For all of us who are dedicated, in one way or another, to cybersecurity, it is clear that we are facing an escalation of arms between cybercrime, and those who defend us from them, the Blue Teams. It is not surprising; a 2019 study already shocked us when it was found that cybercrime already moves more money than drug trafficking, and the trend in 2020 and 2021 has been increasing. The professionalization of cybercrime only explains this, contrasted by the police authorities.

We can fall into the cliché of thinking that cybercrime is a kind of evil organization, in the Kingpin style in Spiderman. And although this cliché is correct in some cases, there is a large part of cybercrime that is not committed by malicious and criminal organizations but is perpetuated by companies or individuals who want to obtain an income illegitimately want to harm a company or an organization a person.

It is not uncommon to find cybercrime sponsored by a company that wants to increase the operating costs of its main competitor, creating traffic through bots to force it to scale its infrastructures in the cloud, and thus increase its cost; just as it is not strange to find employees, or former employees, taking advantage of the inside knowledge of the company to undermine their position, or harm it in any way.

Of course, there is also the better-known version of this story. A criminal organization impersonates a legitimate user and executes malicious actions on their behalf. This is possible thanks to the fragility of users, especially those negligent who do not apply the company’s security policies to their accounts or who use the same password in their social networks as in their corporate accounts, for example.

It is not too complicated to extrapolate password leaks from cloud services (social networks, accounts on gaming platforms, etc.) with professional accounts, and it is an increasingly common and dangerous entry vector.

And it is that, as you can guess, not everything is malware or ransomware. Not everything is chaos and destruction. There are many problems that are not as flashy as ransomware but can be even more damaging. Not surprisingly, a company takes an average of 280 days to identify and contain a data breach, according to a recent IBM study. There is, therefore, a lot of room for improvement when it comes to detecting and stopping data leaks.

Many of these leaks are caused by insiders, legitimate users of an organization who conspire against it for personal gain or to cause harm. Many times these insiders are not conscious users, but they are “puppets” moved by cybercriminals, generally compromised by poor security policies or by user negligence.

In order to be able to detect this type of attack sooner, we have to stop looking at what things are and start looking at how they behave. From the classic point of view of intrusion detection, a user who legitimately accesses the platform and has not had a strange behavior pattern from the point of view of authentication (for example, has not had 500 failed authentications in the last minute, is legitimate and there is nothing more to talk about. It is not a threat. But, as we have already anticipated before, there are legitimate users who behave maliciously. Therefore, we need to look at HOW a user behaves and not just how they authenticate.

And what technology is especially good at detecting behavior patterns? Artificial intelligence in general, and machine learning in particular. This is precisely the approach being taken in the OPOSSUM project.

The approach is simple in its conception but much more complex in its implementation. Basically, the OPOSSUM project acts as a reverse proxy between users and applications. In this way, it is in a privileged position to apply security checks, in the manner of a classic Web Application Firewall (WAF), but it is also in a privileged position to analyze how a user uses a certain application, since all communication between the user and the application, it necessarily goes through OPOSSUM.

An interesting novelty of this project is in the concept of context. Classically, cybersecurity solutions have focused on analyzing flat data. Let’s take an HTTP request as an example. For a WAF to determine if the request is malicious or not, it analyzes its content in isolation. This is effective in many cases but not for detecting abnormal behavior. It is true that there are products that take into account a set of data, for example, the last 50 requests, but even so, the data on which they are based remains scarce.

The OPOSSUM project, on the other hand, increases the context of the request, enriching the data on which the predictions will be made using external sources of threat intelligence.like Shodan, Spyse or Alienvault. These platforms add more information to the simple HTTP request, such as if that IP has been involved in security incidents or if the payload of a request contains a compromise indicator.

This enrichment is done in real-time, using the Apache Big Data stack (Hadoop, Kafka, Cassandra, etc.) as a technological base, as well as other interesting technologies, as shown in the following figure.

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Making Travel Easier

A great example of optimization and making human life more comfortable is how machine learning helps with commute estimation. Travellers can have more information about their commutes, which allows them to make better plans and anticipate delays. Using Google Maps, commuters can check specific routes for traffic jams and avoid a route. Google weather apps can also tell travellers what to expect when they arrive at their destination. Riding apps have also been optimized to approximate the commute’s length, how much it will cost, and similar factors. Having this information makes our lives much more comfortable when we are on a tight schedule.

Better Communication

If you use email regularly, you have noticed that Emailing apps have gotten more comfortable to use over time. Emails now have more automated processes and can predict multiple user actions. A great example is the text prediction feature that comes on platforms like Gmail. While you are typing out your email response, the application can predict your messages, and all you need to do is allow it to automate fill out the rest of the words. You can also choose from a list of automated responses that the app provides if you do not want to type out your message.

Financial Services And Security

Machine learning has contributed to creating multiple AI systems that are used in the field of banking and personal finance. With more financial services moving online, there has been an increase in the need for financial security services. Customer data needs to be kept very safe; otherwise, the clients might find their financial information falling into the wrong hands. Banks and other online money transfer services have to prevent credit card fraud and theft of funds. Machine learning has created systems that can flag fraudulent transactions and stop the money transfer before it happens.

Social Networking

The use of machine learning to make human life easier has extended to the world of social media. Apps such as Facebook, Instagram, and YouTube now collect user data from individuals and use it to make the expert more person. Facebook now checks the uploaded photograph and detects faces, and this information is used to establish a user’s social network on the app. Snapchat has taken photography and facial detection to another level. Users can take photographs using filters that automatically fit onto the image of their faces on screen.

Technology

Like with other digital skills such as data analysis and software engineering, machine learning becomes a required course to take. It is not limited to tech experts only, and people in different fields can use this knowledge to enhance their capabilities to optimize human experiences. If you are already taking a machine learning course, you are on the right track. It would be best if you strived to master the skills and get good grades. Homework doer is a website that will help you complete your assignments on time or tackle difficult machine learning concepts when you are stuck. Best of all, the services are readily available and at great prices.

On A Final Note

Other functions that have benefited include cybersecurity, which is now more advanced and relies less on human power. An example of machine learning at the most basic level would be optimizing online websites to make the customer experience more customizable. The software can predict customer preferences and create an environment that is tailored for that specific website visitor.

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Continue reading the motherboard buying guide to know the types. It also includes some factors you need to consider when choosing a motherboard.

What Is A Motherboard?

A motherboard is a printed circuit board containing the essential components of a computer. It’s where all the other parts of your computer meet and communicate with one another. The motherboard is responsible for the real-time calculations to run apps and games.

Types Of Motherboard

There are many types of motherboards, but they work with certain types of processors and memory. In this motherboard buying guide, we explain the most common types.

ATX Motherboard

The ATX motherboard is a huge improvement from the previous type of motherboard. With ATX, you get more power phases, which give you more precise and stable power. The wider gaps between expansion slots give you better graphics and cooling.

LPX Motherboard

LPX motherboards have several output and input ports at its back. Some LPX models lack accelerated graphics ports and connect to the PCI component, instead. This type of motherboard is lighter than other types because of its slim PC build.

Pico BTX Motherboard

Pico BTX motherboards are ideal for riser-card or half-height applications. They support two slots for expansion and share a similar top as the BTX motherboards.

Choosing A MotherBoard Right For You

Choosing a motherboard is the most integral part of your PC build since they affect the performance. Here are some motherboard considerations for the components.

Form Factor

The form factor is the set of standards that include the shape and size of the motherboard. It also consists of the arrangement of the connecting ports, power interface, and type of connectors. You can pick how big you want your systems to be, from the smallest Mini-ITX to E-ATX.

CPU Sockets

CPU sockets or the processor sockets are the sockets housing the CPU. Most processors only have one specific CPU socket, which makes the selection of motherboards limited.

Chipset

The chipsets are responsible for how the hardware interacts with one another. Your CPU will usually tell you the compatible chipsets. If you get the wrong chipset, the BIOS won’t detect the CPU rendering your PC useless.

You can go for the highest-end consumer intel or AMD chips such as Core X or Threadripper with b550 or higher. It all comes down to how many features you need, along with its perks and features.

RAM

Make sure that you know what you’ll be using your PC for. Makes sure to get RAM with 16GB or higher if you plan to use your PC for intensive tasks. Consider getting a motherboard with four memory slots, in case 16GB is not enough.

BIOS

The BIOS is the software that detects the components and hardware of your computer. The BIOS is the first thing to run when you start your computer. Its function is to manage the data flow between the OS and hardware, specifically the CPU, RAM, and hard disks.

The Ultimate Motherboard Buying Guide

You need not content yourself with the most basic graphics when you’re gaming. The best motherboard will help you enhance and elevate your gaming experience. Use this motherboard buying guide to get the best one today!

Do you want to learn more about motherboards? Check out more of our guides to learn all you need to know today!

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With these databases, it makes it easier to analyze information from numerous applications without spending too much. Because IoT (Internet of Things) and social media have surged in popularity, there is a great deal of data that must be gathered and analyzed. After all, analysis is what gives data its value. It’s important to look for insights and patterns that can be useful to you. Traditional databases can’t always handle the amount of data that needs to be analyzed. Open-source software is an affordable and flexible solution. It’s changed the way people use database management systems.

It can take a long time to find database software and management tools, and the process can be costly as well. Many tools offer far more than the typical user needs. In many cases, these tools will have features that will go unused. Luckily, on this list of open source options, you’ll find a wide range of solutions that should work for you.

In some cases, these options come from vendors that hope to persuade you to purchase the product that they are selling. In other cases, solutions come from the development community. Many people want data management to be democratized.

PostgreSQL:

This relational database is open-source, free, and is not owned by an organization. It offers a wide array of useful features, such as data importing, exporting, and indexing. User configuration and version control settings allow you to customize your experience. It can be used to build NoSQL databases via Python or JSON.

This is a particularly effective solution for businesses within the financial industry. Because it is ACID compliant, it can be used to process online transactions. PostgreSQL is used in many other industries as well, including scientific research and manufacturing.

Based on benchmark testing, the latest version of PostgreSQL is significantly improved. Using high-performing tools can increase productivity, which can lead to an increase in profits as well. It’s also possible to use PostgreSQL alongside software from third parties, such as EDB Postgres to enhance the speed & efficiency of the database. If you are currently using PostgreSQL, you’ll want to update to the latest version as soon as possible so that you can enjoy these improvements.

MongoDB:

This database can store high volumes of data and is designed around documents. This open-source NoSQL database is highly flexible. The database is written in C++ and stores data via chunks. Related data is sorted together.

This is a flexible alternative to many other relational databases. If you need to add fields or make other changes, you’ll be able to do so without causing any major issues with the application. Since this database is easy for new users to learn, it’s a very popular option.

It can be used for larger data products, including content and configuration management, real-time analytics, cataloging products for e-commerce businesses, data logging and caching social networking, and many other projects.

SQ Lite:

This relational database management system can be used for database administration, setup, and essential source. The name contains the word “light” because this system is designed to be lightweight. It’s located within the C programming library.

Because this system is self-contained, it can process data of all types. It doesn’t need to be installed before it can be connected with the app. Instead, it will integrate with your applications.

It’s a great option for teams and businesses that are looking for a solution and don’t need a lot of extra features. It’s well suited to development and testing projects on a smaller scale.

Apache Cassandra:

This NoSQL database management system is capable of handling significant amounts of structured data while avoiding failure points. It is free and open-source. If you need a solution that can scale and meets your availability needs, but you also want excellent performance, this is a terrific option.

It can handle heavier write loads without as many reads. It’s a great choice for e-commerce and entertainment sites as well as messaging systems.

Airtable:

Airtable database software, which is cloud-based, offers a wide range of features that aren’t always found in open-source database management solutions. From data tables to file sharing functionality to the ability to store files and track documents, these features make Airtable an option worth considering.

With these tools, you can focus on task management using calendars, spreadsheets, and Kanbad dashboard.

If you opt for the free plan, you’ll have 2 gigabytes of file attachments, two weeks of snapshot revision and history, a capacity of 1,200 records, and unlimited data tables.

This is an excellent solution for teams on the smaller side that want to take advantage of these features. You don’t need programming skills to get a lot out of Airtable. When you use this software, modifying, adding, and deleting data is simple.

Sonadier:

This application is cloud-based and can be used to build forms and databases. Because it utilizes a drag-and-drop interface, building and editing forms is a simple process. It offers many high-value features, including file management, data versioning, the ability to import and access data, and user permissions for data access.

If you opt for the free plan, you’ll be able to create and store as many as 10,000 files and forms. The free plan supports up to 5 users. If you want to provide access to more than 5 users, or if you want access to features like custom groups and domains, single-sign-on, and version history, you will have the option of upgrading.

This is an effective solution for smaller and medium-sized businesses. This is a simple and highly effective tool for building databases and web forms.

Conclusion

When deciding on an open-source database, you’ll need to take the unique needs of your business into account. The size of your system needs to be considered. If your database is smaller, or if it has more limited use, a lightweight solution will be your best option. You won’t have to spend as much time on debugging, and it will be much easier to implement.

If your business is still growing, or if you have a larger system, it’s best to opt for a solution that’s a little more complex, such as PostgreSQL. Although this will take more time upfront, it could save time in the long run. As your business expands, your databases won’t have to be re-coded.

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It is, therefore, a process of induction of knowledge, that is, a method that allows a general statement to be obtained by generalization from statements that describe particular cases.

When all the particular cases have been observed, induction is considered complete, so the generalization it gives rise to is considered valid. However, in most cases it is impossible to obtain a complete induction, so the statement that it gives rise to is subject to a certain degree of uncertainty, and therefore cannot be considered as a formally valid inference scheme or can empirically justify.

In many cases the field of action of machine learning overlaps with that of Data Mining, since the two disciplines are focused on data analysis, however, machine learning focuses more on the study of the computational complexity of problems with the intention of making them feasible from a practical point of view, not only theoretical.

At a very basic level, we could say that one of the tasks of the AA is to try to extract knowledge about some unobserved properties of an object based on the properties that have been observed of that same object (or even of properties observed in other similar objects).

Or, in plainer words, predicting future behavior based on what has happened in the past. A very topical example would be, for example, predicting whether a certain product will be liked by a customer based on the ratings that the same customer has made of other products that they have tried.

In any case, since the topic, we are talking about is related to learning, the first thing we have to ask ourselves is: What do we understand by learning? and, since we want to give general methodologies to produce learning automatically, once we establish this concept we will have to give methods to measure the degree of success/failure of learning.

In any case, since we are transferring an intuitive concept that we normally use in everyday life to a computational context, it must be borne in mind that all the definitions that we give of learning from a computational point of view, as well as the various forms of Measure it, they will be intimately related to very specific contexts and possibly far from what intuitively, and in general, we understand by learning.

A relatively general definition of learning within the human context could be the following: the process through which skills, abilities, knowledge, behaviors, or values are acquired or modified as a result of the study, experience, instruction, reasoning, and observation.

From this definition it is important to note that learning must occur from experience with the environment, learning is not considered all the skills or knowledge that are innate in the individual or that are acquired as a result of the natural growth of the latter. Following a similar scheme, in AA we will consider learning what the machine can learn from experience, not from the recognition of patterns programmed a priori.

Therefore, a central task of how to apply this definition to the context of computing will be to feed the experience of the machine by means of objects with which to train ( examples ) to subsequently apply the patterns that it has recognized on other objects. different (in a product recommendation system, an example would be a particular customer/product pair, along with information about the valuation that the latter has made of it).

There are a large number of problems that fall within what we call inductive learning. The main difference between them lies in the type of objects they are trying to predict. Some common classes are:

Regression:

They try to predict real value. For example, predicting the value of the stock market tomorrow from the behavior of the stock that is stored (past). Or predict a student’s grade in the final exam based on the grades obtained in the various tasks carried out during the course.

Classification (Binary Or Multiclass):

They try to predict the classification of objects on a set of prefixed classes. For example, classifying whether a certain news item is sports, entertainment, politics, etc. If only 2 possible classes are allowed, then it is called binary classification; if more than 2 classes are allowed, we are talking about multiclass classification.

Ranking:

Try to predict the optimal order of a set of objects according to a predefined order of relevance. For example, the order in which a search engine returns internet resources in response to a search by a user.

Normally, when dealing with a new AA problem, the first thing to do is to frame it within one of the previous classes, since depending on how it is classified, it will be the way in which we can measure the error made between prediction and reality.

Consequently, the problem of measuring how successful the learning obtained is must be addressed for each particular case of applied methodology, although in general, we can anticipate that we will need to “embed” the representation of the problem in a space in which we have defined a measure.

On the other hand, and depending on the type of output that is produced and how the treatment of the examples is approached, the different AA algorithms can be grouped into:

Supervised Learning:

A function is generated that matches the desired inputs and outputs of the system, where the knowledge base of the system is made up of a priori labeled examples (that is, examples of which we know their correct classification). An example of this type of algorithm is the classification problem that we mentioned earlier.

Unsupervised Learning:

Where the modeling process is carried out on a set of examples made up solely of inputs to the system, without knowing its correct classification. So it is sought that the system is able to recognize patterns to be able to label the new entries.

Semi-Supervised Learning:

It is a combination of the two previous algorithms, taking into account classified and unclassified examples.

Reinforcement Learning:

In this case, the algorithm learns by observing the world around it and with a continuous flow of information in both directions (from the world to the machine, and from the machine to the world) by performing a trial-error process, and reinforcing those actions that receive a positive response in the world.

Transduction:

It is similar to supervised learning, but its objective is not to explicitly build a function, but only to try to predict the categories into which the following examples fall based on the input examples, their respective categories, and the new examples to the system. In other words, it would be closer to the concept of dynamic supervised learning.

Multi-Task Learning:

Encompasses all those learning methods that use knowledge previously learned by the system in order to face problems similar to those already seen.

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Machine learning systems are used to create predictive models based on continuous input that is used to be able to anticipate, predict and make decisions.

Machine learning models learn from the data and can adjust themselves to produce better results. The more data they have, the faster they will learn and the more accurate their results will be. It is continuous improvement, applied to knowledge.

Related Topic: Machine Learning: How It Impacts The Business

Machine learning and data management: A Great Opportunity To Utilize

Managing the data of an organization is a growing challenge for companies. However, the solution to this challenge does not lie in focusing on business processes and systems, but rather has to do with innovation.

Resorting to machine learning by training an algorithm and achieving a predictive model is the way to transform difficulty into opportunity and turn inconveniences into benefits such as the following:

1. An Increasing Volume Of Data

If managing complex, heterogeneous, fast data in a big data environment escapes human capabilities, the same does not happen with machine learning. It takes advantage of all those zettabytes of information and exploits the advantages of the billions of IoT sensors that are connected today, to learn and contribute to creating a smarter system.

Also Read: The Origin And Complete Concept Of Big Data

2. A Number Of Business Users Continues To Grow: 

Although it poses a security challenge for companies that must scrupulously take care of endpoint management, it is extremely effective in preventing the algorithm from continuing to learn continuously.

3. New Habits:

Migrations, data transformation, data integration or advanced analytical processes, are not exceptional circumstances in any organization; rather, they are patterns that are repeated more and more, as business users opt for experimentation and organizations empower them to do so.

Equipping them with the appropriate tools. Machine learning takes advantage of all these inputs to continue learning and bringing new perspectives, a more complete vision and a deeper knowledge of each piece of information to the system.

Using machine learning for data management is an extraordinary opportunity to move towards an information-based leadership model that propels the organization toward success in each of its disruptive initiatives. In turn, it will allow you to find answers to all those questions that you could never have allowed yourself to answer, due to budget constraints or simply because it was not humanly possible.

Machine Learning: 4 Benefits You Should Know

Is data your priority? Is your organization ready to unleash the potential of every bit of information? It is important to keep in mind that the results of any digital initiative can only be as good as the quality of the data on which it is executed.

In addition to implementing quality software to ensure adequate standards, the decision to opt for machine learning for data management has many benefits for business users. For example:

1. Increased data delivery speed for critical business initiatives.
2. Increased productivity and process effectiveness.
3. Improved recommendation adequacy, when the predictive model is combined with metadata visibility across the enterprise.
4. Latency reduction thanks to the automation of many data management tasks.

 Artificial intelligence in general – and, in this case, machine learning in particular – opens up a world of possibilities previously unthinkable for human intelligence. We see it in medical diagnoses, in massive facial recognition analyzes, and even now with the COVID-19 contagion tracking.

Companies are already developing these types of projects to take advantage of every bit of their data and thus solve strategic questions, identify large-scale patterns and predict scenarios, among many other uses that, previously due to time, cost and space, were not could carry out.

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New Machine Learning algorithms help solve corporate problems using more efficient and more accurate ways than with outdated prediction techniques.

Although most consumers don’t know it, different Machine Learning methods are being implemented at high speed in a large number of everyday situations. Here are 4 of the best-known ways to use Machine Learning these days.

Natural Language Processing (PLN)

Machine learning systems are becoming more advanced, so they are able to understand the language with which we relate to humans and can respond to us in our own language. This is called Natural Language Processing.

Some Of The Applications Of The Natural Language Processing Are As Follows:

• Automatic translation of texts
• Analysis of feelings
• Natural Language Processing to improve conversational systems
• Automatically summarize with Natural Language Processing
• The Natural Language Processing saves time and streamlines work by automating processes done by hand, allowing us to make decisions more easily.

The Natural Language Processing saves time and streamlines work by automating processes done by hand, allowing us to make decisions more easily.

Data Mining

Tools Data Mining (Data Mining) explore within large databases, automatically or semi-automatically, with the aim of finding repeating patterns, trends or rules that help us to explain the behaviour of the same data in a given context.

What can data mining be used for? The most outstanding applications in the business world are the following:

• Design of strategies based on concrete information
• Better understand the habits, customs and preferences of users
• Facilitate the search for relevant information
• Predicting consumer behaviour
• Detect the risk of customer abandonment

Thanks to modern systems that use data mining, we streamline the tasks of our companies to be more efficient.

Artificial Vision

The Vision ( Computer Vision ), scientific discipline, is concerned with developing artificial systems that obtain information from multidimensional data. That is to say, the Artificial Vision includes processes to acquire, process, analyze and understand images of the real world in which we live in order to produce numerical or symbolic information that can be processed by a computer.

Some of the best-known applications of Machine Vision in the business world:

• QA.
• Classification.
• Counting of products.
• Positioning
• Rotation control.
• Pick and place.

Artificial vision helps to achieve strategic objectives in terms of product quality improvement, increased productivity and reduced production costs. The data collected on parts defects provides an opportunity to identify and fix production line problems.

Robotics

The term “Robotic” is usually incorporated into that of Artificial Intelligence, but not all robots are “Intelligent”. Robots are machines that do a job for themselves, following a set of rules programmed by a computer. Currently, AI and Machine Learning are applied in a limited way to enhance the capabilities of industrial robotic systems.

Supply chains and companies specializing in logistics applications are among the first to implement Artificial Intelligence and machine learning in their robotics tools. Robots are also used in medicine and education, both in operating rooms and in classrooms.

Conclusion:

The number of applications and solutions offered by Machine Learning is enormous. At the same time, we could ensure that the main advantage of machine learning is the ability to sort variables, that is, find and structure data that in some way affects the result. It is for this reason that they are so effective in predicting and determining results, thus helping us to achieve our goals.

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