Boosting enterprise security with deep learning
Posted on November 14, 2019 by Noble
Most enterprises still rely on traditional approaches to network security to defend against threats. This approach relies on feeding historical data – i.e anomalous activity that was suspicious or malicious – into a learning algorithm so the system knows what to look out for in the future. This enables the system to flag suspicious activity that corresponds to historical data to security teams, and prevent such attacks slipping through the net.
However, this approach is no longer adequate in today’s evolving threat landscape, because it hinders an organisation’s ability to investigate activity that hasn’t been seen before, causing them to miss new attacks. Furthermore, behaviour that is deemed “normal” or “good” within an organisation is constantly evolving, and businesses have to be able to adapt in real time. This legacy approach to network monitoring also places additional stress and burden on security analysts, who don’t have the capacity to sift through the vast amounts of data collected by businesses and identify threats. It’s no surprise that 56 per cent of senior executives think their cybersecurity analysts are overwhelmed by the sheer volume of data points they need to analyse to detect and prevent threats.
The result? Businesses that can’t identify new and sophisticated attacks, and attackers who are spending an average of 6 months within a network. Clearly, when it comes to enterprise anomaly detection, a change is needed.
Find out what it is in our latest article for ITProPortal, or get in touch today!