Alexandru Balan Thesis

Pattern Ex uses machine learning algorithms to do outlier detection, and trains the model to be more accurate in real time.

As with all solutions involving machine learning, Dojo-Labs’ model improves as it collects more and more data from customers.Basically, it’s like going to the battlefield without armor.That’s why new Io T vulnerabilities are constantly surfacing, and countless Io T devices are falling victim to hacks, botnets and other evil deeds every day.“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.“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.“Machine learning is a critical component to developing Artificial Intelligence for Io T security,” says Uday Veeramachaneni, co-founder and CEO at Pattern Ex.“The problem is that the Io T’s will be distributed massively and if there is an attack you have to react in real-time.” Most systems relying on machine learning and behavior analysis will gather information about the network and connected devices and subsequently seek everything that is out of normal.The solution includes a pebble-like device that gets installed in the home network, a mobile app that allows the user to control the device and monitor the network status and a cloud service where the data is consolidated and analyzed using proprietary statistical tech and mathematical models coupled with machine learning algorithms.Machine learning is very promising, but it is still in its infancy and has a long way to go.

Leave a Reply

Your email address will not be published. Required fields are marked *

One thought on “Alexandru Balan Thesis”