How machine learning can improve security?
Overview
The cyber security landscape has been constantly evolving. Perhaps at a pace faster than imagination. One feature of the evolving landscape has been security threats. Novel threats have been giving organizations sleepless nights because of their dynamism and penetrative abilities. Organizations are struggling to find ways to manage threats while more breaches are reported. Obviously, traditional or existing systems have been proving inadequate. Machine learning can potentially help manage such threats more efficiently. Machine learning can help manage threats originating from new sources such as Internet of Things (IoT); monitor huge volumes of information exchanges; identify potential zero-day threats and analyze huge volumes of historical data which may be extremely difficult with traditional methods.
Described below are a few ways machine learning can help improve security.
IoT-related threats
Internet-enabled devices have been proliferating fast which, in a sense, also opens doors to new threat types. Many of these devices are vulnerable which can potentially lead to serious security issues. Monitoring and analyzing data from so many devices is not possible manually. Machine learning can analyze and identify abnormalities or deviations from the device data.
Information exchange threats
In organizations, colleagues tend to share a lot of data. Data exchanges and the systems facilitating the exchanges are vulnerable because there are scant resources to monitor or analyze. Machine learning can monitor and analyze information exchange. There is this limitation of over alertness and too many false positives, but it is believed that the problem will resolve eventually as the systems adapt.
Zero-day threats
Zero-day threats do not manifest until after a long time which can be gold mine for hackers. Unknown software vulnerabilities are identified and subject to planned attacks. Machine learning systems can analyze unmonitored data in the TOR networks and provide valuable inputs on not only plugging hitherto unknown loopholes but also preventing attacks.
Threat prediction based management
Machine learning systems can analyze historic data in your organization and provide unique insights. For that, organization security systems also need to integrate with the machine learning systems.
Conclusion
It is not feasible to counter emerging threats with traditional methods alone, however efficient. Machine learning – both supervised and unsupervised – needs to take stage. However, it must be complemented by traditional security systems in the form of integration. It also needs to be noted that machine learning is still being worked out.
