An Introduction To Data Science For Cybersecurity

An Introduction To Data Science For Cybersecurity As a data science enthusiast who works in cybersecurity, I frequently get questioned about how two fields effectively complement one another. When used properly, data science can be a potent tool in cybersecurity. Additionally, effective implementation frequently necessitates a careful balancing of the appropriate individuals, procedures, and technology.

In the context of cybersecurity, I will discuss a few key principles here.

An Efficient Data Science Team For Cybersecurity A nice place to begin today's discussion is with a Venn diagram of data science made by American data scientist Drew Conway in 2010. His three key components were Substantive Experience, Math & Statistical Knowledge, and Hacking (in this case, computer science skills). Data Science is the confluence of these three concepts. Traditional Research is found at the intersection of Math & Statistical Knowledge and Substantive Experience, ML is found at the intersection of Hacking and Math & Statistical Knowledge, and the "Danger Zone" is found at the junction of Substantive Experience and Hacking Skills.

I think it takes six "personas" to build this kind of well-rounded, efficient team in cybersecurity. You need a coder who can manage the data, parse the records, and write code; a visualizer who creates understandable visualizations for trends and patterns; a modeler who converts words into statistics and math; a storyteller who can connect the data to the models to the results to the threats, effectively transferring understanding from the SOC analyst to the board; a hacker who lives and breathes cybersecurity; and a historian who can bring subject matter expertise like threat hunting or foresight.

Artificial Intelligence Vs. Human Intelligence Let's discuss AI in terms of a system diagram, which everyone can grasp. Sensing and perceiving the world around us is one way we show our intellect. We perceive items through sight, sound, and touch. Those "inputs" are all processed in different ways. We make decisions and inferences based on it, and we learn things based on the things we observe and sense. It both informs and is informed by our knowledge and memories. Our final acts or interactions with the environment around us will be the output of these processing functions.

A similar system diagram can be used to represent artificial intelligence. The "input" can be pictured as speech recognition, natural language processing, etc. In the context of cybersecurity, "output" can take the form of robotics, navigational systems, speech production, or the detection of security risks that may be lurking inside your company.

Research in knowledge representation, ontologies, prescriptive analytics and optimization, and machine learning is situated in the middle. A machine can learn in one of two general ways: ● Supervised learning (learning by example)

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Unsupervised learning (learning by observation)

Refer to the machine learning course in Mumbai for a detailed explanation of supervised and unsupervised learning.

Find the problem first, then the solution.

Any seller who uses the algorithm as their main selling point ought to provoke some skepticism. Starting with the use case is the most efficient way to create a cybersecurity data science solution. Understanding the use case(s) can help you select the data sources that are most pertinent to that use case and are readily available. Keep in mind that no algorithm will be useful without data. Better data is more important than "better" algorithms.

Data Science For Cybersecurity In Action Take a look at a use case I'm extremely familiar with using all of these components together: Interset's usage of anomaly detection with unsupervised machine learning. We discovered a use case for automatically and swiftly identifying serious threats five years ago, which remains important today. We sought a solution that would outperform the conventional strategy of rules, thresholds, and warnings. Because applying a single criterion or rule that is accurate for all users is hard, the conventional technique is manual, time-consuming, and inefficient. On the other hand, anomaly detection enables us to baseline everyone—every person, IP address, device, etc.

The mathematical architecture that underlies this anomaly detection represents the entire flow, including the set of input data sources (such as repository logs or Active Directory logs), the features or columns that are extracted from the data (such as the quantity of data moved or the combination of file shares being accessed), and the models that are run on the data (such as volumetric models that look for unusual volumes of data moved or file access models that look for unusual file sha values) (resulting in a forced ranking to find those high-quality leads).

Are you interested in learning more about how data-driven decisions help multiple industries.

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