Practical Cybersecurity Data Science: Essential Knowledge and Tools

An introduction to cybersecurity

Protecting home and business computers from data breaches requires the implementation of cybersecurity protocols. Protocols can be as simple as two-factor authentication, but some require more complex tools and technical knowledge. These tools and processes help collect data, which is analyzed to better understand cybersecurity needs. 

Users and administrators need to stay informed on the latest advancements in cybersecurity software and strategies. This guide is an overview of the essential knowledge and tools needed to improve cybersecurity protocol without incurring significant costs. 

Practical cybersecurity data science 

Cybersecurity experts are trained to collect and analyze data to improve strategy, but an advanced degree isn’t necessary to test and bolster cybersecurity. The average user can benefit from using data science tools. Here are a few data science tools to explore and use: 

Setting up a virtual lab for cybersecurity data science

A virtual lab allows data to be collected, stored and analyzed without the need for a lot of expensive hardware. A computer can run a hypervisor like VMWare ESX(i) to create a virtual environment with multiple hosts, provided the CPU meets the basic requirements. 

Alternatively, there are various free, open-source proxy servers like PfSense or Squid Proxy that function as virtual labs. With the right tools, anyone can configure a virtual test lab for data analysis.

Working with virtual Python environments

When working with Python projects, a virtual Python environment is a great way to keep projects organized and distinct. In order to set up a virtual Python environment, a new directory will need to be created, as well as a new virtual environment within this directory. The virtual environment is activated following the environment setup. Once active, site packages will be installed and managed locally, giving more freedom and security with Python projects.

Basic model assessment

Assessment of the accuracy and efficacy of a model requires the procedurally correct collection and analysis of data. In addition to evaluating basic functions (query language, probability, regularization), tests of the model are run. 

Starting with error testing is the preferred workflow, but there are various evaluation methods available. Validation testing is a good next step. K-fold cross-validation is especially useful when there is a limited sample size. 

Structured learning with XGBoost

XGBoost (Extreme Gradient Boosting) is a popular algorithm for applied machine learning. XGBoost features gradient-boosted decision trees that can make predictions using training data. XGBoost takes a predetermined objective function then optimizes it. 

Whether a simple or highly complex tree is required, XGBoost is one of the most intuitive ways to develop and analyze a structured learning model.

Natural Language Processing (NLP)

As the complexity of human-machine interactions advance, the way humans communicate with computers changes. In the past, communication with computers required coded computer language. These days, virtual assistants facilitate communication with computers using human language. As a result, encoding human languages now plays a vital role in cybersecurity. 

Natural Language Processing (NLP) also offers security methods like obfuscation and encryption, enabling greater security when sending and receiving data.

Anomaly detection with Isolation Forest

Inaccurate and unhelpful conclusions may be drawn when anomalies are not taken into account. Thankfully, the Isolation Forest technique was specifically designed to identify outliers and anomalies in a given data set. 

Isolation Forest decision trees differentiate between data points that lie closer to the root of the trees and data points that are further away. Detecting anomalies is useful in a variety of ways, including preventing data breaches and isolating malware in a network.

Hyperparameter tuning

A hyperparameter is simply a parameter that is determined prior to the start of the learning process. Tuning hyperparameters determines the best hyperparameters for a particular learning algorithm. The traditional tuning method is a grid search which searches through a given subset of hyperparameters, increasing the cross-validation score. 

Generating text using machine learning

A recurrent neural network easily generates text using machine learning. Application Programing Interfaces (API), like Keras, are used to experiment with different models while developing a process that meets specific requirements. Generating automated text with preset algorithms saves time and allows for the employment of obfuscation and encryption, if needed.

Conclusion

Cybersecurity data science protocols seek vulnerability reduction but there is no one-size-fits-all solution. Developing protocols that make sense for specific data, software and networks requires a thorough understanding of that system’s cybersecurity needs. That understanding is achieved through analysis by data science tools like virtual labs, models and anomaly testers like Isolation Forest. 

Following analysis, cybersecurity experts create a comprehensive plan to improve strategy and streamline security processes that best protect data, networks and software.

Sources

Python Virtual Environments: A Primer, Real Python

Keras: The Python Deep Learning Library, Keras

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Matthew
Jones

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Section Guide

Matthew
Jones

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