# An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions

^{*}

## Abstract

**:**

## 1. Introduction

## 2. High Level Platform Architecture

**Visual Intelligence Module**: This module is implemented by using face and object detection and recognition algorithms (You Only Look Once—YOLO [1], Single Stage Headless—SSH [2], etc.) so as to achieve the identification of suspicious activities, persons and objects contained in footage from static or moving cameras.

**Data Mining Module for Crime Prevention and Investigation**: Data mining methods are used to extract valuable information from the existing data for analysis purposes. The data are exported from multiple sources and stored into a common format, in order to be accessible by the data analysis modules. A scalable data crawler infrastructure is employed in order to gather the necessary data from various sources, both from the DarkWeb and the ClearWeb. Then, the collected data are curated into appropriate datasets that can be further used by LEA officials for search and analysis purposes.

**Semantic Information Representation and Fusion Module**: This module is dedicated to data and information fusion and works on the heterogeneous data that are gathered from the data mining module. The use of appropriate semantic technologies and information fusion models, then, transforms the gathered information into valuable knowledge.

**Trends Detection and Probability Prediction Module for Organized Terrorism and Criminal Activities**: Big Data analytics techniques are applied over the gathered data so as to identify hidden trends inside the datasets. The deployment of Big Data analytics alongside the predictive models constitute the link between the analytic and the decision-making process.

**Anomaly and Cyber-Criminal Activities Detection Module**: This module is responsible for applying advanced Big Data analytics techniques, based on machine learning, to the collected data [6]. The main purpose of this module is the identification of anomalies and behavioral indicators, as well as the revelation of unknown associations and rules that are linked with cyber-criminal activities. This module hosts the real-time abnormal behavior detection engine, presented in detail in the following chapters. The anomaly and cyber-criminal activities detection module enables advanced data mining operations in order to support the LEAs’ officers in the task of outlier analysis. Outlier analysis enhances LEAs’ capabilities in the identification of abnormal behavior, i.e., behavior not expected based on the data available until the present moment.

**Situation Awareness and Human–Machine Interaction Module**: Innovative tools for data visualization and knowledge representation are implemented in order to increase the situation awareness of the decision makers. The module hosts three main visualization and situational awareness tools: (i) the Web-based Human–Machine Interface (HMI), a modular component in which users, alongside other functionalities, can access the advanced visualization tool for knowledge graphs described in detail in Section 5 of this paper; (ii) the Web-based Geographic Information System (GIS) and haptic feedback service, dedicated to the visualization of geospatial resources coming from different services, and also allowing users to interact via a haptic device which offers a more direct and intuitive way of controlling and navigating information; and (iii) the Virtual Reality (VR) visualization tool, which constitutes a natural environment for the visualization of 3D graphs that improves the general understanding of users by allowing them to navigate and interact more intuitively with complex data structures.

## 3. An Overview of Outlier Detection Algorithms and Machine Learning Methods

#### 3.1. Oultiers Detection Theory and Z-score for Data Labelling

- Z-score or Extreme Value Analysis (parametric);
- Probabilistic and Statistical Modelling (parametric);
- Linear Regression Models (PCA, LMS);
- Proximity Based Models (non-parametric);
- Information Theory Models;
- High Dimensional Outlier Detection Methods (high dimensional sparse data).

**Credit-card fraud**, where hidden patterns of possible fraudulent or unauthorized activity and/or use of sensitive credit-card number information, as well as transaction data, can be recognized with greater ease.**Intrusion detection systems**, where abnormal or malicious activity of different data types (e.g., network traffic) in various computer systems can be detected and analyzed.**Law enforcement**, by generating specific patterns of financial frauds, insurance claims or trading activity under the action of criminal behaviors.**Medical diagnosis**, based on data collected from various sources (e.g., Magnetic Resonance Imaging - MRI, Positron Emission Tomography - PET or electrocardiogram - ECG scans) which reveal possible disease issues.**Sensor events**, since a massive amount of sensing devices (e.g., location parameters) can provide new insights or events at new domains of interest.**Earth science**, where spatiotemporal data (e.g., weather signs or climate change patterns) provide new environmental or climate trends regarding human activity or alternative hidden causes.

_{i}= (x

_{i}− μ)/σ

- Z
_{i}= Z-score for the specific data point; - x
_{i}= individual measurement for a distinct data point; - μ = the mean of the measurements;
- σ = the standard deviation of the measurements.

_{1}based on (3):

_{21}− λ

_{11})P(w

_{1}|x)> (λ

_{12}− λ

_{22}) P(w

_{2}|x)

- x: the given observation data point;
- w
_{1}: class 1 (as the equation refers into a two-class problem); - w
_{2}: class 2; - λ: the cost between the two classes of the problem;
- P: the posterior probability that observation x is an outlier.

_{2}|x) = 1 − P(w

_{1}|x)

#### 3.2. Outlier Detection Algorithms and Related Work

_{ij}). Every neural network receives a set of inputs and, through its training process, adjusts the weight values so as to produce the desired output(s) [43,44,45]. Outliers affect neural networks significantly and decrease their efficiency and accuracy [45]; however, there are certain methods and techniques, based on artificial neural networks, that can be very useful for outlier detection. Auto-encoder is a special type of neural network which consists of multiple layers and mainly performs a dimensionality reduction of the input data. In the most common case of an auto-encoder, the input and output have the same dimensions. The goal of such algorithms is to train the output to reconstruct the input by reducing the dimensionality. Thus, auto-encoder algorithms can discover outliers because, during the reconstruction process, it is much more difficult to represent outliers than normal points. Outliers will have a much larger error after the reconstruction so it becomes easy to score datapoints and categorize them as outliers or not [46,47].

## 4. Evaluation and Comparison of Different Methods for Outlier Detection

#### 4.1. Evaluation of Results

- t: the defined threshold;
- S(t): the declared outlier;
- G: the true set of outliers in the data set.

#### 4.2. Comparison of Different Algorithms, Datasets and Results

## 5. Real-Time Abnormal Behavior Detection Engine and Knowledge Base Visualization Tool

#### 5.1. Real-Time Abnormal Behavior Detection Engine Interface

- The amount of money transferred in the mobile financial transaction;
- The source name of the transaction;
- The destination name of the transaction;
- The execution type of the transaction (includes Cash-in, Cash-out, Debit, Payment, Transfer);
- Location name of the destination endpoint of the executed transaction;
- Date and time of the executed transaction.

#### 5.2. Knowledge Graph Visualization Tool

## 6. Conclusions and Future Directions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 2.**True Positives (TP), False Positives (FP), True Negatives (TN) and False Negatives (FN) illustration.

**Table 1.**Overview of Area Under the ROC (AUC) and Precision metrics (train and test data) for different algorithms.

Proximity-Based | LinearModel | Vector-Based | Outlier Ensembled | Neural Networks | |||||
---|---|---|---|---|---|---|---|---|---|

Metric | LOF | CBLOF | HBOS | KNN | MCD | PCA | ABOD | I-FOREST | AUTO-ENCODER |

AUC—Train | 0.4942 | 0.8244 | 0.5677 | 0.9285 | 0.7964 | 0.9192 | 0.4731 | 0.9785 | 0.9985 |

Precision—Train | 0.0099 | 0.8475 | 0.8247 | 0.8379 | 0.7522 | 0.7546 | 0.3085 | 0.9274 | 0.9474 |

AUC—Test | 0.4831 | 0.8238 | 0.5711 | 0.9275 | 0.7951 | 0.9185 | 0.4711 | 0.9721 | 0.9974 |

Precision—Test | 0.0086 | 0.8462 | 0.8234 | 0.8367 | 0.7487 | 0.7533 | 0.3074 | 0.9263 | 0.9461 |

**Table 2.**Overview of Receiver Operating Characteristics (ROC) performance using the studied algorithms in different datasets.

Dataset | LOF | CBLOF | HBOS | KNN | MCD | PCA | ABOD | I-FOREST |
---|---|---|---|---|---|---|---|---|

arrhythmia | 0.7787 | 0.7835 | 0.8219 | 0.7861 | 0.7790 | 0.7815 | 0.7688 | 0.8005 |

letter | 0.8594 | 0.5070 | 0.5927 | 0.8766 | 0.8074 | 0.5283 | 0.8783 | 0.6420 |

mnist | 0.7161 | 0.8009 | 0.5742 | 0.8481 | 0.8666 | 0.8527 | 0.7815 | 0.8159 |

pendigits | 0.4500 | 0.5089 | 0.8732 | 0.3708 | 0.3979 | 0.5086 | 0.4667 | 0.7253 |

satellite | 0.5573 | 0.5572 | 0.7581 | 0.6836 | 0.8030 | 0.5988 | 0.5714 | 0.7022 |

Dataset | LOF | CBLOF | HBOS | KNN | MCD | PCA | ABOD | I-FOREST |
---|---|---|---|---|---|---|---|---|

arrhythmia | 0.4334 | 0.4539 | 0.5111 | 0.4464 | 0.3995 | 0.4613 | 0.3808 | 0.4961 |

letter | 0.3641 | 0.0749 | 0.0715 | 0.3312 | 0.1933 | 0.0875 | 0.3801 | 0.1003 |

mnist | 0.3343 | 0.3348 | 0.1188 | 0.4204 | 0.3462 | 0.3846 | 0.3555 | 0.3135 |

pendigits | 0.0653 | 0.2768 | 0.2979 | 0.0984 | 0.0893 | 0.3187 | 0.0812 | 0.3422 |

satellite | 0.3893 | 0.4152 | 0.5690 | 0.4994 | 0.6845 | 0.4784 | 0.3902 | 0.5676 |

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**MDPI and ACS Style**

Demestichas, K.; Peppes, N.; Alexakis, T.; Adamopoulou, E.
An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions. *Information* **2021**, *12*, 34.
https://doi.org/10.3390/info12010034

**AMA Style**

Demestichas K, Peppes N, Alexakis T, Adamopoulou E.
An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions. *Information*. 2021; 12(1):34.
https://doi.org/10.3390/info12010034

**Chicago/Turabian Style**

Demestichas, Konstantinos, Nikolaos Peppes, Theodoros Alexakis, and Evgenia Adamopoulou.
2021. "An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions" *Information* 12, no. 1: 34.
https://doi.org/10.3390/info12010034