Applying Semantic Technologies to Fight Online Banking Fraud

Cybercrime tackling is a major challenge for Law Enforcement Agencies (LEAs). Traditional digital forensics and investigation procedures are not coping with the sheer amount of data to analyse, which is stored in multiple devices seized from distinct, possibly-related cases. Moreover, inefficient information representation and exchange hampers evidence recovery and relationship discovery. Aiming at a better balance between human reasoning skills and computer processing capabilities, this paper discusses how semantic technologies could make cybercrime investigation more efficient. It takes the example of online banking fraud to propose an ontology aimed at mapping criminal organisations and identifying malware developers. Although still on early stage of development, it reviews concepts to extend from well-established ontologies and proposes novel abstractions that could enhance relationship discovery. Finally, it suggests inference rules based on empirical knowledge which could better address the needs of the human analyst

The role of information systems in the prevention and detection of transnational and international crime

© Cambridge University Press 2014. All around the world criminal activity remains at the forefront of governmental concerns, not only as a problem that distorts the very fabric of society within the confines of national jurisdictions, but also as a problem that cuts across national borders to exhibit a global dimension. The international dimension of criminal activity remains critical and is generally characterized by a complexity that is unique and requires action on many different levels. Criminals set out to mask their illegal activities and deliberately generate complexity as a means of concealment. In doing so, they exploit new developments in technology that assist them in achieving their ends. This criminality exhibits forms of innovation that stretch far beyond traditional criminal activity (e.g., drug and human trafficking) and manages to attach itself within the broader fabric of society by exploiting the very latest developments. This evolution is necessary as criminals seek not only to escape arrest, prosecution and conviction, but also to enjoy the fruits of their criminality (mostly financial gains). Thus, they seek to develop ways of exploiting the various diffuse norms of social interaction (e.g., trust), financial modes of conduct (e.g., cash-based economies), technological and communication developments (e.g., Internet), and thereby minimize the possibility for detection. By limiting the resources that can be made available for prevention (or making them obsolete when developing new criminal behaviour), they participate in this co-evolution actively; and this they achieve by generating complexity

Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy

Phishing attacks has been growing rapidly in the past few years. As a result, a number of approaches have been proposed to address the problem. Despite various approaches proposed such as feature-based and blacklist-based via machine learning techniques, there is still a lack of accuracy and real-time solution. Most approaches applying machine learning techniques requires that parameters are tuned to solve a problem, but parameters are difficult to tune to a desirable output. This study presents a parameter tuning framework, using adaptive Neuron-fuzzy inference system with comprehensive data to maximize systems performance. Extensive experiment was conducted. During ten-fold cross-validation, the data is split into training and testing pairs and parameters are set according to desirable output and have achieved 98.74% accuracy. Our results demonstrated higher performance compared to other results in the field. This paper contributes new comprehensive data, novel parameter tuning method and applied a new algorithm in a new field. The implication is that adaptive neuron-fuzzy system with effective data and proper parameter tuning can enhance system performance. The outcome will provide a new knowledge in the field

Intelligent phishing detection parameter framework for E-banking transactions based on Neuro-fuzzy

Phishing attacks have become more sophisticated in web-based transactions. As a result, various solutions have been developed to tackle the problem. Such solutions including feature-based and blacklist-based approaches applying machine learning algorithms. However, there is still a lack of accuracy and real-time solution. Most machine learning algorithms are parameter driven, but the parameters are difficult to tune to a desirable output. In line with Jiang and Ma’s findings, this study presents a parameter tuning framework, using Neuron-fuzzy system with comprehensive features in order to maximize systems performance. The neuron-fuzzy system was chosen because it has ability to generate fuzzy rules by given features and to learn new features. Extensive experiments were conducted, using different feature-sets, two cross-validation methods, a hybrid method and different parameters and achieved 98.4% accuracy. Our results demonstrated a high performance compared to other results in the field. As a contribution, we introduced a novel parameter tuning framework based on a neuron-fuzzy with six feature-sets and identified different numbers of membership functions different number of epochs, different sizes of feature-sets on a single platform. Parameter tuning based on neuron-fuzzy system with comprehensive features can enhance system performance in real-time. The outcome will provide guidance to the researchers who are using similar techniques in the field. It will decrease difficulties and increase confidence in the process of tuning parameters on a given problem

Opportunities and Challenges of Applying Artificial Intelligence in the Financial Sectors and Startups during the Coronavirus Outbreak

Purpose: The main goal of this article is the comprehensive study of the applications of artificial intelligence in financial sectors in addition to startups and its impacts on such cases along with Covid19. Methodology: we have tried to study the applications of artificial intelligence in different areas especially financial fields such as accounting, auditing, management, capital market, banking etc. On the other hand, we have studied the impacts of artificial intelligence on startups during Covid-19 too. Findings: The results showed that AI can be a powerful tool in financial fields such as investment advice, asset allocation, fraud detection, portfolio management and etc. and startups such as increasing production and productivity, time management, data management and analysis and etc. during the Covid-19 outbreaks and it can decrease the harmful effects of Coronavirus. Thus, timely actions can be taken. Originality/Value: The main contribution of this paper is a comprehensive and specialized look at the discussion of the applications of artificial intelligence in the field of finance as well as startups during Covid19. We have tried to consider subjects and contents which cover most of the paper

A Security Model for the Classification of Suspicious Data Using Machine Learning Techniques

Cybercrime first emerged in 1981 and gained significant attention in the 20th century. The proliferation of technology and our increasing reliance on the internet have been major factors contributing to the growth of cybercrime. Different countries face varying types and levels of cyber-attacks, with developing countries often dealing with different types of attacks compared to developed countries. The response to cybercrime is usually based on the resources and technological capabilities available in each country. For example, sophisticated attacks involving machine learning may not be common in countries with limited technological advancements. Despite the variations in technology and resources, cybercrime remains a costly issue worldwide, projected to reach around 8 trillion by 2023. Preventing and combating cybercrime has become crucial in our society. Machine learning techniques, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and more, have gained popularity in the fight against cybercrime. Researchers and authors have made significant contributions in protecting and predicting cybercrime. Nowadays, many corporations implement cyber defense strategies based on machine learning to safeguard their data. In this study, we utilized five different machine learning algorithms, including CNN, LSTM, RNN, GRU, and MLP DNN, to address cybercrime. The models were trained and tested using the InSDN public dataset. Each model provided different levels of trained and test accuracy percentages

Cyber-crime Science = Crime Science + Information Security

Cyber-crime Science is an emerging area of study aiming to prevent cyber-crime by combining security protection techniques from Information Security with empirical research methods used in Crime Science. Information security research has developed techniques for protecting the confidentiality, integrity, and availability of information assets but is less strong on the empirical study of the effectiveness of these techniques. Crime Science studies the effect of crime prevention techniques empirically in the real world, and proposes improvements to these techniques based on this. Combining both approaches, Cyber-crime Science transfers and further develops Information Security techniques to prevent cyber-crime, and empirically studies the effectiveness of these techniques in the real world. In this paper we review the main contributions of Crime Science as of today, illustrate its application to a typical Information Security problem, namely phishing, explore the interdisciplinary structure of Cyber-crime Science, and present an agenda for research in Cyber-crime Science in the form of a set of suggested research questions

A Tertiary Review on Blockchain and Sustainability With Focus on Sustainable Development Goals

Sustainable development is crucial to securing the future of humanity. Blockchain as a disruptive technology and a driver for social change has exhibited great potential to promote sustainable practices and help organizations and governments achieve the United Nations’ Sustainable Development Goals (SDGs). Existing literature reviews on blockchain and sustainability often focus only on topics related to a few SDGs. There is a need to consolidate existing results in terms of SDGs and provide a comprehensive overview of the impacts that blockchain technology may have on each SDG. This paper intends to bridge this gap, presenting a tertiary review based on 42 literature reviews, to investigate the relationship between blockchain and sustainability in light of SDGs. The method used is a consensus-based expert elicitation with thematic analysis. The findings include a novel and comprehensive mapping of impact-based interlinkage of blockchain and SDGs and a systematic overview of drivers and barriers to adopting blockchain for sustainability. The findings reveal that blockchain can have a positive impact on all 17 SDGs though some negative effects can occur and impede the achievement of certain objectives. 76 positive and 10 negative linkages between blockchain adoption and the 17 SDGs as well as 45 factors that drive or hinder blockchain adoption for the achievement of SDGs have been identified. Research gaps to overcome the barriers and enhance blockchain’s positive impacts have also been identified. The findings may help managers in evaluating the applicability and tradeoffs, and policymakers in making supportive measures to facilitate sustainability using blockchain.publishedVersio

Cyber Warfare Impact to National Security - Malaysia Experiences

This study analyzed the cyber warfare impact on national security and focusing on Malaysia experiences. The issues regarding cyber warfare have become a serious concern since it was a risk of national security in Malaysia. The objectives of the study are to analyze issues related to cyber warfare that affected Malaysian system security, to determine causes that caused to cyber warfare. This study used a qualitative research approach to evaluate the current defense approaches related to cyber warfare in Malaysia. The interviews were conducted with the respective respondents: the Senior Manager, Research Management Centre, Strategic Research, and Advisory Department of Cyber Security Malaysia Department. This study can contribute to expanding the security of national security by demanding the government to adopt a broad acquisition risk management strategy. It can assist in the development of highly effective aggressive and defensive methods to any company dealing with future cyber warfare challenges and risk.   Keywords: cyber warfare, national security, experiences

Disrupting Finance

This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry