How Machine Learning Is Set To Change The Face Of Cybersecurity
Machine learning (ML) is a branch of artificial intelligence (AI) in which computer programs learn from available data and experience, without needing explicit rule-based programming.
This technique works by building mathematical models derived from existing datasets to help computers make decisions or predictions, without much need for human assistance.
Seeing as the world of cybercrime is dynamic, ML is gaining popularity within the cybersecurity sector. Here are the main reasons:
- It helps security systems analyze and learn from threat patterns to help prevent similar attacks in the future, and be ready for rapidly evolving malicious codes.
- It helps create a proactive cybersecurity environment while ensuring threats are identified and neutralized in real-time.
- It reduces the amount of time, effort, and money needed to perform routine tasks by diversifying available resources strategically.
Keep reading to learn more about the important tasks performed using ML, and its role in modern-day antivirus technology
ML Most Common Tasks
In the field of cybersecurity, ML helps to carry out various tasks; including regression, classification, and clustering.
Regression, often called prediction, is a task that helps predict the upcoming value based on the available previous values.
Using ML, computers shuffle through existing datasets to predict variable datasets for the future.
This method is commonly used to forecast and find out the cause-and-effect relationship between independent variables.
This technique can change based on the number of available dependent and independent variables, and there are several types of regression task models. Here are the most common ones:
- Simple Linear Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Analysis
- Random Forest
Classification means segregating elements into different categories. In ML, computers are taught to assign class labels to variables based on their domain by using a predictive modeling problem. A simple example would be classifying emails as spam or not spam. It essentially requires a dataset with numerous examples of inputs and outputs that helps the computer learn.
All of these classification modeling algorithms are result-based and used as a standard metric to evaluate performances of any given ML model.
There are four core types of classification models in the field of machine learning:
- Binary Classification
- Multi-Class Classification
- Multi-Label Classification
- Imbalanced Classification
This is another Machine Learning technique involving the grouping of specific data points. The computer uses clustering algorithms on a set of pre-existing data points to further classify them into specific groups. It is an unsupervised learning methodology and is commonly used for statistical data analysis across varied sectors.
It usually draws references from a set of datasets that consist of input data missing labeled responses. Algorithms are then applied to create meaningful structures on the basis of similarities and dissimilarities.
Common clustering methods include:
- Mixture Model
- Gaussian Mixture Model
- Mean Shift
- K-Means, K-Nearest Neighbors
- Agglomerative Models
The New Generation Of Antivirus Technology: Endpoint Detection And Response
Endpoint Detection and Response (EDR) has been crowned the key element that will create the next generation of antivirus technology.
For this, it is essential to learn the features provided in the executable files or the behavior process.
However, it should also be noted that when dealing with Machine Learning at the endpoint layer, the solution might be different depending on the endpoint type.
That said, every endpoint solution might have its own specifications, but the tasks performed are common. Here is a quick overview of how EDR relates to common tasks of regression, classification, and clustering:
- Within a regression model, it helps in predicting the next action to be executed by the system while comparing it with real-time processes that are ongoing.
- With the help of a classification model, endpoint security can classify malicious elements into designated groups of malware, viruses, spyware, ransomware, and other similar categories.
- Finally, using the clustering model, it ensures protecting content from malware on secure email gateways by separating suspicious attachments and files.
As modern-day threats are evolving at an alarming rate, traditional signature-based detection may not be able to keep up with the pace. These days, cyberattacks are carried out using different targeted methods like social engineering, redirects, spam links, and more across desktops, mobiles, and IoT devices. Hence, to combat these threats, the need for endpoint security solutions that rely on ML is becoming a necessity.
Machine learning has proved to be a game-changer in the field of cybersecurity. When modern-day security solutions integrate ML, they help secure devices and networks by effectively making use of available data and predictive analysis - in real-time.
As more and more brands like Google, IBM, HubSpot, Yelp, Qubit, and others embrace this technology, it is increasingly important to learn more about it and continue to follow emerging developments in the ML field.