Machine learning (ML) is a technique that allows computers to learn without having to be explicitly programmed. To put it another way, machine learning teaches computers to do what humans do naturally: learn through trial and error. Machine learning is a subfield of artificial intelligence, which is a broader field that includes many other subfields.
In the field of security, machine learning continuously learns by analysing data to uncover patterns, allowing us to better detect malware in encrypted traffic, identify insider threats, predict where “bad neighbourhoods” are online to keep people safe while browsing, and protect data in the cloud by uncovering suspicious user behaviour to name a few applications.
How does machine learning work in security?
As a result of the evolving cyber threat landscape, organisations are required to continuously track and correlate millions of external and internal data points from across their infrastructure and users. It is simply not possible to manage this volume of information with only a small group of people on the ground level.
When it comes to machine learning, it really shines because it can recognise patterns and predict threats in massive data sets while operating at the speed of a computer. By automating the analysis, cyber teams can detect threats more quickly and isolate situations that require further human investigation and investigation.
How does it work?
Non-data scientists may find the details of machine learning to be intimidating, so let’s take a look at some of the key terms.
Supervised learning makes use of sets of training data, referred to as “ground truth,” which are pairs of correct question-and-answer pairs, to aid in the learning process. This training aids classifiers, which are the workhorses of machine learning analysis, in their efforts to accurately categorise data. It also aids algorithms that are used to organise and orient classifiers in the real world in their successful analysis of new information. An everyday example is the ability to recognise people in online photos: To correctly identify a unique face among the many millions of online photos, classifiers examine the data patterns that they have been trained on, rather than the actual noses and eyes of the subjects.
How machine learning helps security…
Identify potential threats on a network.
Detecting threats with machine learning involves constantly monitoring the behaviour of the network for anomalous patterns. Machine learning engines process massive amounts of data in near real time to detect critical incidents, which are then reported to authorities. This type of technology allows for the detection of insider threats as well as unknown malware and policy violations.
Keep people safe while they are browsing the web.
Machine learning can be used to predict “bad neighbourhoods” on the internet, which can help prevent people from visiting malicious websites. In order to automatically identify attack infrastructures staged for current and emerging threats, machine learning is used to analyse Internet activity.
Endpoint malware protection should be provided.
Algorithms are capable of detecting malware that has never been seen before and is attempting to run on endpoints. It identifies new malicious files and activity based on the characteristics and behaviours of previously identified malicious files and activity.
Keeping your data safe in the cloud
When used to analyse suspicious cloud app login activity, detect location-based anomalies, and conduct IP reputation analysis in order to identify threats and risks in cloud apps and platforms, machine learning can help protect productivity.
Malware in encrypted traffic can be identified.
In encrypted traffic, machine learning can detect malware by analysing the encrypted traffic data elements that are present in common network telemetry. As an alternative to decryption, machine learning algorithms identify malicious patterns in order to detect threats that have been hidden by encryption.
