A Real-Time Prescriptive Solution for Explainable Cyber-Fraud Detection Within the iGaming Industry

Fuzzy Harmony Search Technique for Cyber Risks in Industry 4.0 Wireless Communication Networks

Industry 4.0 houses diverse technologies including wireless communication and shared networks for internal and external operations. Due to the wireless nature and remote operability, the exposure to security threats is high. Cyber risk detection and mitigation are prominent for secure industrial operations and planned outcomes. In addition, the system faces the threat of intelligence attacks, security standards issues, privacy concerns and scalability problems. The cyber risk related research problems influence overall data transmission in industry wireless communication networks. For augmenting communication security through cyber risk detection, this article introduces an Explicit Risk Detection and Assessment Technique (ERDAT) for cyber threat mitigation in the industrial process. A fuzzy harmony search algorithm powers this technique for identifying the risk and preventing its impact. The harmony search algorithm mimics the adversary impact using production factors such as process interruption or halting and production outcome. The search performs a mimicking operation for a high objective function based on production output for the admitted plan. The fuzzy operation admits the above factors for identifying the cyber impacting risk, either for its impacts or profitable outcome. In this process, the fuzzy optimization identifies the maximum or minimum objective output targeted for either outcome or risk interrupts, respectively. The fuzzy threshold is identified using a mediated acceptable range, computed as the ratio between minimum and maximum, mimicking occurrences between the risk and scheduled production outcomes. Therefore, the mimicking crossing or falling behind the threshold for the interruption/halting or production, respectively, are identified as risks and their source is detected. The detection communication source is disconnected from the industrial process for preventing further adversary impacts. The introduced system achieves 8.52% high-risk detection, 12.5% fewer outcome interrupts, 8.3% fewer halted schedules, 8.08% less interrupt span, and 7.94% less detection time compared to traditional methods.

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

Purpose

The 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/approach

The 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.

Findings

Finally, 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/implications

Additionally, 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 implications

Structured 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/value

The 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.