scholarly journals Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 106576-106584
Author(s):  
Anar A. Hady ◽  
Ali Ghubaish ◽  
Tara Salman ◽  
Devrim Unal ◽  
Raj Jain
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Healthcare Systems ◽  
Network Data ◽  
Comparison Study

A Method of Network Forensics Analysis Based on Frequent Sequence Mining

2011 ◽  
Vol 50-51 ◽  
pp. 578-582
Author(s):  
Xiu Yu Zhong
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Data ◽  
Evidence Based ◽  
Sequence Mining ◽  
Network Forensics ◽  
Frequent Sequence ◽  
Signature Database ◽  
Simulation Results

For the mistaken report and false alarm occurring frequently in intrusion detection system (IDS), the evidence based on forensics system of IDS is inefficient and low credibility. Frequent sequence mining based on Jpcap is proposed for network forensics analysis. After fetching and filtering network data package, the system mines data with frequent sequence according to the evidence relevance to build and update signature database of offense, and judges whether the current user’s behavior is legal in the network forensics analysis stage or not. Simulation results show that the algorithm of frequent sequence mining can identify the new crime behavior and improve the credibility and efficiency of evidence in network forensics analysis.

scholarly journals Increasing Feature Selection Accuracy through Recursive Method in Intrusion Detection System

2019 ◽  
Vol 4 (2) ◽  
pp. 43
Author(s):  
Andreas Jonathan Silaban ◽  
Satria Mandala ◽  
Erwid Mustofa Jadied
Keyword(s):  
Feature Extraction ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Network Data ◽  
Classification Algorithms ◽  
Recursive Method ◽  
Slow Response ◽  
Network Intrusion

Artificial intelligence semi supervised-based network intrusion detection system detects and identifies various types of attacks on network data using several steps, such as: data preprocessing, feature extraction, and classification. In this detection, the feature extraction is used for identifying features of attacks from the data; meanwhile the classification is applied for determining the type of attacks. Increasing the network data directly causes slow response time and low accuracy of the IDS. This research studies the implementation of wrapped-based and several classification algorithms to shorten the time of detection and increase accuracy. The wrapper is expected to select the best features of attacks in order to shorten the detection time while increasing the accuracy of detection. In line with this goal, this research also studies the effect of parameters used in the classification algorithms of the IDS. The experiment results show that wrapper is 81.275%. The result is higher than the method without wrapping which is 46.027%.

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