Data Mining and Cyber Security 101: Key Relationships Simplified

With billions of people scouring the internet every day, it has become harder than ever to find relevant and accurate information as we endlessly consume, create, and copy data. In 2021, the entire global population went through 74 zettabytes of data, and while you may be thinking, “that doesn’t sound like a lot,” what if I were to tell you that was trillions of gigabytes of data? Luckily, we can incorporate techniques such as data mining to help us sort through the data so that we may better organize it and use those techniques to improve our cybersecurity. 

By implementing Data Mining and Cyber Security, your security logs and databases can improve your detection of malware, network or system intrusions, and insider attacks along with many other security threats, with a few techniques even able to predict attacks accurately and pick up on zero-day threats. 

In the article below we will be discussing how a cybersecurity software team can utilize Data mining and Cyber security to improve a company’s network and endpoint security. 

Table of Contents

• What is Data Mining?
  • Key Features of Data Mining
• Implementing Data Mining and Cyber Security
• 6 Important Key Data Mining Techniques
  • Classification
  • Regression Analysis
  • Time Series Analysis
  • Association Rules Analysis
  • Clustering
  • Summarization
• Examples for Data Mining in Cyber Security
• Conclusion

What is Data Mining?

The process of examining large datasets to find patterns, correlations, and anomalies is known as data mining. These datasets include information from personnel databases, financial information, vendor lists, client databases, network traffic, and customer accounts, among other things.

The Data mining process begins with determining the business goal that will be achieved with the data. The collected data is then loaded into Data Warehouses, which serve as analytical data repositories. Sanitization of data also includes the addition of missing data and the removal of duplicates.

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Key Features of Data Mining

The following characteristics are associated with data mining:

• Databases and large data sets
• Prediction of the Likely Outcome.
• Recognition of Patterns Predictions is made using behavior analysis.
• To compute a feature from other features, any SQL phrase can be used.

Implementing Data Mining and Cyber Security

Data mining is often found to be used in scientific research, customer relations, and business development with those professions using the technique to analyze information, predict future trends and discover new patterns of data. 

Data Mining is one of the steps in a process known as Knowledge Discovery in Databases (KDD), however many people treat it as a synonym for KDD instead. The main use of KDD is to acquire information that is useful or previously unknown from a large set of data. There are 4 steps in the entire KDD process:

• Pre-processing
• Transformation
• Mining
• Pattern Evaluation

By combining data mining and cyber security we can better determine what the cyber-attack will be as well as improve the attack detection process.

Data mining can quickly help you analyze extremely large datasets to automatically find hidden patterns, which is a crucial part of creating an effective anti-malware application that can successfully detect previously unknown threats. However, the quality of the data you use will greatly affect the results of your data mining methods. 

Pros

• Useful insight from pre-existing data
• Identify security flaws and blind spots
• Detect zero-day attacks
• Detect intricate and masked attack patterns

Cons

• Requires specialized deep data science expertise
• Preparation for data mining takes time and effort
• Constantly updating the classifiers and mining techniques
• Risk of leaking sensitive information from databases
• Requires manual verification of data mining results

While these are general pros and cons associated with data mining and cyber security, each technique has its own limitations, specific use cases, and advantages. 

6 Important Key Data Mining Techniques

There are two ways that you can mine databases, predictive or descriptive techniques, and each is split into 3 different techniques. With prescriptive techniques, you can predict data based on past events, and use descriptive techniques to focus on the analysis and structure of the existing database. 

• Classification
• Regression Analysis
• Time Series Analysis
• Association Rules Analysis
• Clustering
• Summarization

1) Classification

Classification allows you to split your large dataset up into different predefined concepts, classes, and variables. By doing this you can analyze variables that have been added to the database after you have built your model and group them into their corresponding classes. To get accurate real-time classifications, you need to put a lot of time into supervised training of your algorithm and make sure to test how it works.

2) Regression Analysis

Regression analysis is when you create an algorithm that predicts the change in the value of variables based on the average of values in other variables found in a dataset. With this technique, a relationship between independent and dependent variables, in a database, is built. These changes between variables in the datasets can be compared with dependent variables to compare and identify changes as well as the influence one variable has on another. This technique is mainly used to forecast trends or events, including but not limited to cyber-attacks.  

3) Time Series Analysis

These techniques and algorithms use the analysis from the time of data entry changes in the database to discover and predict time-based patterns. You can use this technique to predict security attacks that would happen during an event, time of day, or season, making it easier to get insights into periodic activities by mining a database containing multi-year information.

4) Association Rules Analysis

By using the widespread groups found in this data mining algorithm, it can be a useful tool in finding relations between different variables that appear together in the dataset, thus discovering hidden patterns. It can be used to predict, or analyze user behavior, defines patterns of cyber-attacks, and examine your network traffic which all help Cybersecurity officers to study an attacker’s way of thinking and behavior. 

5) Clustering

While clustering is similar to classification, in most aspects, clustering cannot process new variables in real-time. This technique can, however, be used to structure and analyze an existing database while identifying data items that possess common characteristics as well as understanding the similarities or differences in variables. This allows you to make changes to the model, and create any subclusters, without needing to redo the algorithms. 

6) Summarization

With this technique mostly being used to generate reports and logs by Security Officers, the main focus of this technique is to compile a brief description of datasets, clusters, and classes. This will help you understand what is contained in your dataset and any results of the data mining process by collecting the information and eliminating the need to go through that data manually.

Each technique listed above can also be enhanced by using Machine Learning or Artificial Intelligence in any of the algorithms; however, by adding these advanced technologies, you will potentially increase the complexity of your algorithms, though they will allow you to discover more hidden patterns and improve on the accuracy of your predictions. 

Examples for Data Mining in Cybersecurity

Data Mining and Cyber Security are flexible as simply by adjusting your technique to your dataset, it’s no wonder that data mining can be incredibly useful in cybersecurity; by detecting unusual data records or events that could indicate a security risk. 

Below are a few common examples of how Data Mining and Cyber Security can be used:

1) Malware Detection 

Malware has become a fierce threat to the computer world. To fight against malware, companies have designed techniques that help in weakening the malware from attacking systems. The most commonly used techniques include signature-based and behavior-based detection methods. However, they all have their drawbacks; signature-based methods have failed to spot new and unknown malware., while behavior-based techniques have yielded numerous false positives when attempting to detect unknown malware.

When developing security software, Data mining and Cyber security have been used to enhance the speed and quality of malware detection and detect zero-day attacks.

There are 3 methods for detecting malware in Data mining and Cyber security:

• Anomaly detection implies modeling a system’s expected behavior to recognize deviations from standard activity patterns. Anomaly-based techniques can detect even previously unknown attacks. However, anomaly detection can notify even genuine activity if it shifts from the norm, thus producing false-positive notifications.
• Misuse detection, also known as signature-based detection, can only recognize known attacks that have been established on examples of their signatures. While this method has a lower rate of false positives, its main disadvantage is the inability to detect zero-day attacks.
• The hybrid approach mixes anomaly and misuse detection techniques to improve the number of detected intrusions while lowering the number of false positives.

Regardless of the chosen strategy, the development of a malware detection system is a two steps process:

• Extracting Malware features in Data mining and Cyber security
• Classifying and Clustering in Data mining and Cyber security

First, the data mining algorithm draws malware features from various records and events as a way to extract malware features from potentially unsafe files.

During the classification and clustering step, by employing corresponding techniques file samples can be divided into groups based on feature analysis. With the help of a classifier, you will be able to catch even recently released malware.

2) Intrusion Detection

Data mining and Cyber security can also be effectively used to detect intrusions and analyze audit results to spot abnormal patterns.

Malicious intrusions comprise attacks on an organization’s networks, databases, servers, web clients, and operating systems. 

There are three types of attacks that are typically caught by Intrusion detection systems:

• Scanning attacks
• Denial of service (DOS) attacks
• Penetration attacks

To be able to detect o detect break-ins or break-in attempts into a computer system or network, cybersecurity software has to analyze features extracted from programs. Detecting network-based attacks require a solution capable of analyzing network traffic; just like with malware detection, data mining can help with regular and irregular behavior or cases of misuse. Data mining is, at its core, pattern finding.

Intrusion detection systems rely on classification, clustering, and association rules methods. These techniques allow for extracting attack features, classifying them, and flagging all new records that have the same features. 

In spite of several detection algorithms available such as regression and decision trees, bayesian networks, k-nearest neighbors, learning automata, and hierarchical clustering, there is a shortage of proper mechanisms that can accurately check real-time datasets generated dynamically.

Intrusion detection systems combine Data mining and Cyber security methods, methodologies, and algorithms to add prediction capabilities and detect the intrusion dynamically.

3) Fraud Detection

The global fraud detection and prevention market size is expected to grow from $26.99 billion in 2021 to $33.51 billion by the end of 2022, and it is predicted to surpass $81 billion by 2026.

The fraud detection and prevention market classification is categorized by fraud type into:

• Check fraud
• Identity fraud 
• Internet /Online fraud 
• Investment fraud
• Payment fraud
• Insurance fraud

Fraud detection is a problem of increased difficulty as fraudsters do their best to make their behavior appear legitimate. This issue can be solved by using supervised and unsupervised ML algorithms.

Supervised learning splits all records into two distinct types: fraudulent and non-fraudulent. The main disadvantage of this approach is its incapacity to detect new types of attacks for Data mining and Cyber security. 

Unsupervised Machine Learning algorithms, such as cluster analysis and peer group analysis, analyze data without any identified fraud and indicate new anomalies and interest patterns.

Conclusion

We all know that it is physically impossible to manually gather all the data that organizations generate on a day-to-day basis, which is why Data mining and Cyber security are a vital part of combating cyber threats. 

By using the choice of techniques above, you can identify any malicious activity and predict possible attacks. They are also great for gathering threat intelligence and detecting malware, fraud, insider attacks, and intrusions. 

To become more efficient in handling your Data Mining and Cyber Security, it is preferable to integrate them with a solution that can carry out Data Integration and Management procedures for you without much ado and that is where Hevo Data, a Cloud-based ETL Tool, comes in. Hevo Data supports 100+ Data Sources and helps you transfer your data from these sources to Data Warehouses in a matter of minutes, all without writing any code!

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