Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.

Deep Learning for Anomaly Detection: A Survey.

Aiming at the problemsof too much iterative times in selecting initial centroids stochastically for K-Means algorithm,a method is proposed to optimize the initial centroids through cutting the set into k segmentations and select one point in each segmentation as initial centroids for iterative computing. A new valid function called clustering-index is defined as the sum of clustering-density and clustering-significance and can be used to search the optimization of k in the internal of [1,n(1/2) ]. The simulation experiment with IRIS data set shows that the proposed algorithm converges faster and the value k found is close to the actual value,which proves the validity of the algorithm.

An Improved K-Means Clustering Algorithm

Intrusion Detection Systems (IDSs) play a vital role in securing today's Data-Centric Networks In a dynamic environment such as the Internet of Things (IoT), which is vulnerable to various types of attacks, fast and robust solutions are in demand to handle fast-changing threats and thus the ever-increasing difficulty of detection In this paper, we present a novel framework for the detection of anomalies, which, in particular, supports intrusion detection The anomaly-detection framework we propose combines reinforcement learning with class-imbalance techniques Our goal is not only to exploit the auto-learning ability of the reinforcement-learning loop but also to address the dataset imbalance problem, which is pervasive in existing learning-based solutions We introduce an adapted SMOTE to address the class-imbalance problem while remodelling the behaviors of the environment agent for better performance Experiments are conducted on NSL-KDD datasets Comparative evaluations and their results are presented and analyzed Using techniques such as SMOTE, ROS, NearMiss1 and NearMiss2, performance measures obtained from our simulations have led us to recognize specific performance trends In particular, the proposed model AESMOTE outperforms AE-RL in several cases Experiment results show an Accuracy greater than 082 and a F1 greater than 0824

AESMOTE: Adversarial Reinforcement Learning With SMOTE for Anomaly Detection

The great losses caused by financial fraud have attracted continuous attention from academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus pandemic (COVID-19) unexpectedly shocks the global financial system and accelerates the use of digital financial services, which brings new challenges in effective financial fraud detection. This paper provides a comprehensive overview of intelligent financial fraud detection practices. We analyze the new features of fraud risk caused by the pandemic and review the development of data types used in fraud detection practices from quantitative tabular data to various unstructured data. The evolution of methods in financial fraud detection is summarized, and the emerging Graph Neural Network methods in the post-pandemic era are discussed in particular. Finally, some of the key challenges and potential directions are proposed to provide inspiring information on intelligent financial fraud detection in the future.

Intelligent financial fraud detection practices in post-pandemic era.

In the last decades Machine Learning achieved notable results in various areas of data processing and classification, which made the creation of real-time interactive and intelligent systems possible. The accuracy and precision of those systems depends not only on the correctness of the data, logically and chronologically, but also on the time the feed-backs are produced. This paper focuses on one of these systems which is a fraud detection system. In order to have a more accurate and precise fraud detection system, banks and financial institutions are investing more and more today in perfecting the algorithms and data analysis technologies used to identify and combat fraud. Therefore, many solutions and algorithms using machine learning have been proposed in literature to deal with this issue. However, comparison studies exploring Deep learning paradigms are scarce, and to our knowledge, the proposed works don't consider the importance of a Real-time approach for this type of problems. Thus, to cope with this problem we propose a live credit card fraud detection system based on a deep neural network technology. Our proposed model is based on an auto-encoder and it permits to classify, in real-time, credit card transactions as legitimate or fraudulent. To test the effectiveness of our model, four different binary classification models are used as a comparison. The Benchmark shows promising results for our proposed model than existing solutions in terms of accuracy, recall and precision.

An Efficient Real Time Model For Credit Card Fraud Detection Based On Deep Learning

Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.

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Chengkun Guo

Papers

A state of the art survey of data mining-based fraud detection and credit scoring