Prevention of DDoS Attacks Targeting Financial Services using Supervised Machine Learning and Stacked LSTM

Kathirkamanathan, Nisanthan; Thevarasa, Bahinthan; Mahadevan, Gukajan; Skandhakumar, Nimalaprakasan; Kuruwitaarachchi, N.

doi:10.1109/i2ct54291.2022.9825228

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…A lower MSE indicates higher precision and accuracy in the model's predictions. In the following formulas (11)-formulas (13), 𝑦 𝑖 is the true value of the 𝑖 𝑡ℎ data point, and the 𝑦 ̂𝑖 is the predicted value of the 𝑖 𝑡ℎ data point, and n is the number of all data points in the dataset. The calculation formula for MSE is as follows:…”

Section: Evaluation Metricmentioning

confidence: 99%

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Goldman Sachs’s Price Forecast Based on ARIMA and LSTM

Yang

2024

HBEM

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The prediction of stock prices is a common and crucial problem in trading. Correctly predicting future stock prices enables traders to determine the optimal time to buy and sell stocks, increasing the probability of making profits. This study focuses on predicting the closing price of Goldman Sachs. Initially, an ARIMA (4,1,6) benchmark model was established based on the AIC information criteria for time series prediction. The model was then applied to make forward predictions. Subsequently, a two-layer LSTM model was constructed. The prediction results of both models were visualized, and the regression indicators were calculated for each model. By comparing the prediction results of the two algorithms, it was determined that LSTM model outperforms ARIMA on the dataset used in this paper. Furthermore, this paper highlights some shortcomings of the ARIMA model, including its unsuitability for long-term forecasting and its subjective parameter selection. In contrast, LSTM performs better in predicting turning points in stock prices.

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Section: Evaluation Metricmentioning

confidence: 99%

“…However, these previous investigations have certain limitations. To address these gaps, this paper employs both integrated and moving average (ARIMA) [12] and Long Short-Term Memory (LSTM) [13] models as forecasting techniques. The experimental result indicate that LSTM is more accurate in predicting stock price.…”

Section: Introductionmentioning

confidence: 99%