Dr. Ifeoma Nwafor: Cybercrime and the law: issues and developments in Nigeria. (2022) CLDS Publishing. pp. 1–285

Mtuze, Sizwe Snail ka

doi:10.1365/s43439-023-00080-3

Cited by 2 publications

(3 citation statements)

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“…− o (output gate) controls the output from the memory cell (5). − C represents the cell state (6). − H illustrates the hidden state (7).…”

Section: Rnn and Lstmmentioning

confidence: 99%

“…Pieterse [5] highlights that public and private enterprises, as well as municipalities, are commonly targeted by ransomware attacks in South Africa. An example of this is the Department of Justice, which experienced its third ransomware attack in 2023, following a previous incident in 2020 [6]. These attacks have resulted in significant financial losses for various South African companies.…”

Section: Introductionmentioning

confidence: 99%

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Ransomware Detection Using Stacked Autoencoder for Feature Selection

Wa Nkongolo,

Tokmak

2024

IJEEI

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In response to the escalating malware threats, we propose an advanced ransomware detection and classification method. Our approach combines a stacked autoencoder for precise feature selection with a Long Short-Term Memory classifier which significantly enhances ransomware stratification accuracy. The process involves thorough preprocessing of the UGRansome dataset, training an unsupervised stacked autoencoder for optimal feature selection, and fine-tuning via supervised learning to elevate the Long Short-Term Memory model's classification capabilities. We meticulously analysed the autoencoder's learned weights and activations to pinpoint essential features for distinguishing 17 ransomware families from other malware and created a streamlined feature set for precise classification. Our results demonstrate the exceptional performance of the stacked autoencoder-based Long Short-Term Memory model across the 17 ransomware families. This model exhibits high precision, recall, and F1 score values. Furthermore, balanced average scores affirm its ability to generalize effectively across various malware types. To optimise the proposed model, we conducted extensive experiments, including up to 400 epochs, and varying learning rates and achieved an exceptional 98.5% accuracy in ransomware classification. These results surpass traditional machine learning classifiers. Moreover, the proposed model surpasses the Extreme Gradient Boosting (XGBoost) algorithm, primarily due to its effective stacked autoencoder feature selection mechanism and demonstrates outstanding performance in identifying signature attacks with a 98.5% accuracy rate. This result outperforms the XGBoost model, which achieved a 95.5% accuracy rate in the same task. In addition, a prediction of the ransomware financial impact using the proposed model reveals that while Locky, SamSam, and WannaCry still incur substantial cumulative costs, their attacks may not be as financially damaging as those of NoobCrypt, DMALocker, and EDA2.

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“…− o (output gate) controls the output from the memory cell (5). − C represents the cell state (6). − H illustrates the hidden state (7).…”

Section: Rnn and Lstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ransomware Detection Using Stacked Autoencoder for Feature Selection

Wa Nkongolo,

Tokmak

2024

IJEEI

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“…The study carried by Pieterse (2021) indicated that the South African public sector is the major target of the cyberattackers. Mtuze and Musoni (2023) stated that the combat of cyberfraud under the Cybercrime Act passed in 2021 is more effective and sustainable compared to the era of the common law and ECTA; however, lack of skilled experts in the area of cybercrime investigation and evidence gathering remains a major challenge.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of cyberfraud in the South African banking industry: a multiple regression approach

Akinbowale,

Mashigo,

Zerihun

2023

JFC

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Purpose The purpose of this study is to analyse cyberfraud in the South African banking industry using a multiple regression approach and develop a predictive model for the estimation and prediction of financial losses due to cyberfraud. Design/methodology/approach To mitigate the occurrence of cyberfraud, this study uses the multiple regression approach to correlate the relationship between financial loss and cyberfraud activities. The cyberfraud activities in South Africa are classified into three, namely, digital banking application, online and mobile banking fraud. Secondary data that captures the rate of cyberfraud occurrences within these three major categories with their resulting financial losses were used for the multiple regression analysis that was carried out in the Statistical Package for Social Science (SPSS, 2022 environment). Findings The results obtained indicate that the South African financial institutions still incur significant financial losses due to cyberfraud perpetration. The two main independent variables used to estimate the magnitude of financial loss in the South Africa’s banking industry are online (internet) banking fraud (X2) and mobile banking fraud (X3). Furthermore, a multiple regression model equation was developed for the prediction of financial loss as a function of the two independent variables (X2 and X3). Practical implications This study adds to the literature on cyberfraud mitigation. The findings may promote the combat against cyberfraud in the South Africa’s financial institutions. It may also assist South Africa’s financial institutions to predict the financial loss that financial institutions can incur over time. It is recommended that South Africa’s financial institutions pay attention to these two key variables and mitigate any associated risks as they are crucial in determining their profitability. Originality/value Existing literature indicated significant financial losses to cyberfraud perpetration without establishing any relationship between the magnitude of losses incurred and the prevalent forms of cyberfraud. Thus, the novelty of this study lies in the analysis of cyberfraud in the South African banking industry using a multiple regression approach to link financial losses to the perpetration of the prevalent forms of cyberfraud. It also develops a predictive model for the estimation and projection of financial losses.

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Dr. Ifeoma Nwafor: Cybercrime and the law: issues and developments in Nigeria. (2022) CLDS Publishing. pp. 1–285

Cited by 2 publications

References 0 publications

Ransomware Detection Using Stacked Autoencoder for Feature Selection

Ransomware Detection Using Stacked Autoencoder for Feature Selection

Analysis of cyberfraud in the South African banking industry: a multiple regression approach

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