Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application

Abstract

Cyber security comes with a combination of various security policies, AI techniques, network technologies that work together to protect various computing resources like computing networks, intelligent programs, and sensitive data from attacks. Nowadays, the shift to digital freedom had led to opened many new challenges for financial services. Cybercriminals have found the ability to leverage e- currency exchanges and other financial transactions to perform their fraudulent activities. The unregulated channel makes it essential for banks and financial institutions to deploy advanced AI & ML (DL) techniques to fight cybercrime. This can be implemented by deploying AI & ML (DL) techniques. Customers are experiencing an increase in the fraud-hit rate in financial banking operations. It is difficult to defend against dynamic cyber-attacks using conventional non- dynamic algorithms. Therefore, AI with machine learning techniques has been set up with cyber security to build intelligent models for malware categorization & intelligently sensing the fraught with danger. This paper introduces the cyber security defense mechanism by using artificial intelligence (AI), machine learning (ML)) techniques with the current Feedzai security model to identifying fraudulent banking transaction. We have given a preface to the popular ML & AI model with random forest algorithm and Feedzai’s Open ML fraud detection software tool, which provides automatic fraud-recognition to the current intelligent framework for solving Financial Fraud Detection.

Next-Generation Cyber Threat Detection and Mitigation Strategies: A Focus on Artificial Intelligence and Machine Learning

“…Security Information and Event Management (SIEM) Systems: SIEM systems gather, assess, and correlate data from a myriad of sources, comprising alerts and logs developed by security solutions. They offer a centralized forum for regulating and resolving security incidents (Raghavendran, 2022). While SIEMs provide valuable insights and reporting capacities, they are not always real-time resolutions and may struggle with the high volume of data produced by contemporary networks, possibly missing threats that require immediate attention (Islam et al, 2022).…”

“…Supervised Learning is an extensive machine learning methodology that facilitates the categorization and processing of data. It highly depends on the employment of labeled datasets, where every single input data point is designated a corresponding label signifying if it belongs to a "good" or "bad" category (Raghavendran, 2022). This labeled information acts as the premise for equipping a model to make accurate classifications or predictions on new, unseen data.…”

“…The power of supervised learning lies in its capability to classify and discern data based on pre-defined classification. By offering candid labels to the training data, the model learns to pinpoint specific trends, attributes, or consolidation thereof that are indicative of the desired category (Raghavendran, 2022). This facilitates the framework to make informed decisions when provided with new, unlabeled data points, accurately classifying them as either "good" or "bad" based on the learned patterns.…”

ANN and RSM based Modeling of Moringa Stenopetala Seed Oil Extraction: Process Optimization and Oil Characterization

“…The weight of the MSO samples was measured both before and after undergoing a drying process in an oven at a temperature of 105 oC for a duration of 3 hours. The procedure for determining the percentage of moisture content was conducted in accordance with the AOAC procedure and the equation ( 2) [17] was utilised.…”

“…The Kjeldhal apparatus (D-40599 Moldel, Behr Labour Technik, Germany) was utilised in the study. The crude fibre content in the oil was determined through a process involving treatment with H2SO4 (1.25%) and NaOH (1.25%) solutions, utilising the Labconco Fibertech equipment from Labconco Corporation in Kansas, USA [19].…”