Let’s learn about cybersecurity data science, and how it applies in our new normal today? Also, let’s see the structure of data science.
The Cybersecurity Data Science
Data science is the study of the computational process to obtain insight from data. Also, data science is often broken down into three sub-fields:
- data mining
- predictive modeling, and
- descriptive modeling.
So what are these three subs-fields? And how does it connect to cybersecurity?
Let’s find out.
Data Mining is the process of analyzing data with the goal of the following
- extracting patterns
- correlations, and
Data mining is often applied to large volumes of data to predict future outcomes. It is used in cybersecurity to detect abnormal behavior or detect intrusions into computer systems.
Predictive modeling uses statistical methods to model future events. It can be used to predict when equipment will break down in the following:
- when customers will make purchases
- products will fail, and
- when fraud will occur.
It is often used in cybersecurity to predict how likely it is for an attack to occur or predict the likelihood of a cyber-attack.
Descriptive modeling produces detailed information about past events. It can be used in cybersecurity to analyze past attacks in order to prevent future attacks. But only after data has been collected.
It is a type of modeling that doesn’t produce predictions about the future; instead, it provides explanations for past actions or events that occurred in the past.
Cybersecurity Data Science Endpoint Detection & Response
Cybersecurity Data Science Endpoint Detection & Response or EDR is a new form of cybersecurity defense. Moreover, it uses data science techniques to detect malware on endpoints far earlier than traditional security solutions
EDR takes advantage of the following:
- artificial intelligence
- machine learning
- user-behavior analytics and other technologies
Moreover, they were once only available in the enterprise network perimeter at the access layer and below. But are now available at the endpoint level due to advances in endpoint security platforms.
Its advantage allows EDR to detect malware earlier than traditional security solutions. Why? Because it can analyze all endpoint data sources.
including the following:
- registry items or any other endpoint resources
So that can be analyzed by artificial intelligence algorithms. EDR deploys this advanced detection capability across all endpoints. Also inside an enterprise network at a fraction of the cost of traditional perimeter security solutions.
Such as the following:
- intrusion prevention systems (IPS)
- anti-virus software and gateways.
Cybersecurity Data Science Machine Learning
Machine learning is a subset of data science. So that uses statistical techniques to build predictive models.
It is used in cybersecurity to enable the following:
Detecting and preventing cyber-attacks.
It detects and prevents cyber-attacks using machine learning algorithms. These algorithms learn from previous cyber-attacks and use the knowledge to detect and prevent future attacks.
Predict future cyber-attacks. It predicts future cyber-attacks by using historical data from past cyber-attacks. The historical data is analyzed to determine how it was performed, by whom, when it occurred, what was done, how it was done, etc.
Also, machine learning algorithms analyze this information. To determine likely attack techniques.