Cyberattack and Fraud Detection Using Ensemble Stacking

Soleymanzadeh, Raha; Aljasim, Mustafa; Qadeer, Muhammad Waseem; Kashef, Rasha

doi:10.3390/ai3010002

Cited by 19 publications

(11 citation statements)

References 30 publications

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“…Banking Fraud Detection requires [9] a lot of effort as it contains a high risk and impact on reputation. Customer analysis is one of the biggest problems in banking sectors for analyzing the loan defaults or for detecting any fraud transaction, so keeping in mind of all these aspects we used Feedzai model.…”

Section: Supervised Methods For Fraud Detection In Banking Sectors Us...mentioning

confidence: 99%

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

Narsimha¹,

Raghavendran

Rajyalakshmi

et al. 2022

IJEER

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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.

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Section: Supervised Methods For Fraud Detection In Banking Sectors Us...mentioning

confidence: 99%

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

Narsimha¹,

Raghavendran

Rajyalakshmi

et al. 2022

IJEER

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show abstract

“…The same datasets as the baseline models were used for a variety of machine learning algorithms. The best machine learning classifiers for detecting fraud were found to be Random Forest, Logistic Regression, MLP, and Gradient Boosting classifiers [9].…”

Section: Separating Training and Test Datamentioning

confidence: 99%

“…The ensemble is found to be a superior answer to detecting malware pharming attacks. Since it can combine the resemblance in accuracy and several error-detection rate characteristics in picked algorithm [9].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Model for Predicting Social Engineering Pharming Attacks

Njuguna¹,

Kamau²

2023

IJCATR

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Pharming attack has a broad scope as social engineers can masquerade as anyone, particularly during the COVID-19 pandemic from health authorities or even organization executives getting in touch with their personnel. The study aims to develop an ensemble model for predicting social engineering-based pharming attacks from the client-side pharming attack. The target population for the study includes 1781 URLs, which are secondary and readily available on Kaggle having been compiled by Manu Siddhartha. The study focused on identifying URLs that facilitate Pharming attacks, a cybersecurity threat. Malicious URLs miss the protocol segmentation, certificate of authorization, and bait targets into becoming victims of pharming attacks. The research instrument used for data process and building a pharming attack model was CSV and Jupyter notebook. Data was collected from secondary data sources. A model for predictive pharming attacks was built utilizing Logic regression, Random Forest, and gradient boost as the model for boosting algorithms to reduce pharming malware attacks.

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“…Kamal and Abulaish [16] modeled a new Convolutional and Attention with Bi-directional GRU (CAT-BiGRU) method, which has an input layer, embedded layer, convolution layer, Bi-directional GRU (BiGRU) layer, and two attention layers [17]. The convolution layer extracts SDS-related semantic and syntactic characteristics from the embedded layer; the BiGRU layer retrieves contextual data from the features, which are extracted in succeeding and preceding directions; and the attention layers retrieve SDS-related complete context representation from the input text [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection in Online Social Networks

et al. 2022

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Sarcasm detection has received considerable interest in online social media networks due to the dramatic expansion in Internet usage. Sarcasm is a linguistic expression of dislikes or negative emotions by using overstated language constructs. Recently, detecting sarcastic posts on social networking platforms has gained popularity, especially since sarcastic comments in the form of tweets typically involve positive words that describe undesirable or negative characteristics. Simultaneously, the emergence of machine learning (ML) algorithms has made it easier to design efficacious sarcasm detection techniques. This study introduces a new Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection and Classification (HCOA-SACDC) model. The presented HCOA-SACDC model predominantly focuses on the detection and classification of sarcasm in the OSN environment. To achieve this, the HCOA-SACDC model pre-processes input data to make them compatible for further processing. Furthermore, the term frequency–inverse document frequency (TF-IDF) model is employed for the useful extraction of features. Moreover, the stacked autoencoder (SAE) model is utilized for the recognition and categorization of sarcasm. Since the parameters related to the SAE model considerably affect the overall classification performance, the HCO algorithm is exploited to fine-tune the parameters involved in the SAE, showing the novelty of the work. A comprehensive experimental analysis of a benchmark dataset is performed to highlight the superior outcomes of the HCOA-SACDC model. The simulation results indicate that the HCOA-SACDC model accomplished enhanced performance over other techniques.

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Cyberattack and Fraud Detection Using Ensemble Stacking

Cited by 19 publications

References 30 publications

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

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

An Ensemble Learning Model for Predicting Social Engineering Pharming Attacks

Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection in Online Social Networks

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