PHASES OF AN INCIDENT RESPONSE PLAN

March 17, 2019 | 131 views

As per the Ponemon study in 2018, there is an increase of 6.4% of the global average cost of a data breach in comparison to the previous year. The study evidently depicts the need for an incident response plan (IRP) [1]. An IRP is mainly responsible for outlining the procedure to be followed, after the occurrence of a security breach, apart from other cyber threats. Without an IRP, the process of managing the damage of a security breach becomes cumbersome and confusing. This leads to an unnecessary waste of time and money. With the presence of rapidly spreading malware, as we witnessed in the case of WannaCry, the infection can easily cross international borders in no time.

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HALOCK Security Labs

HALOCK is a U.S.-based information security consultancy that is privately owned and operated out of its headquarters in Schaumburg, IL since 1996. From mid-sized to the Fortune 100, HALOCK’s clients span a variety of industries including financial services, healthcare, legal, education, energy, SaaS/cloud, enterprise retail, and many others. HALOCK strives to be your security partner, providing both strategic and technical security offerings. HALOCK combines strong thought leadership, diagnostic capabilities, and deep technical expertise with a proven ability to get things done.

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SOFTWARE

The Revolutionary Power of 5G in Automation and Industry Digitization

Article | August 8, 2022

Fifth-generation (5G) mobile phone networks that can carry data up to 50 times faster than major carriers' current phone networks are now rolling out. But 5G promises to do more than just speed up our phone service and download times. The mobile industry's fifth-generation (5G) networks are being developed and are prepared for deployment. The expansion of IoT and other intelligent automation applications is being significantly fueled by the advancing 5G networks, which are becoming more widely accessible. For advancements in intelligent automation—the Internet of Things (IoT), Artificial Intelligence (AI), driverless cars, virtual reality, blockchain, and future innovations we haven't even considered yet—5 G's lightning-fast connectivity and low-latency are essential. The arrival of 5G represents more than simply a generational shift for the tech sector as a whole. Contributions by 5G Networks For a number of reasons, the manufacturing sector is moving toward digitalization: to increase revenue by better servicing their customers; to increase demand; to outperform the competition; to reduce costs by boosting productivity and efficiency; and to minimize risk by promoting safety and security. The main requirements and obstacles in the digitization industry were recently recognized by a study. Millions of devices with ultra-reliable, robust, immediate connectivity. Gadgets, which are expensive with a long battery life. Asset tracking along the constantly shifting supply chains. Carrying out remote medical operations. Enhancing the purchasing experience with AR/VR. Implementing AI to improve operations across the board or in various departments. The mobile telecommunications requirements of the Internet of Things cannot be met by the current 4G and 4G LTE networks. Compared to current 4G LTE networking technologies, 5G can also offer a solution to the problem and the quickest network data rate with a relatively low cost and greater communication coverage. The 5G network's quick speeds will lead to new technical developments. The upcoming 5G technology will support hundreds of billions of connections, offer transmission speeds of 10 Gbps, and have an extremely low latency of 1 ms. Additionally, it makes rural areas' services more dependable, minimizing service disparities between rural and urban areas. Even though the 5G network is a development of the 4G and 4G LTE networks, it has a whole new network design and features like virtualization that provide more than impressively fast data speeds.

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SOFTWARE

AI's Impact on Improving Customer Experience

Article | August 2, 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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AI TECH

The Evolution of Quantum Computing and What its Future Beholds

Article | July 20, 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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FUTURE TECH

Language Models: Emerging Types and Why They Matter

Article | July 7, 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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Spotlight

HALOCK Security Labs

HALOCK is a U.S.-based information security consultancy that is privately owned and operated out of its headquarters in Schaumburg, IL since 1996. From mid-sized to the Fortune 100, HALOCK’s clients span a variety of industries including financial services, healthcare, legal, education, energy, SaaS/cloud, enterprise retail, and many others. HALOCK strives to be your security partner, providing both strategic and technical security offerings. HALOCK combines strong thought leadership, diagnostic capabilities, and deep technical expertise with a proven ability to get things done.

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Malicious ESLint Packages Steal Software Registry Login Tokens

SecurityWeek | July 16, 2018

Following the compromise of an ESLint maintainer’s account last week, malicious packages that attempted to steal login tokens from the npm software registry were published without authorization. Originally created by Nicholas Zakas, ESLint is described as an open source "pluggable and configurable linter tool" for identifying and reporting on patterns in JavaScript. The issue affected version 3.7.2 of the popular package eslint-scope, as well as version 5.0.2 of eslint-config-eslint. The former is a scope analysis library used by older versions of eslint, and the latest versions of babel-eslint and webpack, while the latter is a configuration used internally by the ESLint team. Both packages were hosted on npm, “the package manager for JavaScript and the world’s largest software registry,” which has faced similar incidents before. Upon installation, the rogue packages would download and execute code from pastebin.com. The code was designed to grab the content of the user’s .npmrc file, which usually contains access tokens for publishing to npm, and send the information to the attacker. “The script extracts the _authToken from a user's .npmrc and sends it to histats and statcounter inside the Referer header,” Henry Zhu notes. Both of the malicious packages were unpublished from npm soon after the issue was discovered. Furthermore, the pastebin.com paste linked in these packages was taken down as well, ESLint explains in a post. The npm login tokens targeted by the malicious code would not provide the attacker with the user’s npm password, but npm nonetheless decided to revoke possibly impacted tokens. Users too have the option to revoke existing tokens and npm advised them to do so.

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Malicious ESLint Packages Steal Software Registry Login Tokens

SecurityWeek | July 16, 2018

Following the compromise of an ESLint maintainer’s account last week, malicious packages that attempted to steal login tokens from the npm software registry were published without authorization. Originally created by Nicholas Zakas, ESLint is described as an open source "pluggable and configurable linter tool" for identifying and reporting on patterns in JavaScript. The issue affected version 3.7.2 of the popular package eslint-scope, as well as version 5.0.2 of eslint-config-eslint. The former is a scope analysis library used by older versions of eslint, and the latest versions of babel-eslint and webpack, while the latter is a configuration used internally by the ESLint team. Both packages were hosted on npm, “the package manager for JavaScript and the world’s largest software registry,” which has faced similar incidents before. Upon installation, the rogue packages would download and execute code from pastebin.com. The code was designed to grab the content of the user’s .npmrc file, which usually contains access tokens for publishing to npm, and send the information to the attacker. “The script extracts the _authToken from a user's .npmrc and sends it to histats and statcounter inside the Referer header,” Henry Zhu notes. Both of the malicious packages were unpublished from npm soon after the issue was discovered. Furthermore, the pastebin.com paste linked in these packages was taken down as well, ESLint explains in a post. The npm login tokens targeted by the malicious code would not provide the attacker with the user’s npm password, but npm nonetheless decided to revoke possibly impacted tokens. Users too have the option to revoke existing tokens and npm advised them to do so.

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