Cybersecurity Protection Increasingly Depends on Machine Learning
Previously, computing power was centralized in the cloud or an on-premises data center. But many enterprise tasks require a decentralized model, where capabilities are brought closer to the devices and users that need these resources.
This need for low latency, data-rich digital capabilities is moving compute to the edge of the network. Computing power is distributed at the edge, fueling growth in data-driven intelligence among burgeoning numbers of Internet of Things (IoT) devices.
One downside to the rising popularity of edge computing is increasing infrastructure complexity. Protecting this extended infrastructure may ultimately depend on machine learning (ML) technologies to automate threat detection and response.
In this report, we explore the capabilities of machine learning for cybersecurity tasks.
Key takeaways from this special report include the following:
- 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.
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