The word for 2016 is that cyber security will redirect its focus from detecting cyberattacks to actually predicting them and responding proactively.
That is certainly the focus of the new artificial intelligence security solution from PatternEx, which leverages Analyst Intuition to predict existing and emerging cyberattacks in real time with what the firm insists is 10 times better detection rates and 5 times fewer false positives.
San Jose, CA-based PatternEx recently launched its PatternEx Threat Prediction Platform, which creates “virtual security analysts” that mimic the intuition of human security analysts in real time and at scale.
The PatternEx Threat Prediction Platform reportedly detects ten times more threats with five times fewer false positives compared with approaches based on Machine Learning-Anomaly Detection technology. Using a new technology called “Active Contextual ModelingTM” or ACM, the product synthesizes analyst intuition into predictive models. These models, when deployed across global customers, can reportedly learn from each other and achieve a network effect in detecting attack patterns.
“The most frustrating thing in InfoSec is that the data to detect malicious behavior often already exists in enterprise infrastructures today,” notes Uday Veeramachaneni, PatternEx CEO and co-founder. “The human analysts can detect it, but analysts are difficult to hire and are not scalable. The only way to get real time detection is to be able to mimic those analysts using artificial intelligence based on ACM technology.”
The ACM technology transforms raw data into behaviors, and synthesizes analyst intuition into predictive models, Veeramahcaneni explains. The Platform then leverages these models to make real time predictions about specific threat vectors. The more attacks the system predicts, the more feedback it receives from the analysts, which in turn improves the accuracy of future predictions.
As the platform learns a predictive model from one customer environment, this knowledge can be transferred between enterprises to detect threats globally and converge on new attacks at faster speeds for all customers, commonly known as a network effect, Veeramachaneni explains.
The Prediction Platform solution includes a number of novel components combined into a single platform:
• A big data platform designed for large data volumes and real time response
• An ensemble of algorithms designed to detect rare behaviors with the goal of identifying new attacks
• A mechanism to obtain feedback from security analysts and continuously update models with the provided feedback.
• An active learning feedback loop that continuously improves detection rates over time
• A repository of threat intelligence that can be shared among enterprises
The Threat Prediction Platform is available as software on premise, in the cloud, or in a private cloud.
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