Interest emotion recognition approach using self-organising map and motion estimation

Belhouchette, Kenza; Bahaj, Mohamed; Belhadef, Hacene

doi:10.1504/ijista.2019.10022616

Cited by 2 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data on cybercrimes in banks of Canada are analyzed with KNN and DTs with a level of accuracy between 39% and 44% (Park and Kim, 2020). In a study by Baesens et al (2021) and Belhouchette et al (2019), it was found that cybercrime data of 19 types occurred in San Francisco banks; logistic regression (LR), DTs, RF, support vector machine (SVM) and Bayesian methods were used to classify these crimes into blue/white-collar crime and was later prioritized in order of importance to predict the crime (Iznova, 2013; Wang, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable response system building against insider-led cyber frauds in banking sector: a machine learning approach

Roy

Prabhakaran

2022

JFC

View full text Add to dashboard Cite

Purpose This paper aims to focus on the different types of insider-led cyber frauds that gained mainstream attention in recent large-scale fraud events involving prominent Indian banking institutions. In addition to identifying and classifying cyber fraud, the study maps them on a severity scale for optimal mitigation planning. Design/methodology/approach The methodology used for identification and classification is an analysis of a detailed literature review, a focus group discussion with risk and vigilance officers and cyber cell experts, as well as secondary data of cyber fraud losses. Through machine learning-based random forest, the authors predicted the future of insider-led cyber frauds in the Indian banking business and prioritized and predicted the same. The projected future reveals the dominance of a few specific cyber frauds, which will make it easier to develop a fraud mitigation model based on a victim-centric approach. Findings The paper concludes with a conceptual framework that can be used to ensure a sustainable cyber fraud mitigation ecosystem within the scope of the study. By using the findings of this research, policymakers and fraud investigators will be able to create a more robust environment for banks through timely detection of cyber fraud and prevent it appropriately before it happens. Research limitations/implications The study focuses on fraud, risk and mitigation from a victim-centric perspective and does not address it from the fraudster’s perspective. Data availability was a challenge. Banks are recommended to compile data that can be used for analysis both by themselves and other policymakers. Practical implications The structured, sustainable cyber fraud mitigation suggested in the study will provide an agile, quick, proactive, stakeholder-specific plan that helps to safeguard banks, employees, regulatory authorities, customers and the economy. It saves resources, cost and time for bank authorities and policymakers. The mitigation measures will also help improve the reputational status of the Indian banking business and prolong the banks’ sustenance. Originality/value The innovative cyber fraud mitigation approach contributes to the sustainability of a bank’s ecosystem quickly, proactively and effectively.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Sustainable response system building against insider-led cyber frauds in banking sector: a machine learning approach

Roy

Prabhakaran

2022

JFC

View full text Add to dashboard Cite

show abstract

“…Studies (Baesens et al. , 2021; Belhouchette et al. , 2019) showed that there were 19 cybercrimes in San Francisco banks.…”

Section: Literature Review and Theoretical Frameworkmentioning

confidence: 99%

“…(2021) used numerical and textual information for frauds prioritization in European banks. Belhouchette et al. (2019) compared the effectiveness of different cyber frauds using neural networks, SVMs, LR and genetic programming.…”

Section: Literature Review and Theoretical Frameworkmentioning

confidence: 99%

“…The accuracy of k-NNs and DTs for analyzing cybercrimes in Canadian banks is between 39 and 44% (Park and Kim, 2020). Studies (Baesens et al, 2021;Belhouchette et al, 2019) showed that there were 19 cybercrimes in San Francisco banks. These were further classified into blue-collar or white-collar crimes using logistic regression (LR), DT, random forest (RF) and support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

“…Sunrise and Elizabeth (2019) used multiple features of selection methods; Çetinkaya and Damar (2019) and Durlauf and Quah (1999) used self-organizing maps (SOM); Hidayati et al (2021) used numerical and textual information for frauds prioritization in European banks. Belhouchette et al (2019) compared the effectiveness of different cyber frauds using neural networks, SVMs, LR and genetic programming. Soltani Halvaiee and Akbari (2014) investigated credit card fraud and recommended a solution to improve accuracy, hit rate and false-positive rate.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Internal-led cyber frauds in Indian banks: an effective machine learning–based defense system to fraud detection, prioritization and prevention

Roy

Prabhakaran²

2022

AJIM

View full text Add to dashboard Cite

PurposeThe study aims to overview the different types of internal-led cyber fraud that have gained mainstream attention in recent major-value fraud events involving prominent Indian banks. The authors attempted to identify and classify cyber frauds and its drivers and correlate them for optimal mitigation planning.Design/methodology/approachThe methodology opted for the identification and classification is through a detailed literature review and focus group discussion with risk and vigilance officers and cyber cell experts. The authors assessed the future of cyber fraud in the Indian banking business through the machine learning–based k-nearest neighbor (K-NN) approach and prioritized and predicted the future of cyber fraud. The predicted future revealing dominance of a few specific cyber frauds will help to get an appropriate fraud prevention model, using an associated parties centric (victim and offender) root-cause approach. The study uses correlation analysis and maps frauds with their respective drivers to determine the resource specific effective mitigation plan.FindingsFinally, the paper concludes with a conceptual framework for preventing internal-led cyber fraud within the scope of the study. A cyber fraud mitigation ecosystem will be helpful for policymakers and fraud investigation officers to create a more robust environment for banks through timely and quick detection of cyber frauds and prevention of them.Research limitations/implicationsAdditionally, the study supports the Reserve Bank of India and the Government of India's launched cyber security initiates and schemes which ensure protection for the banking ecosystem i.e. RBI direct scheme, integrated ombudsman scheme, cyber swachhta kendra (botnet cleaning and malware analysis centre), National Cyber Coordination Centre (NCCC) and Security Monitoring Centre (SMC).Practical implicationsStructured and effective internal-led plans for cyber fraud mitigation proposed in this study will conserve banks, employees, regulatory authorities, customers and economic resources, save bank authorities’ and policymakers’ time and money, and conserve resources. Additionally, this will enhance the reputation of the Indian banking industry and extend its lifespan.Originality/valueThe innovative insider-led cyber fraud mitigation approach quickly identifies cyber fraud, prioritizes it, identifies its prominent root causes, map frauds with respective root causes and then suggests strategies to ensure a cost-effective and time-saving bank ecosystem.

show abstract

Interest emotion recognition approach using self-organising map and motion estimation

Cited by 2 publications

References 0 publications

Sustainable response system building against insider-led cyber frauds in banking sector: a machine learning approach

Sustainable response system building against insider-led cyber frauds in banking sector: a machine learning approach

Internal-led cyber frauds in Indian banks: an effective machine learning–based defense system to fraud detection, prioritization and prevention

Contact Info

Product

Resources

About