Cybersecurity is increasinly becoming more dependent on machine learning; ultimately relying on a distributed network model. Companies must be ready for this and eqquiped correctly to be succesful. There are downsides that come with this, one being: increasing infrastructure complexity.

In this special report, you can explore the capabilities of machine learning for cybersecurity tasks. Key takeaways:

• How machine learning algorithms can infer relationships and patterns of previously unseen activity to recognize network activity that indicates pending attacks.
• While cyberattacks on IoT devices grow, CIOs and CISOs mistakenly assume they are required to purchase separate point solutions, build a separate IoT security team and change IT security processes to bring it all together, begging the question “is it possible to secure IoT devices without spending on additional infrastructure or upsetting the already established IT status quo?”
• As organizations take advantage of emerging 5G connectivity to exploit more data from more devices, they must guard against the potential of bad actors hijacking ML-embedded devices and broadband capacity.