[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122885-en":3,"doc-seo-122885-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122885,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","NIDS: An Efficient Network Intrusion Detection Model for Security of Big Data Using Different Machine Learning classifiers - Research Overview","Big data security is addressed by designing an efficient network intrusion detection approach to identify unauthenticated intruders and invalid packets during data access. The method uses machine learning classifiers and trains on the KDD intrusion dataset to distinguish multiple types of network traffic intrusions. Packet validity detection is emphasized to prevent loss of crucial information. Experiments report performance with a random forest ensemble classifier achieving the highest accuracy relative to prior reported results.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 IssueS-6 Year 2023 Page 1010:1016  \nNIDS: An Efficient Network Intrusion Detection Model for Security of Big Data Using Different Machine Learning classifiers  \nA. P. Bhuvaneswari1 Dr.R.Praveen Sam2 Dr.C.Shoba Bindu3  \n1,3Dept. of Computer Science and Engineering, JNTUA University, Ananthapuramu, A.P, India. 2Dept. of Computer Science and Engineering, G.Pulla Reddy Engineering College, Kurnool, A.P, India.  \n*Corresponding author’s E-mail: A. P. Bhuvaneswari  \n\n| Article History\u003Cbr>Received: 06 June 2023\u003Cbr>Revised: 05 Sept 2023\u003Cbr>Accepted: 30 Nov 2023\u003Cbr>CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>Security of the big data is one of the important challenges which needs to be addressed by designing an efficient network intrusion model for detecting the unauthenticated intruders in the network. The model should be able to detect the validity of the packet. The detection of intrusions in network was already represented by multiple researchers using different algorithms which still needs instant addressing. Proposing a machine learning classifier algorithm for intrusion detection. The KDD intrusion dataset is used in training the machine for identifying the different intrusions of the network traffic. The machine must be trained efficiently using the different classification algorithms and the security for the data needs to be attained by identifying the invalid network packets. The experimental results demonstrate that the random forest ensemble machine learning classifier is having highest accuracy of 0.2 % when compared with the existing research results in the identification of different intrusions towards the network packets.\u003Cbr>Keywords: Intrusion detection, Network, Machine learning, Classification algorithms, Accuracy |\n| --- | --- |\n\n1. Introduction  \nIn the present days, the security of the data plays an important role which needs to be protected with at most care especially in the case of health information. So, we need an efficient security model which can identify the network packets validity in accessing the data. By using machine learning techniques, we are improving the quality of detection of different intrusions to prevent the loss of crucial data. The machines are trained properly and tested for the detection of invalid packets while accessing the data through the networks.  \nNetwork Security  \nThe computer networks allow a secure end-to-end communication using network infrastructure between the applications. With the huge usage of network many types of attacks are occurring now a days which is challenging the availability, confidentiality, Integrity, Authenticity of the available information. To protect the information the network traffic must be observed and the detection of invalid packets coming from the intruders need to be removed to safeguard the data. For that many security algorithms have been designed for resolving authentication problems. Machines are trained effectively with different classifiers in identifying the invalid packet and tested the accuracy in identifying the intruder packets. Compared with the traditional methods the machine learning algorithms can identifying the intruder packets with high accuracy [1-2] . IDS using machine learning will act as a shield in protecting the network from different attackers and hackers.  \nMachine Learning  \nThe extraction of knowledge from the available data by machines is known as machine learning. The machines can gain the knowledge from the data, identify the existing patterns, and can make decisions with minimal human intervention. Machines need to be trained and tested for the accuracy of the results. Big data is defined to have more volume, velocity, and value. Big data is to be processed for attaining a good quality for training the machine to achieve good accurate results. For accessing the available information more attacks will happen with the network infrastructure which needs to be  \npr","cbCair2poKGmrXlA","https://ap.wps.com/l/cbCair2poKGmrXlA","pdf",422015,1,7,"English","en",105,"# Introduction\n## Network Security\n## Machine Learning\n# Related Works","[{\"question\":\"What problem does the NIDS model aim to solve?\",\"answer\":\"The model targets big data security by detecting unauthenticated intruders and invalid network packets during network access.\"},{\"question\":\"Which dataset is used to train the machine learning model?\",\"answer\":\"The KDD intrusion dataset is used for training to identify different intrusions in network traffic.\"},{\"question\":\"Which machine learning classifier achieved the highest accuracy in the experiments?\",\"answer\":\"The random forest ensemble classifier is reported to have the highest accuracy compared with existing research results.\"}]","NIDS: An Efficient Network Intrusion Detection Model for Security of Big Data Using Different Machine Learning classifiers - 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