Last updated on December 13th, 2020 at 09:42 am
An in depth look at data mining from a cyber security perspective.
This post is a guest submission. Please see our Affiliate Disclosure & Notification for details.
The amount of raw data that companies and organizations have access to is astronomical, but this data can be beneficial when adequately collected. Endless volumes of unstructured data is essentially pointless as there is too much information to make sense of. But when properly structured and analyzed, data can help predict future outcomes and improve a company’s systems.
The process of organizing and analyzing massive sets of data is called data mining and it’s one of the essential areas that you will learn about when obtaining a masters degree in cybersecurity.
What is Data Mining?
Data mining is the process of structuring and analyzing raw sets of data to pull valuable insights into an organization or business. You can use the data to discover anomalies, patterns, rules, meanings, correlations, and help make predictions. You can then apply it in a variety of ways, including building artificial intelligence applications, like search algorithms, and maximizing data investments.
You can use many different data mining techniques, and each provides its own set of insights. By combing these techniques together, you can analyze data thoroughly and create a comprehensive set of actionable insights for an organization. The best way to learn all there is to know about data mining and the techniques is through a masters in cybersecurity course. If you’re interested in a career in data mining, then formal training and furthering your education with a masters in cybersecurity will set you on the right path.
1. Data Preparation and Cleaning
The first and most important technique you will learn on a masters in cybersecurity program for data mining is the preparation and cleaning of the data. Since you’ll be working with raw data, it’s essential to cleanse and format it so that it can be analyzed.
Without structure, the data is chaotic and incomprehensible. The cleansing also involves condensing the data, removing pieces of information that are irrelevant. Data cleaning and preparation involves elements of data migration, transformation, data modeling, ETL (extract, transform, load), ELT (extract, load, transform), aggregation, and data integration.
High-quality, cleansed data is essential to build business growth strategies; therefore, this technique is highly important and why you will focus on developing this during your masters in cybersecurity. Organizations and companies must be able to trust the data and analytics attained from it. Unless data is formatted correctly, any insights are likely to be incomplete or inaccurate.
The data mining technique of classification is the process of analyzing a variety of attributes linked to each piece of data. Once you identify the primary characteristics of the data, you separate it into classes. By categorizing data by class, it becomes easier to locate, and organizations can find precisely what they are looking for. Classification is a more substantial way of categorizing the data, and then the organization can break it down into smaller categories to suit their needs. You can learn more about this on a masters in cybersecurity.
The clustering technique is similar to classification but with a few significant differences. Where in classification, the data is segregated into classes, clustering it is separated into segments. Clustering is a more visual form of data mining. It allows you to analyze the data in the form of a graphic, which identifies how data is distributed based on a set of metrics. For each organization, the metrics that you calculate will vary based on what is beneficial information.
Typically, clustering is presented in a graph or pie chart with different colors to help make the analysis comprehensive. Clustering makes it easy to visually identify trends within the data that are valuable to the organization.
4. Tracking Patterns
Tracking patterns is the most useful data mining technique for predicting future outcomes and is therefore extremely useful to organizations and why it is essential on a masters in cybersecurity. It entails first identifying patterns with the data and then monitoring the trends to see if they change. Using this information, you can make educated inferences about the future of your business and make decisions based on that.
An excellent example of how you can use the tracking patterns technique to your advantage is regarding product sales. You can track which products are the best sellers and what demographic of people are buying them. You can then use this information to tailor what they sell and aim to sell more products of similar variety. On the flip side, you can track products that aren’t selling and faze them out from your business. Identifying demographics is helpful as well since you can use this information in your marketing efforts.
The tracking patterns technique is one of the most beneficial data mining techniques available. Being able to predict outcomes within your business will allow you to eliminate negative ones and boost positive ones. Therefore, you should consider applying for a masters in cybersecurity to develop this skill.
5. Sequential Patterns
Another data mining technique involving the use of patterns is analyzing sequential patterns. It varies from tracking patterns because the focus is on a series of events that occur in a sequence, rather than just individual events that occur frequently. It’s most useful when analyzing data related to transactions. By analyzing consumer behaviors, you can identify what they are most likely to purchase and in what order. Once a customer buys a few products, you can analyze the order in which they made their purchases. Using the technique of the sequential pattern, you can then mine the data to see if other customers also make purchases in the same order.
The ability to predict your customers’ actions and identify what they are most likely to purchase next will allow you to optimize your sales strategy. For example, if you know that many customers will come back to buy a pack of ankle socks after they purchase sneakers, you can market socks directly to those who have bought sneakers. The likelihood they will buy them is much higher since you know there’s a sequential pattern there. Using this technique can directly lead to increased sales and so is a crucial area to learn on a masters in cybersecurity.
Where sequential patterns involve actions taking place one after another, the association technique is finding actions that take place together. The data mining technique of association looks at statistics to identify pieces of data that directly link to other pieces of data. When one data-driven action occurs, so does another. By identifying data associations, you can denounce when an occurrence likely takes place based on the presence of specific pieces of data.
Within a data collection, the association will help you to discover the likelihood of a co-occurrence. It’s a beneficial technique that you’ll primarily see in transactional data and medical data analysis. An example of association, using a similar scenario to the above, is if customers who purchase sneakers also purchase socks at the same time. When some purchases both in one transaction, it is the association, whereas when a customer purchases socks after they are buying sneakers, that is a sequential pattern. In this scenario, this information is useful for the marketing and sales team. They could use marketing prompts at the checkout, encouraging people to add on socks to their purchase order. They could also use a feature such as a “people also bought” section on the same page as the sneakers to encourage buyers to add them to their cart.
The data mining technique regression is useful for identifying variables within the collected data. It is also called the white box technique – something your tutor will explain to you when you take a masters in cybersecurity. Regression looks at the relationship between a set of given variables to see how they relate to each other. There are many different types of regression, but they all similarly examine data. They look at how an independent variable influences a dependant variable.
For example, a dependant variable would be your company’s yearly sales. If you were going to try to predict your annual sales, many independent variables would influence that outcome. Identifying these independent variables and analyzing how they affect the dependant variable is the basis of regression.
Some of the types of regression you may encounter in data mining include:
- Linear regression
- Logistic regression
- Polynomial regression
- Quantile regression
- Lasso regression
- Ridge regression
- Principal components regression
- Elastic net regression
- Support vector regression
- Partial least squares regression
The above list is just a portion of the full spectrum of regression types, and the most effective type depends on the company, industry, and the metrics you’re trying to analyze.
8. Decision Trees
Another important area that a masters in cybersecurity will cover is decision trees. The decision tree technique allows companies to mine data effectively by using a predictive model. A decision tree gives the company a comprehensive understanding of how the way that you input data can affect the output. The output relates to the information you can gain from the data and the findings from the analysis. A single decision tree model is generally straightforward and easy to understand. You can analyze one form of data input and see precisely how it impacts the output. When you combine multiple decision tree models for predictive analysis, it is known as a random forest.
A random forest is much more complex to analyze. It’s not as straightforward to decern outputs based on inputs due to the sheer volume of data. That said, because there is more data involved, it is more accurate than the single decision tree. The random forest technique is also referred to as a black box machine learning technique, where the decision tree technique is considered a white box. This technique is most beneficial in machine learning.
9. Statistical Techniques
Statistical techniques are often the most accurate of all data mining techniques, which is why they are so important to get right early on, ideally, when you are completing your masters in cybersecurity. Where all other data mining techniques focus on the data itself, both previous and current, statistical data focuses on mathematics. The creation of all analytics models relies on statistical concepts. They will pull out any numerical values from your raw data relative to your company’s objectives. The statistical analysis technique revolves around working with formulas and probabilities to build models, which are a central part of artificial intelligence. Some of these models may be static, while those relating to machine learning will continue to adapt.
10. Outlier Detection
The outlier detection technique identifies any inconsistencies within a database. You can then analyze the pieces of irregular data to get a grasp on how and why they happened in the first place. By developing an understanding of your raw data anomalies on a masters in cybersecurity, you can prepare for when they happen again in the future and use it to your advantage. By forecasting when strange spikes or dips occur in different areas of your business, you can adapt your business strategy accordingly.
An example of this would be if outlier detection identifies a drop in sales during a specific week of the year, you can tailor your marketing efforts to run a sale during that week. Or another example would be if you notice a high volume of traffic to your website at a time of day. If you can work out why this is happening, you can use that information to gain traffic at other times that aren’t as popular.
Data visualization is the most accessible and dynamic form of data mining that you can learn on a masters in cybersecurity course. Visualization allows you to see insights presented visually and in a way that is easier to understand. With today’s technology, visualizations can be anything from a simple graph to dynamic and engaging animations. This technique allows you to live-stream data so you can check new insights by the minute. Trends and patterns are presented simply, and metrics are accessible to everyone.
There are situations where visualization is more appropriate, while there is something to be said for the intricacy and accuracy of statistical models. When you’re presenting data to an organization or to those who aren’t necessarily expert data analysts than visualization is an excellent technique to use. Some examples of data visualization tools that you can access include Microsoft Excel, Google Charts, Tableau, Infogram, and more.
The success of a business or organization depends on data and how they use it. There is so much beneficial information to gain by adequately structuring and analyzing the accessible banks of raw data. By using the right data mining techniques and expanding your education with a masters in cybersecurity, any business can create strategies to skyrocket their success. There are many different data mining techniques available, and this list is only the beginning. As technology continues to develop and grow, there are new ways to analyze data always popping up.
While using these techniques on their own will still be helpful, combing them together is where you will really see the benefits. Using a combination of all the data mining techniques and a masters in cybersecurity will allow you to develop thorough and accurate insights that you can use for dramatic results.
Subscribe to Our Mailing List
If you found the information in this post helpful, we'd love to have you join our mailing list. We promise we won't spam you, we only send out emails once a month or less.