Machine Learning: why is vital for cybersecurity

Machine learning is becoming more vital than ever in today’s – and tomorrow’s -cybersecurity.

Being a subset of AI, machine learning consists of the techniques that enable computers to figure things out from the data and deliver AI applications.

Applying it to cybersecurity systems, machine learning can assess and analyze patterns and learn from them to be better prepared for future similar attacks.

Main advantages include increased proactivity in threat prevention and response as well as a reduced amount of time spent for routine activity.

Organizations indeed may benefit from machine learning as it enables a more strategic use of available resources.

Machine learning could be very useful, but cannot substitute human analysis and surveillance.

Find out on our blog why machine learning has become crucial to cybersecurity!

What machine learning is?

To understand the impact of machine learning on the cybersecurity universe, we should define it first.

It refers to “a branch of computer science and artificial intelligence which focuses on the use of data and algorithms to imitate the way humans learn”, as an IT giant defines it.

Three parts compose a standard ML algorithm: a Decision Process, an Error Function, and a Model Organization Process

Finally, you can dig further on this link of the University of Berkeley.

Machine learning & cybersecurity

Having defined it, we should outline why machine learning is beneficial to cybersecurity.

As ML is all about the development and manipulation of patterns, these functions can be used to deal with cyber threats then.

How? its algorithms can use data from network in order to detect – and in some cases – respond to security incidents.

The main advantage of this cutting-edge approach is automized certain processes, especially those performed as routine. This leads to substantial time savings and lightens workload for analysts.

AI-based ML well adapts, moreover, to keep up with the increasingly complexity of cyber threats. Nowadays, they are multilayered and can originate from different networks.

Briefly, these systems could efficiently deal with evolution of cyberthreats, when integrated in your SIEM.

To sum up, it could perform the following security activities:

• Network threat identification. This includes threat intelligence tasks.
• Automated application security
• Email monitoring
• Next generation anti-virus

It is clear that ML can be of great help. However, we issue a warning. AI-based systems are per se imperfect. As a result, they cannot substitute human monitoring. No one should forget, then, that work of analysts is going to be vital in the foreseeable future.

Additionally, a concern lays in the security of AI-systems themselves. A threat actor may well attempt to manipulate them in order to disguise an intrusion, for instance. Their intrinsic security is no less important then.

Conclusion

To conclude, ML has advantageous applications in cybersecurity that could enhance business cyber defenses.

It enables indeed to keep up with the evolution of cyber threats that has been becoming more and more sophisticated.

Finally, Telsy offers to its clients’ cyber solutions based on AI: check out our website for more information!