This big data discipline of artificial intelligence gives systems the freedom to automatically gain information and improve from experience without manual programming. Machine learning (ML)is literally just that – “letting the machine learn”.
The definition of machine learning is “the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as ‘training data’, in order to make predictions or decisions without being explicitly programmed to perform the task”.
It all started with a man named Arthur Samuel and a game of checkers
IBM employee Arthur Samuel (1901 – 1990) pioneered artificial intelligence and machine learning research. His inspiration came from the game of checkers and creating a learning program for the first IBM commercial computer, the IBM 701, so he can play against the machine as if it was a human opponent. According to Stanford, “games are convenient for artificial intelligence because it is easy to compare computer performance with that of people.”
Arthur Samuel continued winning against the computer, so he wrote a program to let the computer play against itself. The program collected data on its games and created a predictive analytics engine to improve its decision making. Once the computer started to gather data and experience, Samuel finally started losing (or winning – however you choose to look at it) and the program was a success!
We see machine learning in a variety of industries such as manufacturing, retail, healthcare, hospitality, financial services and energy. Gurucul Risk Analytics applies ML algorithms to its behavior analytics solution to detect anomalous activity based upon a change in behavioral patterns.
Machine Learning is a Branch of Artificial Intelligence
Machine learning differs from artificial intelligence in the sense that machines aren’t just expected to be taught how to act intelligently when performing a task; these machines must be able to learn on their own and make decisions without human supervision. The machines can look at data, figure out if a decision was wrong or right, and use that information to make better choices next time.
Categories of machine learning algorithms:
- Supervised algorithms build a mathematical model of a data set containing both inputs and desires outputs
- Unsupervised algorithms take a set of data that contains only inputs with little to no idea what the outputs should look like
- Reinforcement algorithms enables the machine to learn and train itself using trial and error from past experiences
Automated and iterative machine learning algorithms reveals patterns in big data, detects anomalies, and identifies structures that may be new and previously unknown. Therefore, when paired with statistical analysis, ML identifies relationships that may otherwise have gone undetected. All in all, it can surpass human capability and software engineering capability to make use of volumes of big data.
Gurucul Risk Analytics uses over 1,500 machine learning models
One of the reasons Gurucul Risk Analytics uses machine learning algorithms to detect and prevent anomalous behavior is because it is not rules-based. The excessive alerts that comes from rules creates too much data to sift through and lots of false positives. Not to mention, it focuses on known unknowns whereas an algorithm not based on rules enables us to find unknown unknowns. After all, humans cannot predict what future attacks will look like.
Fourteen of Gurucul’s most popular ML models were presented at the 2018 Black Hat USA conference. The models serve to detect and predict malicious activity such as compromised accounts, fraudulent activity, insider threats, money laundering, and more. Gurucul discusses these models in detail up on the company blog with interesting use cases provided.
Gurucul’s most popular machine learning models include:
- Outlier Categorical Model
- Link Analysis
- Feature Analysis
- Dimensionality Reduction
- Rare and Volume Based Analytics
- Clustering and K-Means
- Abnormal Powershell Command Execution
- Workflow Classification Regression Tree
- Domains Generated Algorithmically
- Linear Regression
- Entitlement Classification
- Predictive Flight Risk
- Email Fuzzy Logic
With machine learning, we’re moving beyond tedious rules and patterns to rule out bad actors. Gone are the days of having to sift through heaps of data – a massive waste of productivity when your precious human employees can be focusing on other tasks.
Let the machine learn and do the dirty work for you with a reliable behavior-based security analytics solution. Request a Gurucul Risk Analytics demo today!