Many tools are developed in the cybersecurity sector to protect websites or web applications. But cybercrime never stops increasing. How can we deal efficiently with cyber risks? How can we know whom to trust? Do we need to trust humans or technology?
HTTPCS gives you the keys in this page to understand the pros and cons of both solutions and how Machine Learning is necessary in a powerful cybersecurity tool development.
HTTPCS integrates Machine Learning in all its IT security products to ensure you a cutting-edge technology. Thus, HTTPCS offers you reliable, intelligent and up-to-date solutions.
According to Wikipedia, « Automatic Learning (or Machine Learning) or Static Learning, is a subset of artificial intelligence that often uses statistical techniques to give computers the ability to 'learn' (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.»
Therefore, Machine Learning involves making the machines we created capable of learning from collected data, analyzing and learning from actions they do in order to make strategic decisions self-sufficiently without being told what to do.
Machine Learning’s first step is to «train» the robot. To do that, we need to give it lots of examples! The robot will therefore be able to observe and «learn». The point is that the machine gathers a great number of information in order to learn, reuse them to adapt itself to new situations and even anticipate them with the lessons learned from the many examples it got, hence the term «learning».
Then the robot will be able to build autonomously what is called an «internal representation», that is a kind of intelligent mapping of the situation allowing to do the requested task and thus make some «predictions», «recommendations», «decision-makings», etc.
Indeed, the robot will collect a lot of data. It is this process which makes the robot “intelligent”.Despite «Machine Learning» not being a new concept, it has been reinforced with the advent of Big Data.
There are more and more cyberattacks and data to process. Indeed, IT security experts may experience the lack of efficient tools to detect these threats.
This is where Machine Learning becomes a major asset in cyber risks detection and retreatment.
As a matter of fact, one of the greatest challenges facing cybersecurity experts is to anticipate tomorrow’s attacks. If today, it is quite easy to detect or block well-known attacks, how to deal with new types of attacks?
This is where the Machine Learning or more globally Artificial Intelligence comes into play.
Machine Learning can be particularly useful in the field of cybersecurity and especially in the detection of new hacking attacks. In fact, IT security specialists have a great quantity of data to deal with and this is exactly where Machine Learning is an undisputed champion!
If it is possible today to analyze attacks done by humans and identify the symptoms and signs on a starting infection, then there is a high likelihood of being able to predict future attacks.
How? The attack was carried out by a hacker whose human behavior is predictable, and thus foreseeable thanks to Machine Learning.
As explained above, the robot analyses a lot of examples and is then able to learn and determine predictions.
Indeed, Machine Learning becomes a necessary complement to existing tools to achieve a better protection for companies. Therefore, Automatic Learning has a full role to play in the field of cybersecurity.
However, we must not think that Machine Learning should replace humans and cybersecurity specialists. Robots are here to help humans, for particularly long or difficult tasks. By using robots and Machine Learning, humans extend their actions and increase their performance.
While robots are not yet endowed with creativity and intuition, they can be of great help to the pentesters. “Smart” robots are a major asset to process a gigantic amount of data, to highlight anomalies, to make decisions much faster than a human, but they will not replace these experts.
HTTPCS got it right and integrates Machine Learning into all its cybersecurity tools, in addition to its IT security experts.
HTTPCS offers a cybersecurity toolbox to protect efficiently your websites and web applications against hacking. In order to combine advanced technology and human expertise, HTTPCS integrates Machine Learning in its solutions. Check them out now!
Security by HTTPCS is a web vulnerability scanner. Thanks to daily or weekly automated audits, it allows to detect the security flaws in your website or web application, all year long.
Machine Learning : thanks to Machine Learning, the HTTPCS smart robot simulates the hacker’s behavior and learns from the attack scenarios it already tested in order to detect new vulnerabilities. Furthermore, by simulating attacks when a flaw has been detected, the vulnerability scanner is guaranteed zero false positive: all the detected flaws are trusted. Besides, you can redo the attack carried out by the robot to visualize the flaw and the involved risks. Plus, the robot detects automatically that a flaw has been corrected and flags it up from “to be corrected” to “corrected». Simplify as much as possible your security flaws management with Security by HTTPCS!Watch the Video More about Security by HTTPCS
Monitoring by HTTPCS is a tool which allows you to monitor the availability of a website or a web application. Be notified in real time when your website becomes unavailable or inaccessible and analyze its response time and latency statistics. It is definitely a key tool to monitor the performance of your website or your web application.
Machine Learning : thanks to Machine Learning, HTTPCS is able, by recording «footprints» of the site in its normal state, to detect any change and thus anticipate the “down” times before there arrive!Watch the Video More about Monitoring by HTTPCS
Cyber Vigilance by HTTPCS is a monitoring tool to allow you to anticipate cyberattacks. And yes, you read that right: it is possible to anticipate attacks that have not happened yet, and this tool can help you thwarting them. Whether it is a data leak or a planned hacking campaign, be the first to know thanks to the real-time alerts from Cyber Vigilance by HTTPCS.
Machine Learning : the HTTPCS smart robots crawls the darknet and the web looking for critical information. They provide in real time all the information that concerns your organization (IP, domain names, brand names, email addresses …). For example, be notified whenever one of your employees’ email address is being hacked, which could have serious consequences for your business thereafter.More about Cyber Vigilance by HTTPCS
Integrity by HTTPCS is a website integrity controller or website integrity tester.Its goal: identify if you are being hacked via the identification of changes deemed as fraudulent on your website or web application. At the slightest change, you will be notified in order to react as quickly as possible.
Machine Learning : thanks to Machine Learning, the HTTPCS smart robot is able to browse your website as a human would do, by scrolling, clicking on the buttons, viewing the modals… It is also capable to identify and scan your partners’ external links. Thus, your website is 100% mapped and the slightest change on your or on your partners’ website will be notified.Watch the Video More about Integrity by HTTPCS
Machine Learning also called Automatic Learning is a subset of Artificial Intelligence. Its goal is to process learning abilities. Artificial Intelligence is more global and aims to replicate the abilities of a human being and is not confined to learning.
Machine Learning is a very useful and powerful domain especially in cybersecurity. It allows to process huge amounts of data, to analyze and to take decisions faster than a human could do.
Machine Learning, as useful as it may be, is not designed for replacing humans and will never be able to do so. Robots are not capable to have creativity nor intuition. Cybersecurity will be more effective if it combines human and machine.
No, they don’t, because they already have many tools capable of developing new malwares. Their goal is to create new conditioning mechanisms for malwares and not to think about new attack techniques.Indeed, the Machine Learning is not a prerequisite for hackers.