Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volu...Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and re-al-time data transactions, the stock market has increased vulnerability to at-tacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipula-tions are not available for training. This paper discovers a competitive alterna-tive to the leading approach and investigates the effects of combining AIS with clustering algorithms;Kernel Density Estimation, Self-Organized Maps, Densi-ty-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.展开更多
Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial firm.It is considered a challenging issue in the financial sector.Statistical and machine le...Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial firm.It is considered a challenging issue in the financial sector.Statistical and machine learning(ML)models can be employed for the design of accurate FCP models.Though numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different datasets.This study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for FCP.The major intention of the BSA-FMMNN model is to determine the financial status of a firm or company.The presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity range.Besides,k-medoid clustering approach is employed for the outlier removal process.Finally,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of BSA.The utilization of proficient parameter selection process using BSA demonstrate the novelty of the study.The experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.展开更多
文摘Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and re-al-time data transactions, the stock market has increased vulnerability to at-tacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipula-tions are not available for training. This paper discovers a competitive alterna-tive to the leading approach and investigates the effects of combining AIS with clustering algorithms;Kernel Density Estimation, Self-Organized Maps, Densi-ty-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.
文摘Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial firm.It is considered a challenging issue in the financial sector.Statistical and machine learning(ML)models can be employed for the design of accurate FCP models.Though numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different datasets.This study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for FCP.The major intention of the BSA-FMMNN model is to determine the financial status of a firm or company.The presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity range.Besides,k-medoid clustering approach is employed for the outlier removal process.Finally,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of BSA.The utilization of proficient parameter selection process using BSA demonstrate the novelty of the study.The experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.
