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“The way to address this in real time is to create a learning system that takes those outliers and solicits human feedback on them,” Veeramachaneni explains.“The human alone can distinguish between malicious and benign, and that feedback returns to the system to create predictive models that can mimic human judgment — but at huge scale and in real time.” This is especially pertinent in Io T ecosystems, where large numbers of devices are involved, and the real-time analysis of the overwhelming amount of data generated are beyond human abilities.The same mechanics can be employed in security-related use cases, such as determining safe device behavior and general usage patterns, which can subsequently help to spot and block abnormal activity and potentially harmful behavior.
This idea was leveraged by startup tech company Dojo-Labs to create a smart-home Io T security solution.
“When it comes to Io T devices they were designed to do a very, very specific function,” says Yossi Atias, co-founder and CEO of the company.
The system generates events that indicate potential attacks.
The human investigates the events and determines whether the system was correct in its assessment or not.
“You gather all the traffic,” says Balan, “sanitize and normalize it, learn from it, see what servers the devices talk to, what other devices they talk to, how they normally interact with the Internet and with each other, and you pick up on the abnormal traffic.” Bitdefender uses cloud-based intelligence and pattern recognition, along with local network analysis through its suite of endpoint security software and hardware, to control Internet traffic in home networks and block connections to malicious URLs, malware downloads and suspicious packets.
Leveraging cloud services has enabled the company to bring enterprise-level intelligence and protection to the consumer space.
“Neither machine learning nor humans can do it alone,” he says.
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“To get security in the enterprise or in the Io T realm, you have to have powerful machines organizing data, crunching data, and seeking patterns in data.
But you also need the human’s intuition to spot new attacks and to train the system to stop these new (and old) attacks.” Veeramachaneni calls this combination “augmented intelligence,” an alternative for the acronym AI, which is where the strengths of both man and machine converge to defeat cyber threats.