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ML: What It Is& What Are The Benefits Applied To Cybersecurity

Machine learning is a form of automatic learning which, through the recognition of patterns and the use of particular algorithms, is divided into a series of different methodologies that correspond to the various scientific communities that have carried out the development.

To better understand the meaning and value of machine learning, it should be explained how computational statistics, artificial neural networks, adaptive filtering, data mining as well as image processing, and recognition systems are, in general, part of machine learning. 

All systems that allow a computer to learn without having been explicitly programmed. In recent decades, programming algorithms that allow data to be interpreted and predicted on sample-based inductive models have paved the way for machine learning, especially in those fields of computer science where designing and programming explicit algorithms is impractical.

Why Machine Learning Is Helpful For Cybersecurity

Understanding why machine learning appears attractive to those involved in cyber security is simple: identity and access management increasingly relies on a growing number of factors (from physical and behavioral biometrics to geolocation data), and to process these last, companies use algorithms. Shortly, according to experts, there will be so many interactions required for authentication and confirmation of identities in total security that it will not be possible to delegate the entire management of the system to human beings alone. 

With a view to intelligent protection, some of the work will necessarily be done by machines. Most authentications will be performed by machine learning, while human judgment will be reserved for specific cases. To give an idea of ​​the volume of activity that identity management systems face, think that Microsoft currently matters – every day! – 115.5 million blocked login attempts and 15.8 million account takeover attempts, as stated at the summit by Alex Simons, director of program management at Microsoft’s Identity Division.

Identity And Access Management: From Authentication To Recognition

Like Microsoft, IBM already uses machine learning applications for identity and access management activities. The transformation phase from authentication to recognition: to allow this, will be the application of machine learning linked with biometric authentication mechanisms. Maass said pattern recognition for physical and behavioral biometrics would be able to provide continuous authentication. And the model would be similar to the one based on human behavior, where trust is built over time and through numerous different factors.

Despite this critical introduction of machines, experts are keen to emphasize that human contribution cannot be eliminated from identity and access management activities due to the complexity of this process. Authentication is a complex operation precisely because it is difficult to prove who you are to a machine. It isn’t easy to write an endless list of Access Control Lists and different types of authorization and rights policies to define who can do what, which systems, when, where, and how.

The Risks Of Machine Learning In Cybersecurity Applications

A Space Odyssey in which the supercomputer even goes so far as to eavesdrop on the dialogue between the two astronauts. Bowman and Poole. An evocative scene that still today evokes in the general public a sort of suspicion and fear towards this technology applied to identity and access management activities. Experts believe that machine learning will become the prevailing option in access management activities, but current identity and access management systems may not yet be ready for this revolution. 

Many IAM systems rely on what the speakers have called weak signals – such as user names, passwords, security questions – that can be stolen, guessed, or falsified, rather than on solid signals such as a biometric model linked to an encryption key. So what’s the problem? The weak signals are continuously increased to raise the level of security, and managing all these elements in authentication phases is becoming a particularly complex calculation: it would be advisable to implement fewer but stronger signals in the first place. 

But the catch with IAM systems is that machine learning requires more data to be effective, the experts noted. An example? Facebook and Google present extremely accurate neural networks because these companies have so much data. For example, a small local start-up could not have so much information and therefore would not be able to achieve such a high level of accuracy.

Data Mining And Data Analytics

To improve the level of accuracy, according to experts, data mining will become a critical part of identity and access management and machine learning: access management systems, instead of relying on static profiles based on fundamental and unchanged data, will continue to extract information about users not only to authenticate them but also to monitor their access and behavior to register risks or potential threats.

However, summit speakers pointed out that predictive analytics related to user behavior may not always be accurate. If the basic guidelines for good behavior are set incorrectly, identity management systems will learn and erroneously. They will make mistakes. Another potential problem for identity and access management elaborated by machine learning is represented by the enormous and potentially infinite accumulation of data in identity management systems that will make machine learning applications in cybersecurity extremely complex.

And more difficult for professionals to manage humans. If clusters of data relating, for example, to a user’s typing patterns or mouse movements, begin to accumulate with other behavioral analytics, it becomes more challenging to navigate this sea of ​​information.  If there is too much information and therefore too many calculations, these systems can become black boxes in which it is no longer possible to be sure of what is happening.

If complexity can be avoided, experts agree: just because machines can get every bit of data and keep it forever doesn’t mean they have to. The human brain is very good at optimizing important information in this respect, and machine learning should work that way.


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