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An Insight Into OWASP Top 10 Vulnerabilities

When you are dealing with a website it is important to be on top in terms of security and vulnerabilities. OWASP goes by the name of open web application security project that is an online methodology going on to incorporate tools, documentation, articles, in the domain of web application security. It showcases the list of […]

The post An Insight Into OWASP Top 10 Vulnerabilities appeared first on Aiiot Talk – Artificial Intelligence | Internet of Things | Technology.

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When you are dealing with a website it is important to be on top in terms of security and vulnerabilities. OWASP goes by the name of open web application security project that is an online methodology going on to incorporate tools, documentation, articles, in the domain of web application security.

It showcases the list of the top 10 major vulnerabilities and normally it is updated every 3 to 4 years. Now let us understand about OWASP top 10 mobile in details.

Injection

Such a scenario arises when an attacker dishes out invalid data to a web application. Here the intention is to make the application do something that it was not supposed to be doing. Preventing such injection vulnerability depends upon the technology you are planning to be using.

“For example, if you are using word press you can limit vulnerabilities by reducing the amount of plug- INS or installation of themes.” 

If there is a customized web application team along with a team of developers ensure that they follow the protocols of security development while writing or designing software.

Broken Authentication

Here an attack resorts to the use of manual or automatic methods, so as to gain control over any account they thrive in a system. It could be worse as they might be looking to gain control over an entire system. A website with broker authentication protocol is a common concept on the web.

To prevent their occurrence make sure that the developers resort to the use of best practices for security control. You can provide them with the access to security audit reports and the code has to be properly tested before you employ them on production platforms.

Sensitive Exposure to Data

It is one of the popular vulnerabilities, as part of the OWASP list. It needs to be incorporated with compromising data whose protection was necessary. For an organization, it is of utmost importance to have a fair understanding of privacy and information of the users.

The onus is on the company to comply with the privacy laws. The concept of sensitive data handling has assumed a lot of importance since the emergence of GDRP. In fact, this is a new form of privacy law that came into operation in May 2018. Even the data that is at transit should be protected in all forms.

External Entities

It is a form of attack against any application incorporating an XML output. In fact, most of the XML parsers are bound to be vulnerable to an XXE attack. The responsibility of a developer is to ensure that the application does not face such vulnerability. There are a series of controls that you can resort to prevent their occurrence.

Access Control at a Broken Level

As per security control, access control points to limits on what pages or sections security may reach. Once again this would depend upon their needs. An example is if you are the owner of an eCommerce store you might be needing access to an admin panel to be adding a new product or rolling out promotion offers.

Then you can allow the rest of the users to be using the log in page. In fact, this is a problem that most CMS face up these days.

Security Misconfigurations

The concept of brute force is resorting to the use of a series of combinations. Numerous variants come into play to enhance the success rate. In modern times CMS could be tricky from a security aspect of an end-user. Most of the common attack types emerge to be automated. Most attacks are known to rely on users to have a default setting.

“There is a possibility to end up with attacks if you don’t follow or change the default settings during the installation process of CMS.” 

Cross-site Scripting

XSS is a rampant vulnerability that has an impact on numerous web applications. The concept behind XSS is that it paves way for an attacker to inject content on to the website, and alter their display where the victim’s browser secures the code that is provided by an attacker when they load the page.

It is present in two-thirds of all the applications. It needs the interaction of a particular type of user and if you are not able to dispatch it this poses to be a danger to the site.

Insecure Deserialization

This security risk emerged from a data survey and it has got nothing to do with quantifiable research. The web developer has to take solace from the fact that an attacker is expected to be playing with everything that is going to interact with their application and URL. In the domain of computer science, an object is present in the form of a data structure a route to structure the data.

Lacking Monitoring and Logging

The security of a website is of utmost importance. Yes, you cannot achieve a 100 % security protocol, but there are ways by which you can ensure the security of your website. It also points to the fact that when something happens immediate action can be taken. If you do not have a proper logging and monitoring process in place this tends to complicate the process.

“There is a suggestion that each and every website has to be monitored in a proper way. For any suspicious activity on your website have an audit log ready.” 

It works out to be a type of document that goes on to detect any anomalies, and the person who is responsible ensures that comprising of the account has not taken place. It is hard for users to be conducting the audit log regularly.

There could be a host of reasons when you are running outdated software on the web application the fact of the matter is that you can leave it unprotected. For some users performing audit logs manually could turn out to be a difficult task.

Also, Read How To Prevent Credential Stuffing Attack

Source: https://www.aiiottalk.com/data-and-security/owasp-vulnerabilities/

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How does it know?! Some beginner chatbot tech for newbies.

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Wouter S. Sligter

Most people will know by now what a chatbot or conversational AI is. But how does one design and build an intelligent chatbot? Let’s investigate some essential concepts in bot design: intents, context, flows and pages.

I like using Google’s Dialogflow platform for my intelligent assistants. Dialogflow has a very accurate NLP engine at a cost structure that is extremely competitive. In Dialogflow there are roughly two ways to build the bot tech. One is through intents and context, the other is by means of flows and pages. Both of these design approaches have their own version of Dialogflow: “ES” and “CX”.

Dialogflow ES is the older version of the Dialogflow platform which works with intents, context and entities. Slot filling and fulfillment also help manage the conversation flow. Here are Google’s docs on these concepts: https://cloud.google.com/dialogflow/es/docs/concepts

Context is what distinguishes ES from CX. It’s a way to understand where the conversation is headed. Here’s a diagram that may help understand how context works. Each phrase that you type triggers an intent in Dialogflow. Each response by the bot happens after your message has triggered the most likely intent. It’s Dialogflow’s NLP engine that decides which intent best matches your message.

Wouter Sligter, 2020

What’s funny is that even though you typed ‘yes’ in exactly the same way twice, the bot gave you different answers. There are two intents that have been programmed to respond to ‘yes’, but only one of them is selected. This is how we control the flow of a conversation by using context in Dialogflow ES.

Unfortunately the way we program context into a bot on Dialogflow ES is not supported by any visual tools like the diagram above. Instead we need to type this context in each intent without seeing the connection to other intents. This makes the creation of complex bots quite tedious and that’s why we map out the design of our bots in other tools before we start building in ES.

The newer Dialogflow CX allows for a more advanced way of managing the conversation. By adding flows and pages as additional control tools we can now visualize and control conversations easily within the CX platform.

source: https://cloud.google.com/dialogflow/cx/docs/basics

This entire diagram is a ‘flow’ and the blue blocks are ‘pages’. This visualization shows how we create bots in Dialogflow CX. It’s immediately clear how the different pages are related and how the user will move between parts of the conversation. Visuals like this are completely absent in Dialogflow ES.

It then makes sense to use different flows for different conversation paths. A possible distinction in flows might be “ordering” (as seen here), “FAQs” and “promotions”. Structuring bots through flows and pages is a great way to handle complex bots and the visual UI in CX makes it even better.

At the time of writing (October 2020) Dialogflow CX only supports English NLP and its pricing model is surprisingly steep compared to ES. But bots are becoming critical tech for an increasing number of companies and the cost reductions and quality of conversations are enormous. Building and managing bots is in many cases an ongoing task rather than a single, rounded-off project. For these reasons it makes total sense to invest in a tool that can handle increasing complexity in an easy-to-use UI such as Dialogflow CX.

This article aims to give insight into the tech behind bot creation and Dialogflow is used merely as an example. To understand how I can help you build or manage your conversational assistant on the platform of your choice, please contact me on LinkedIn.

Source: https://chatbotslife.com/how-does-it-know-some-beginner-chatbot-tech-for-newbies-fa75ff59651f?source=rss—-a49517e4c30b—4

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Who is chatbot Eliza?

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Discover the whole story.

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Frédéric Pierron

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Its creator, Joseph Weizenbaum was a researcher at the famous Artificial Intelligence Laboratory of the MIT (Massachusetts Institute of Technology). His goal was to enable a conversation between a computer and a human user. More precisely, the program simulates a conversation with a Rogérian psychoanalyst, whose method consists in reformulating the patient’s words to let him explore his thoughts himself.

Joseph Weizenbaum (Professor emeritus of computer science at MIT). Location: Balcony of his apartment in Berlin, Germany. By Ulrich Hansen, Germany (Journalist) / Wikipedia.

The program was rather rudimentary at the time. It consists in recognizing key words or expressions and displaying in return questions constructed from these key words. When the program does not have an answer available, it displays a “I understand” that is quite effective, albeit laconic.

Weizenbaum explains that his primary intention was to show the superficiality of communication between a human and a machine. He was very surprised when he realized that many users were getting caught up in the game, completely forgetting that the program was without real intelligence and devoid of any feelings and emotions. He even said that his secretary would discreetly consult Eliza to solve his personal problems, forcing the researcher to unplug the program.

Conversing with a computer thinking it is a human being is one of the criteria of Turing’s famous test. Artificial intelligence is said to exist when a human cannot discern whether or not the interlocutor is human. Eliza, in this sense, passes the test brilliantly according to its users.
Eliza thus opened the way (or the voice!) to what has been called chatbots, an abbreviation of chatterbot, itself an abbreviation of chatter robot, literally “talking robot”.

Source: https://chatbotslife.com/who-is-chatbot-eliza-bfeef79df804?source=rss—-a49517e4c30b—4

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FermiNet: Quantum Physics and Chemistry from First Principles

Weve developed a new neural network architecture, the Fermionic Neural Network or FermiNet, which is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.

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Unfortunately, 0.5% error still isn’t enough to be useful to the working chemist. The energy in molecular bonds is just a tiny fraction of the total energy of a system, and correctly predicting whether a molecule is stable can often depend on just 0.001% of the total energy of a system, or about 0.2% of the remaining “correlation” energy. For instance, while the total energy of the electrons in a butadiene molecule is almost 100,000 kilocalories per mole, the difference in energy between different possible shapes of the molecule is just 1 kilocalorie per mole. That means that if you want to correctly predict butadiene’s natural shape, then the same level of precision is needed as measuring the width of a football field down to the millimeter.

With the advent of digital computing after World War II, scientists developed a whole menagerie of computational methods that went beyond this mean field description of electrons. While these methods come in a bewildering alphabet soup of abbreviations, they all generally fall somewhere on an axis that trades off accuracy with efficiency. At one extreme, there are methods that are essentially exact, but scale worse than exponentially with the number of electrons, making them impractical for all but the smallest molecules. At the other extreme are methods that scale linearly, but are not very accurate. These computational methods have had an enormous impact on the practice of chemistry – the 1998 Nobel Prize in chemistry was awarded to the originators of many of these algorithms.

Fermionic Neural Networks

Despite the breadth of existing computational quantum mechanical tools, we felt a new method was needed to address the problem of efficient representation. There’s a reason that the largest quantum chemical calculations only run into the tens of thousands of electrons for even the most approximate methods, while classical chemical calculation techniques like molecular dynamics can handle millions of atoms. The state of a classical system can be described easily – we just have to track the position and momentum of each particle. Representing the state of a quantum system is far more challenging. A probability has to be assigned to every possible configuration of electron positions. This is encoded in the wavefunction, which assigns a positive or negative number to every configuration of electrons, and the wavefunction squared gives the probability of finding the system in that configuration. The space of all possible configurations is enormous – if you tried to represent it as a grid with 100 points along each dimension, then the number of possible electron configurations for the silicon atom would be larger than the number of atoms in the universe!

This is exactly where we thought deep neural networks could help. In the last several years, there have been huge advances in representing complex, high-dimensional probability distributions with neural networks. We now know how to train these networks efficiently and scalably. We surmised that, given these networks have already proven their mettle at fitting high-dimensional functions in artificial intelligence problems, maybe they could be used to represent quantum wavefunctions as well. We were not the first people to think of this – researchers such as Giuseppe Carleo and Matthias Troyer and others have shown how modern deep learning could be used for solving idealised quantum problems. We wanted to use deep neural networks to tackle more realistic problems in chemistry and condensed matter physics, and that meant including electrons in our calculations.

There is just one wrinkle when dealing with electrons. Electrons must obey the Pauli exclusion principle, which means that they can’t be in the same space at the same time. This is because electrons are a type of particle known as fermions, which include the building blocks of most matter – protons, neutrons, quarks, neutrinos, etc. Their wavefunction must be antisymmetric – if you swap the position of two electrons, the wavefunction gets multiplied by -1. That means that if two electrons are on top of each other, the wavefunction (and the probability of that configuration) will be zero.

This meant we had to develop a new type of neural network that was antisymmetric with respect to its inputs, which we have dubbed the Fermionic Neural Network, or FermiNet. In most quantum chemistry methods, antisymmetry is introduced using a function called the determinant. The determinant of a matrix has the property that if you swap two rows, the output gets multiplied by -1, just like a wavefunction for fermions. So you can take a bunch of single-electron functions, evaluate them for every electron in your system, and pack all of the results into one matrix. The determinant of that matrix is then a properly antisymmetric wavefunction. The major limitation of this approach is that the resulting function – known as a Slater determinant – is not very general. Wavefunctions of real systems are usually far more complicated. The typical way to improve on this is to take a large linear combination of Slater determinants – sometimes millions or more – and add some simple corrections based on pairs of electrons. Even then, this may not be enough to accurately compute energies.

Source: https://deepmind.com/blog/article/FermiNet

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