Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

Security, Fraudulent transactions and Customer Loyalty: A Field Study

Security and Privacy has become a dominant issue for both consumers and corporations. In this paper, we investigate how customer behavior is affected after they have been a victim of financial fraud. Our analysis provides insights into how security concerns affect the continuation of the existing relationship of the customers depending on kind of fraudulent transactions. With the data from one of the largest banks in the US, we show that the probability of ending the relationship in the next six months increases significantly after a fraudulent transaction. We provide results with a detailed analysis including the kind of fraudulent transaction, tenure and location

データマイニングにおけるユーザベリティ向上と応用に関する研究

制度:新 ; 文部省報告番号:甲2574号 ; 学位の種類:博士(工学) ; 授与年月日:2008/3/15 ; 早大学位記番号:新473

Blockchain: A Graph Primer

Bitcoin and its underlying technology Blockchain have become popular in recent years. Designed to facilitate a secure distributed platform without central authorities, Blockchain is heralded as a paradigm that will be as powerful as Big Data, Cloud Computing and Machine learning. Blockchain incorporates novel ideas from various fields such as public key encryption and distributed systems. As such, a reader often comes across resources that explain the Blockchain technology from a certain perspective only, leaving the reader with more questions than before. We will offer a holistic view on Blockchain. Starting with a brief history, we will give the building blocks of Blockchain, and explain their interactions. As graph mining has become a major part its analysis, we will elaborate on graph theoretical aspects of the Blockchain technology. We also devote a section to the future of Blockchain and explain how extensions like Smart Contracts and De-centralized Autonomous Organizations will function. Without assuming any reader expertise, our aim is to provide a concise but complete description of the Blockchain technology.Comment: 16 pages, 8 figure

Managing health insurance using blockchain technology

Health insurance plays a significant role in ensuring quality healthcare. In response to the escalating costs of the medical industry, the demand for health insurance is soaring. Additionally, those with health insurance are more likely to receive preventative care than those without health insurance. However, from granting health insurance to delivering services to insured individuals, the health insurance industry faces numerous obstacles. Fraudulent actions, false claims, a lack of transparency and data privacy, reliance on human effort and dishonesty from consumers, healthcare professionals, or even the insurer party itself, are the most common and important hurdles towards success. Given these constraints, this chapter briefly covers the most immediate concerns in the health insurance industry and provides insight into how blockchain technology integration can contribute to resolving these issues. This chapter finishes by highlighting existing limitations as well as potential future directions.Comment: 37 pages, 2 figures, 12 table

Graph Mining for Cybersecurity: A Survey

The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance. It is imperative to summarize existing graph-based cybersecurity solutions to provide a guide for future studies. Therefore, as a key contribution of this paper, we provide a comprehensive review of graph mining for cybersecurity, including an overview of cybersecurity tasks, the typical graph mining techniques, and the general process of applying them to cybersecurity, as well as various solutions for different cybersecurity tasks. For each task, we probe into relevant methods and highlight the graph types, graph approaches, and task levels in their modeling. Furthermore, we collect open datasets and toolkits for graph-based cybersecurity. Finally, we outlook the potential directions of this field for future research

Identifying Crypto Addresses with Gambling Behaviors: A Graph Neural Network Approach

The development of blockchain technology has brought prosperity to the cryptocurrency market and has made the blockchain platform a hotbed of crimes. As one of the most rampant crimes, crypto gambling has more high risk of illegal activities due to the lack of regulation. As a result, identifying crypto addresses with gambling behaviors has emerged as a significant research topic. In this work, we propose a novel detection approach based on Graph Neural Networks named CGDetector, consisting of Graph Construction, Subgraph Extractor, Statistical Feature Extraction, and Gambling Address Classification. Extensive experiments of large-scale and heterogeneous Ethereum transaction data are implemented to demonstrate that our proposed approach outperforms state-of-the-art address classifiers of traditional machine learning methods. This work makes the first attempt to detect suspicious crypto gambling addresses via Graph Neural Networks by all EVM-compatible blockchain systems, providing new insights into the field of cryptocurrency crime detection and blockchain security regulation