Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nowadays, information plays a significant role in the enterprise organizations. Sensitive and vital data have a key character in organizing and storing within the database. Traditional mechanisms such as encryption, access control, and authentication cannot provide a high level of confidence. The existence of Intrusion Detection Systems in the Data-Base (DB-IDS) is a necessity because enterprises awash in data often struggle to ansr basic question about detecting or preventing all facet of their threat. ,  propose a novel type of intrusion detection system for detecting attacks in both database transaction level and inter-transaction level (user task level) in a high-rate transaction processing. For simplicity, our model is divided into two parts; detection method at transaction level and inter-transaction level (detection with learning method, concurrently in both level). Detection method at transaction level is based on describing the expected (normal) transactions within the database applications. This is also focused on anomaly detection and used data mining to find dependency and sequence rules (the effect of spatial and temporal heterogeneity) in where inter-transaction level is used. Also, it gains advantages of a hybrid method, including specification-based detection and anomaly detection, to minimize both false positive and false negative errors. Simulation and implementation experiments in Mobile Telecommunication Company of Iran (MCI) revealed the accuracy of our models. The experiment results demonstrated the true positive detection rate is higher than 0.8, and the false positive detection rate is lor than 0.1 choosing appropriate ranges for support and confidence thresholds. The experimental evaluation results show high accuracy and effectiveness of the proposed system.
['Mostafa Doroudian', 'Narges Arastouie', 'Mohammad Talebi', 'Ali Reza Ghanbarian']
I am writing a research paper on a idea Multilayered database intrusion detection system for detecting malicious behaviors in big data transaction
[{"from": "human", "value": "I am writing a research paper on a idea Multilayered database intrusion detection system for detecting malicious behaviors in big data transaction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nowadays, information plays a significant role in the enterprise organizations. Sensitive and vital data have a key character in organizing and storing within the database. Traditional mechanisms such as encryption, access control, and authentication cannot provide a high level of confidence. The existence of Intrusion Detection Systems in the Data-Base (DB-IDS) is a necessity because enterprises awash in data often struggle to ansr basic question about detecting or preventing all facet of their threat. ,  propose a novel type of intrusion detection system for detecting attacks in both database transaction level and inter-transaction level (user task level) in a high-rate transaction processing. For simplicity, our model is divided into two parts; detection method at transaction level and inter-transaction level (detection with learning method, concurrently in both level). Detection method at transaction level is based on describing the expected (normal) transactions within the database applications. This is also focused on anomaly detection and used data mining to find dependency and sequence rules (the effect of spatial and temporal heterogeneity) in where inter-transaction level is used. Also, it gains advantages of a hybrid method, including specification-based detection and anomaly detection, to minimize both false positive and false negative errors. Simulation and implementation experiments in Mobile Telecommunication Company of Iran (MCI) revealed the accuracy of our models. The experiment results demonstrated the true positive detection rate is higher than 0.8, and the false positive detection rate is lor than 0.1 choosing appropriate ranges for support and confidence thresholds. The experimental evaluation results show high accuracy and effectiveness of the proposed system."}]