Overview of Anomaly Detection Technology and Various Implementations

Overview of Anomaly Detection Technology and Applications

<Overview>

Anomaly Detection is a technique for detecting anomalous behavior or patterns in a data set or system. Anomaly detection involves modeling the behavior and patterns of normal data and detecting anomalies by evaluating deviations from them. An anomaly refers to the unexpected appearance of data or abnormal behavior, and is considered as a difference from normal data or an outlier. Anomaly detection is performed using both supervised and unsupervised learning methods.

<Applications of Anomaly Detection Applications>

Anomaly detection technology has the following applications

• Network Security: Anomaly detection is used to detect abnormal user access patterns and communication behavior, monitor network traffic and log data with the goal of identifying anomalous activity, and detect cyber security attacks and intrusions.
• System Monitoring: Anomaly detection is used to detect behavior or failures that deviate from normal system operation. It is used, for example, to monitor server logs and sensor data to detect abnormal performance or unpredictable events, detect faults and abnormal behavior early, and assist in maintenance and troubleshooting.
• Manufacturing: Anomaly detection is applied to quality control and trouble detection in manufacturing processes. The purpose is to monitor sensor data and production line parameters to detect anomalies and defects in products, thereby improving quality and preventing recalls.
• Medical Diagnosis: Abnormality detection also plays an important role in medical diagnosis, for example, by analyzing patients’ bio-data and medical images to detect abnormal lesions and diseases. This includes early detection of heart disease and risk assessment through the detection of abnormal ECG data.

Next, we describe the algorithms used in abnormality detection technology.

Algorithms used in anomaly detection techniques

Various algorithms and methods are used in anomaly detection techniques. Some of the most common among them are described below.

• Statistical Anomaly Detection
  • Anomaly score: Anomaly scores are calculated using statistical characteristics of the data to detect anomalies. Typical methods include Z-score and outlier score.
  • Troubleshooting Chart: Detects anomalies by comparing data to control limits or statistical prediction bandwidths.
• Supervised Anomaly Detection:
  • Support Vector Machines (SVMs): build discriminative models to classify normal and abnormal data, and classify unknown data to detect anomalies.
  • Random Forest: Combines multiple decision trees to build a model to detect anomalies in data.
• Unsupervised Anomaly Detection:
  • Density Estimation: Estimates the distribution of data and detects anomalies by assuming that anomalous data resides in low-density regions. Typical methods include kernel density estimation and Gaussian mixture models.
  • Clustering: Data is divided into clusters, and anomalies are detected based on which cluster the new data belongs to. Typical methods include k-means and DBSCAN.
• Deep Learning-Based Anomaly Detection: Anomaly detection based on deep learning.
  • Autoencoder: An unsupervised learning technique using neural networks to detect anomalies by reconstructing input data.
  • Other deep learning techniques: Anomaly detection using models such as recurrent neural networks (RNN) as described in “Overview of RNN and examples of algorithms and implementations” and convolutional neural networks (CNN) described in “Overview of CNN and examples of algorithms and implementations”.

Examples of specific implementations of the above algorithms are described below.

Example python implementation of anomaly score detection

Although there are different implementation methods for detecting anomaly scores depending on the specific algorithm and characteristics of the data, the following is an example of a Python implementation for general anomaly score detection.

import numpy as np

def calculate_anomaly_score(data):
    # If preprocessing such as normalization is required, do it here.

    # Calculation of mean and standard deviation
    mean = np.mean(data)
    std = np.std(data)

    # Anomaly Score Calculation
    anomaly_scores = np.abs((data - mean) / std)

    return anomaly_scores

# test data
data = [1, 2, 3, 4, 5, 100]

# Anomaly Score Calculation
anomaly_scores = calculate_anomaly_score(data)

# Display Results
for i, score in enumerate(anomaly_scores):
    print(f"Data: {data[i]}, Anomaly Score: {score}")

In the above example, an anomaly score is calculated for each element of the given data. This would first add the appropriate preprocessing steps to normalize or preprocess the data, then calculate the mean and standard deviation of the data, and then calculate the anomaly score. The anomaly score is calculated as the value of how much the data deviates from the mean, normalized by the standard deviation. The results of the run show the value of each data and the anomaly score for it; the higher the anomaly score, the more likely the data is to be considered anomalous.

Since the method of anomaly score detection depends on the problem and data characteristics, the selection of appropriate algorithms and parameters is important, and it is recommended that different methods and algorithms be tried based on specific anomaly detection requirements.

Supervised anomaly detection implementation in python

Supervised anomaly detection requires labeling of normal and abnormal data. Below is an example Python implementation of supervised anomaly detection using a support vector machine (SVM).

from sklearn import svm

# normal data
normal_data = [[0, 0], [1, 1], [2, 2], [3, 3]]
# abnormal data
anomalous_data = [[2, 2], [3, 3], [10, 10]]

# Combine and label data
X_train = normal_data + anomalous_data
y_train = [0] * len(normal_data) + [1] * len(anomalous_data)

# SVM model construction and training
model = svm.OneClassSVM()
model.fit(X_train)

# Predicts anomaly scores for new data
new_data = [[4, 4], [5, 5]]
anomaly_scores = model.decision_function(new_data)

# Display Results
for i, score in enumerate(anomaly_scores):
    print(f"データ: {new_data[i]}, 異常スコア: {score}")

In the above example, normal and abnormal data are prepared and each is labeled (label 0 for normal data and label 1 for abnormal data). The model is then built using SVM’s OneClassSVM class, trained using the training data, and the new data is given to it as input to predict the abnormality score using the decision_function method. The anomaly score is a value that indicates how much the data deviates from the normal data; the closer the anomaly score is to a negative value, the more anomalous the data is considered, and finally, the results are displayed. Each data value and the anomaly score for it are displayed.

Anomaly detection methods and algorithms may vary depending on the characteristics of the data and the definition of anomaly. The selection of appropriate algorithms and parameters should be tailored to the specific requirements and data set.

Unsupervised anomaly detection implementation in python

Unsupervised anomaly detection uses only normal data to detect anomalies. Below is an example Python implementation of unsupervised anomaly detection using One-Class SVM.

from sklearn import svm

# normal data
normal_data = [[0, 0], [1, 1], [2, 2], [3, 3]]

# Use data for learning
X_train = normal_data

# Building and Learning One-Class SVM Models
model = svm.OneClassSVM(nu=0.1)  # nuパラメータは異常データの割合を指定
model.fit(X_train)

# Predicts anomaly scores for new data
new_data = [[2, 2], [4, 4]]
anomaly_scores = model.decision_function(new_data)

# Display Results
for i, score in enumerate(anomaly_scores):
    print(f"データ: {new_data[i]}, 異常スコア: {score}")

In the above example, only normal data is used to build the training data; the One-Class SVM model assumes that normal data is in the data space and attempts to identify other data as abnormal. nu parameter specifies the percentage of abnormal data and should be adjusted appropriately. It is fed new data as input and uses the decision_function method to predict the anomaly score. The anomaly score is a value that indicates how much the data deviates from normal data; the closer the anomaly score is to a negative value, the more anomalous the data is considered. Finally, the results are displayed. The value of each data and the anomaly score for it are displayed.

Anomaly detection methods and algorithms may vary depending on the characteristics of the data and the definition of anomaly, and must be tailored to the specific requirements and data set in order to select the appropriate algorithm and parameters.

Python implementation of deep learning-based anomaly detection

Anomaly detection using deep learning typically uses an autoencoder (Autoencoder). Below is an example Python implementation of deep learning-based anomaly detection using Keras.

import numpy as np
from tensorflow import keras

# Train models using only normal data
X_train = ...  # Normal data features

# Build a model of an auto encoder
input_dim = X_train.shape[1]
encoding_dim = 32  # Number of encoding dimensions
model = keras.Sequential([
    keras.layers.Dense(encoding_dim, activation='relu', input_dim=input_dim),
    keras.layers.Dense(input_dim, activation='sigmoid')
])
model.compile(optimizer='adam', loss='mse')

# Model Learning
model.fit(X_train, X_train, epochs=50, batch_size=32, verbose=0)

# Predicts anomaly scores for test data
X_test = ...  # Test Data Features
X_pred = model.predict(X_test)
mse = np.mean(np.power(X_test - X_pred, 2), axis=1)  # Mean squared error (anomaly score)

# Display Results
for i, score in enumerate(mse):
    print(f"Data: {X_test[i]}, anomaly score: {score}")

In the above example, the autoencoder model is trained using only normal data. An autoencoder is a model that converts data into a low-dimensional representation (encoding) and reconstructs (decoding) it. In training, the model is trained to reconstruct the input data into itself.

Test data is given as input and the mean squared error (MSE) with the data reconstructed by the model is calculated; the MSE is used as the anomaly score, and the larger the reconstruction error, the more anomalous the data is considered. Finally, the results are displayed, showing the value of each data and the anomaly score for it.

In the implementation of deep learning models, data preprocessing, model architecture, and hyperparameter selection are important. They should be appropriately tailored to the specific requirements and data set.

Reference Information and Reference Books

For various other anomaly detection techniques, see “Anomaly Detection and Change Detection Techniques. Reference books include Machine Learning approaches for Anomaly detection in Stock Securities.

Anomaly Detection Principles and Algorithms

Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and PyTorch