data-driven industries like marketing, sales, and finance. AI can assist in a variety of ways, including automating invoicing, evaluating reports, making intelligent suggestions, and anticipating churn rates. How Does an Organization Look at Low-code/No-code as the Future? Developers and other tech-related positions are in high demand, particularly in the fields of AI and data science. Organizations have the chance to close the gap with the aid of citizen data scientists who don't require an AI professional to design unique AI solutions for many scenarios, thanks to low-code and no-code AI technologies. The demand for technological solutions and AI technologies is rising significantly as the technological landscape rapidly changes. AI systems, for example, require complex software that uses a lot of code, a variety of frameworks, and the Internet of Things (IoT). One person's capacity to comprehend every technical detail is strained by the array of complicated technology. Software delivery must be timely, effective, and secure while maintaining high standards. Conclusion Low-code AI solutions offer the speed, ease of use, and adaptability of ready-made software solutions while also drastically reducing the time to market for AI solutions and the cost of recruiting software and computer vision engineers. Organizations are free to construct the architecture, functionality, or pipeline that best suits their project, the sky being the limit. However, creating such unique models may be both costly and time-consuming. Therefore, employing low-code/no-code platforms would apply to particular pipeline actions that would streamline and accelerate the processes.

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Spotlight

Instantor AB

Instantor helps organisations understand their customers’ true financial capacity. Instantor’s solutions process transactional data from bank accounts, support financial data aggregation and improve credit risk management. Instantor empowers customers to accurately predict the possibilities of defaulting – making credit risk decisions faster, easier, and fairer for all.

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Radware Snaps Up ShieldSquare for New Bot Management Product Line

SDxCentral | January 07, 2019

ShieldSquare, founded in 2014, is a security firm that specializes in bot management. It delivers an API-based service that can detect and eliminate “bad bots” from websites, mobile applications, and APIs. The technology detects bots and relies on a cloud engine to classify visitor activity as either human, search engine crawler, or bad bots to secure the application. Bot management — according to Michael Groskop, Radware’s vice president of product management portfolio — refers to the detection of malicious bot activity and the practice of mitigating bot attacks by differentiating legitimate flows from bots. It also must allow differentiation between good and bad bots. He noted that as bots grow more sophisticated, bot management must include “advanced behavioral detection and intent analysis,” delivered with machine learning, which ShieldSquare provides. ShieldSquare technology will be offered as a new product line called Radware Bot Manager. The product line will integrate with Radware’s additional attack migration products, particularly its web application firewall (WAF) cloud services. Groskop said it selected ShieldSquare for its machine learning capabilities, the way it expands its existing security services, and the “strong technology synergy” between the two firms. Additionally, he said that ShieldSquare’s technology “sets a stronger path for us in the context of API and mobile application protection.” The new product line of bot management technology will boast a number of tools. This includes an out-of-path, inline-capable bot detection engine, deep behavior analysis that identifies attack intent and stops automated attacks, and anti-bot feeds to allow both proactive and preventive bot management.

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Radware Snaps Up ShieldSquare for New Bot Management Product Line

SDxCentral | January 07, 2019

ShieldSquare, founded in 2014, is a security firm that specializes in bot management. It delivers an API-based service that can detect and eliminate “bad bots” from websites, mobile applications, and APIs. The technology detects bots and relies on a cloud engine to classify visitor activity as either human, search engine crawler, or bad bots to secure the application. Bot management — according to Michael Groskop, Radware’s vice president of product management portfolio — refers to the detection of malicious bot activity and the practice of mitigating bot attacks by differentiating legitimate flows from bots. It also must allow differentiation between good and bad bots. He noted that as bots grow more sophisticated, bot management must include “advanced behavioral detection and intent analysis,” delivered with machine learning, which ShieldSquare provides. ShieldSquare technology will be offered as a new product line called Radware Bot Manager. The product line will integrate with Radware’s additional attack migration products, particularly its web application firewall (WAF) cloud services. Groskop said it selected ShieldSquare for its machine learning capabilities, the way it expands its existing security services, and the “strong technology synergy” between the two firms. Additionally, he said that ShieldSquare’s technology “sets a stronger path for us in the context of API and mobile application protection.” The new product line of bot management technology will boast a number of tools. This includes an out-of-path, inline-capable bot detection engine, deep behavior analysis that identifies attack intent and stops automated attacks, and anti-bot feeds to allow both proactive and preventive bot management.

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