Security Optimization Management for loT-Assisted Bank Liquidity Risk Emergency Using Big Data Analytic-Based Case Reasoning

Abstract

In modern times, financial institutions are the core carrier of efficient operation of financial markets. With the continuous development of financial models such as IoT (Internet of Things) finance, commercial banks have made many attempts in the integration and innovation of finance and logistics, but they have also increased the types and complexity of risks they face while improving financing efficiency. It has the characteristics of great destructiveness, strong infectivity, and high complexity. The establishment of a perfect emergency security optimization management for early warning of bank liquidity risk is an important part of timely detection and effective management of liquidity risk. In order to enable decision makers to accurately use the effective disposal of similar liquidity risk emergencies as a reference for decision-making, this paper studies the generation method of emergency security plan for bank liquidity risk using big data analytic-based case reasoning. Firstly, analyze the characteristics of various types of bank liquidity emergencies a, and identify the key risk points, and form the accident case database. Secondly, carry out the interval division according to the different numerical distribution characteristics of the indicators, and calculate the repeatability of the involved stages. Finally, calculate the comprehensive similarity to obtain the emergency security plan for reference. At the same time, taking A commercial bank as an example, verify the effectiveness of the method by using the constructed case-based reasoning model to generate emergency security plans intelligently. It provides reference for commercial bank liquidity risk management.

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

“…The models required high computational power and specialized hardware, e.g., they needed a good GPU to accomplish the training process. [39] ANN Model 83.5% IoT Banking Devices Datasets deh et al [40] CNN-LSTM 78%, 79% Banking Fraud Time Series Data et al [41] SVM 86.7% DDoS Datasets a et al [42] Trees 85.55% DDoS Datasets ani, et al [43,44] ML(KNN,SVM,ANN) 83%,84%,81% Banking Datasets n et al [45] GRU 81.7% DDoS Datasets et al [46] ANN, SVM 88.5%, 91% Real Time Dataset…”