Anomaly Detection: Guide to Prevent Network Intrusions

Data is an indispensable part of businesses and organizations, and it’s only valuable when structured properly and managed efficiently. 

According to a statistics, 95% of businesses today find managing and structuring unstructured data a problem. 

This is where data mining comes in. It’s the process of discovering, analyzing, and extracting meaningful patterns and valuable information from large sets of unstructured data.

Companies use software to identify patterns in large data batches to learn more about their customers and target audience and develop business and marketing strategies to improve sales and reduce costs. 

Besides this benefit, fraud and anomaly detection are the most important applications of data mining. 

This article explains anomaly detection and further explores how it can help prevent data breaches and network intrusions to ensure data security.

What is Anomaly Detection and Its Types?

While data mining involves finding patterns, correlations, and trends that link together, it’s a great way to find anomalies or outlier data points within the network. 

Anomalies in data mining are data points that differ from other data points in the dataset and deviate from the dataset’s normal behavior pattern. 

Anomalies can be classified into distinct types and categories, including:

• Changes in Events: Refer to sudden or systematic changes from the previous normal behavior. 
• Outliers: Small anomalous patterns appearing in a non-systematic way in data collection. These can be further classified into global, contextual, and collective outliers. 
• Drifts: Gradual, undirectional, and long-term change in the data set. 

Thus, anomaly detection is a data processing technique highly useful for detecting fraudulent transactions, handling case studies with high-class imbalance, and disease detection to build robust data science models. 

For instance, a company may want to analyze its cash flow to find abnormal or recurring transactions to an unknown bank account to detect fraud and conduct further investigation.

Benefits of Anomaly Detection

User behavior anomaly detection helps strengthen security systems and makes them more precise and accurate. 

It analyzes and makes sense of varied information that security systems provide to identify threats and potential risks within the network. 

Here are the advantages of anomaly detection for companies:

• Real-time detection of cybersecurity threats and data breaches as its artificial intelligence (AI) algorithms constantly scan your data to find unusual behavior. 
• It makes tracking anomalous activities and patterns quicker and easier than manual anomaly detection, reducing the labor and time required to resolve threats. 
• Minimizes operational risks by identifying operational errors, such as sudden performance drops, before they even occur. 
• It helps eliminate major business damages by detecting anomalies quickly, as without an anomaly detection system, companies can take weeks and months to identify potential threats. 

Thus, anomaly detection is a huge asset for businesses storing extensive customer and business data sets to find growth opportunities and eliminate security threats and operational bottlenecks.

Techniques of Anomaly Detection

Anomaly detection uses several procedures and machine learning (ML) algorithms to monitor data and detect threats. 

Here are the major anomaly detection techniques:

#1. Machine Learning Techniques

Machines Learning techniques use ML algorithms to analyze data and detect anomalies. The different types of Machine Learning algorithms for anomaly detection include:

• Clustering algorithms 
• Classification algorithms
• Deep learning algorithms 

And the commonly used ML techniques for anomaly and threat detection include support vector machines (SVMs), k-means clustering, and autoencoders.

#2. Statistical Techniques

Statistical techniques use statistical models to detect unusual patterns (like unusual fluctuations in the performance of a particular machine) in the data to detect values that fall beyond the range of the expected values. 

The common statistical anomaly detection techniques include hypothesis testing, IQR, Z-score, modified Z-score, density estimation, boxplot, extreme value analysis, and histogram.

#3. Data Mining Techniques

Data mining techniques use data classification and clustering techniques to find anomalies within the data set. Some common data mining anomaly techniques include spectral clustering, density-based clustering, and principal component analysis. 

Clustering data mining algorithms are used to group different data points into clusters based on their similarity for finding data points and anomalies falling outside these clusters. 

On the other hand, classification algorithms allocate data points to specific predefined classes and detect data points that don’t belong to these classes.

#4. Rule-Based Techniques

As the name suggests, rule-based anomaly detection techniques use a set of predetermined rules to find anomalies within the data. 

These techniques are comparatively easier and simpler to set up but can be inflexible and may not be efficient in adapting to the changing data behavior and patterns. 

For instance, you can easily program a rule-based system to flag transactions exceeding a specific dollar amount as fraudulent.

#5. Domain-Specific Techniques

You can use domain-specific techniques to detect anomalies in specific data systems. However, while they may be highly efficient in detecting anomalies in specific domains, they may be less efficient in other domains outside the specified one. 

For example, using domain-specific techniques, you can design techniques specifically to find anomalies in financial transactions. But, they may not work for finding anomalies or performance drops in a machine.

Need For Machine Learning For Anomaly Detection

Machine learning is very important and highly useful in anomaly detection.

Today, most companies and organizations requiring outlier detection deal with huge amounts of data, from text, customer information, and transactions to media files like images and video content. 

Going through all the bank transactions and data generated each second manually to drive meaningful insight is just next to impossible. Moreover, most companies face challenges and major difficulties in structuring unstructured data and arranging the data in a meaningful way for data analysis. 

This is where tools and techniques like machine learning (ML) play a huge role in collecting, cleaning, structuring, arranging, analyzing, and storing huge volumes of unstructured data. 

Machine Learning techniques and algorithms process large data sets and provide the flexibility to use and combine different techniques and algorithms to provide the best results.

Besides, machine learning also helps streamline anomaly detection processes for real-world applications and saves valuable resources. 

Here are some more benefits and importance of machine learning in anomaly detection: 

• It makes scaling anomaly detection easier by automating the identification of patterns and anomalies without requiring explicit programming. 
• Machine Learning algorithms are highly adaptable to changing data set patterns, making them highly efficient and robust with time. 
• Easily handles large and complex datasets, making anomaly detection efficient despite the data set complexity. 
• Ensures early anomaly identification and detection by identifying anomalies as they happen, saving time and resources. 
• Machine Learning based anomaly detection systems help achieve higher levels of accuracy in anomaly detection compared to traditional methods. 

Thus, anomaly detection paired with machine learning helps faster and more early detection of anomalies to prevent security threats and malicious breaches.

Machine Learning Algorithms For Anomaly Detection

You can detect anomalies and outliers in data with the help of different data mining algorithms for classification, clustering, or association rule learning. 

Typically, these data mining algorithms are classified into two different categories—supervised and unsupervised learning algorithms. 

Supervised Learning

Supervised learning is a common type of learning algorithm that consists of algorithms like support vector machines, logistic and linear regression, and multi-class classification. This algorithm type is trained on labeled data, meaning its training data set includes both normal input data and corresponding correct output or anomalous examples to construct a predictive model. 

Thus, its goal is to make output predictions for unseen and new data based on the training data set patterns. The applications of supervised learning algorithms include image and speech recognition, predictive modeling, and natural language processing (NLP). 

Unsupervised Learning

Unsupervised learning is not trained on any labeled data. Instead, it discovers complicated processes and underlying data structures without providing the training algorithm guidance and instead of making specific predictions. 

The applications of unsupervised learning algorithms include anomaly detection, density estimation, and data compression. 

Now, let’s explore some popular machine learning-based anomaly detection algorithms.

Local Outlier Factor (LOF)

Local Outlier Factor or LOF is an anomaly detection algorithm that considers local data density to determine whether a data point is an anomaly. 

It compares an item’s local density to the local densities of its neighbors to analyze areas of similar densities and items with comparatively lower densities than their neighbors—which are nothing but anomalies or outliers. 

Thus, in simple terms, the density surrounding an outlier or anomalous item differs from the density around its neighbors. Hence, this algorithm is also called a density-based outlier detection algorithm.

K-Nearest Neighbor (K-NN)

K-NN is the simplest classification and supervised anomaly detection algorithm that’s easy to implement, stores all the available examples and data, and classifies the new examples based on the similarities in the distance metrics. 

This classification algorithm is also called a lazy learner because it only stores the labeled training data—without doing anything else while the training process. 

When the new unlabeled training data point arrives, the algorithm looks at the K-nearest or the closest training data points to use them to classify and determine the class of the new unlabeled data point. 

The K-NN algorithm uses the following detection methods to determine the closest data points: 

• Euclidean distance to measure the distance for continuous data. 
• Hamming distance to measure the proximity or “closeness” of the two text strings for discrete data.

For instance, consider your training data sets consist of two class labels, A and B. If a new data point arrives, the algorithm will calculate the distance between the new data point and each of the data points in the data set and select the points that are the maximum in number closest to the new data point. 

So, suppose K=3, and 2 out of 3 data points are labeled as A, then the new data point is labeled as class A. 

Hence, the K-NN algorithm works best in dynamic environments with frequent data update requirements. 

It’s a popular anomaly detection and text mining algorithm with applications in finance and businesses to detect fraudulent transactions and increase the fraud detection rate.

Support Vector Machine (SVM)

Support vector machine is a supervised machine learning-based anomaly detection algorithm mostly used in regression and classification problems. 

It uses a multidimensional hyperplane to segregate data into two groups (new and normal). Thus, the hyperplane acts as a decision boundary that separates the normal data observations and the new data.

The distance between these two data points is referred to as margins. 

Since the goal is to increase the distance between the two points, SVM determines the best or the optimal hyperplane with the maximum margin to ensure the distance between the two classes is as wide as possible. 

Regarding anomaly detection, SVM computes the margin of the new data point observation from the hyperplane to classify it. 

If the margin exceeds the set threshold, it classifies the new observation as an anomaly. At the same time, if the margin is less than the threshold, the observation is classified as normal. 

Thus, the SVM algorithms are highly efficient in handling high-dimensional and complex data sets.

Isolation Forest

Isolation Forest is an unsupervised machine-learning anomaly detection algorithm based on the concept of a Random Forest Classifier. 

This algorithm processes randomly subsampled data in the data set in a tree structure based on random attributes. It constructs several decision trees to isolate observations. And it considers a particular observation an anomaly if it’s isolated in fewer trees based on its contamination rate. 

Thus, in simple terms, the isolation forest algorithm splits the data points into different decision trees—ensuring each observation gets isolated from another. 

Anomalies typically lie away from the data points cluster—making it easier to identify the anomalies compared to the normal data points. 

Isolation forest algorithms can easily handle categorical and numerical data. As a result, they’re faster to train and highly efficient in detecting high-dimensional and large datasets anomalies.

Inter-Quartile Range

Interquartile range or IQR is used to measure statistical variability or statistical dispersion to find anomalous points in the data sets by dividing them into quartiles. 

The algorithm sorts the data in ascending order and splits the set into four equal parts. The values separating these parts are the Q1, Q2, and Q3—first, second, and third quartiles. 

Here’s the percentile distribution of these quartiles: 

• Q1 signifies the 25th percentile of the data.
• Q2 signifies the 50th percentile of the data.
• Q3 signifies the 75th percentile of the data. 

IQR is the difference between the third (75th) and the first (25th) percentile data sets, representing 50% of the data. 

Using IQR for anomaly detection requires you to calculate the IQR of your dataset and define the lower and upper bounds of the data to find anomalies. 

• Lower boundary: Q1 – 1.5 * IQR
• Upper boundary: Q3 1.5 * IQR

Typically, observations falling outside these boundaries are considered anomalies.

The IQR algorithm is effective for datasets with unevenly distributed data and where the distribution isn’t well understood.

Final Words

Cybersecurity risks and data breaches don’t seem to curb in the coming years—and this risky industry is expected to grow further in 2023, and the IoT cyber attacks alone are anticipated to double by 2025. 

Moreover, cybercrimes will cost global companies and organizations an estimated $10.3 trillion annually by 2025. 

This is why the need for anomaly detection techniques is becoming more prevalent and necessary today for fraud detection and preventing network intrusions.

This article will help you understand what anomalies in data mining are, different types of anomalies, and ways to prevent network intrusions using ML-based anomaly detection techniques. 

Next, you can explore everything about the confusion matrix in machine learning.