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

Demestichas, Konstantinos; Πεππές, Νικόλαος; Alexakis, Theodoros; Adamopoulou, Evgenia

doi:10.3390/info12010034

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…The logical operators in Description Logic hold particular significance for their role in shaping the semantics of the ontology. Standard constructors include existential quantification (∃), universal quantification (∀), conjunction (∩), and negation (¬), among others [129][130][131][132]. These operators allow for the expression of intricate relationships and constraints among ontology elements.…”

Section: Description Logicmentioning

confidence: 99%

“…This formalization using Description Logic provides a mathematical foundation for the ontology and enables the application of automated reasoning tools for ontology verification and query answering. Thus, the transition from tabular representations to Description Logic enhances the ontology's operational effectiveness and semantic clarity [132][133][134][135].…”

Section: Description Logicmentioning

confidence: 99%

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Interoperability-Enhanced Knowledge Management in Law Enforcement: An Integrated Data-Driven Forensic Ontological Approach to Crime Scene Analysis

Spyropoulos,

Bratsas,

Makris

et al. 2023

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Nowadays, more and more sciences are involved in strengthening the work of law enforcement authorities. Scientific documentation is evidence highly respected by the courts in administering justice. As the involvement of science in solving crimes increases, so does human subjectivism, which often leads to wrong conclusions and, consequently, to bad judgments. From the above arises the need to create a single information system that will be fed with scientific evidence such as fingerprints, genetic material, digital data, forensic photographs, information from the forensic report, etc., and also investigative data such as information from witnesses’ statements, the apology of the accused, etc., from various crime scenes that will be able, through formal reasoning procedure, to conclude possible perpetrators. The present study examines a proposal for developing an information system that can be a basis for creating a forensic ontology—a semantic representation of the crime scene—through descriptive logic in the owl semantic language. The Interoperability-Enhanced information system to be developed could assist law enforcement authorities in solving crimes. At the same time, it would promote closer cooperation between academia, civil society, and state institutions by fostering a culture of engagement for the common good.

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Section: Description Logicmentioning

confidence: 99%

Section: Description Logicmentioning

confidence: 99%

Interoperability-Enhanced Knowledge Management in Law Enforcement: An Integrated Data-Driven Forensic Ontological Approach to Crime Scene Analysis

Spyropoulos,

Bratsas,

Makris

et al. 2023

Information

View full text Add to dashboard Cite

show abstract

“…Anomaly detection is used in several scientific domains such as healthcare, intrusion detection, sensor network, and fraud detection, to name a few. Detecting irregularities in the network, identifying anomalies in financial transactions, detecting fraudulent activities, and detecting anomalies in medical images are some anomaly detection applications [36]. In networks, anomaly patterns can be identified based on the classification of packet data containing abnormal patterns.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

2021

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The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solutions for intrusion detection systems and intrusion prevention systems. Network communities have produced benchmark datasets available for researchers to improve the accuracy of intrusion detection systems. The scientific community has presented data mining and machine learning-based mechanisms to detect intrusion with high classification accuracy. This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Performance analysis of the proposed intrusion detection system is evaluated using publicly available intrusion datasets UNSW-NB15 and CICIDS2017. The proposed model-based intrusion detection accuracy for the UNSW-NB15 dataset is 98.801 percent, and the CICIDS2017 dataset is 97.02 percent. The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.

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“…It is found that the random forest prediction effect has obvious advantages, and it is said that the random forest also plays an important role in gene prediction. Demestichas [27] predicted the remote sensing location by using the random forest model and other commonly used prediction algorithm models and found that the prediction accuracy of the random forest model is the highest. Kurniawan [28] simplified the financial indicators based on the correlation and importance of the indicators, constructed the financial early warning model, and established the variable precision weighted average roughness decision tree, which significantly improved the noise prevention ability and classification accuracy of the early warning model.…”

Section: Related Workmentioning

confidence: 99%

Empirical Analysis of Financial Statement Fraud of Listed Companies Based on Logistic Regression and Random Forest Algorithm

Liu

2021

Journal of Mathematics

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Financial supervision plays an important role in the construction of market economy, but financial data has the characteristics of being nonstationary and nonlinear and low signal-to-noise ratio, so an effective financial detection method is needed. In this paper, two machine learning algorithms, decision tree and random forest, are used to detect the company's financial data. Firstly, based on the financial data of 100 sample listed companies, this paper makes an empirical study on the fraud of financial statements of listed companies by using machine learning technology. Through the empirical analysis of logistic regression, gradient lifting decision tree, and random forest model, the preliminary results are obtained, and then the random forest model is used for secondary judgment. This paper constructs an efficient, accurate, and simple comprehensive application model of machine learning. The empirical results show that the comprehensive application model constructed in this paper has an accuracy of 96.58% in judging the abnormal financial data of listed companies. The paper puts forward an accurate and practical method for capital market participants to identify the fraud of financial statements of listed companies and has certain practical significance for investors and securities research institutions to deal with the fraud of financial statements.

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

Cited by 5 publications

References 43 publications

Interoperability-Enhanced Knowledge Management in Law Enforcement: An Integrated Data-Driven Forensic Ontological Approach to Crime Scene Analysis

Interoperability-Enhanced Knowledge Management in Law Enforcement: An Integrated Data-Driven Forensic Ontological Approach to Crime Scene Analysis

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

Empirical Analysis of Financial Statement Fraud of Listed Companies Based on Logistic Regression and Random Forest Algorithm

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