scholarly journals An Analysis of AI-based Supervised Classifiers for Intrusion Detection in Big Data

2018 ◽  
pp. 26-46
Author(s):  
Gulshan Kumar ◽  
Kutub Thakur
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Supervised Classifiers

A hybrid deep learning model for efficient intrusion detection in big data environment

2020 ◽  
Vol 513 ◽  
pp. 386-396 ◽  
Author(s):  
Mohammad Mehedi Hassan ◽  
Abdu Gumaei ◽  
Ahmed Alsanad ◽  
Majed Alrubaian ◽  
Giancarlo Fortino
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Learning Model ◽  
Data Environment ◽  
Deep Learning Model

scholarly journals Intrusion Detection Based on Big Data Fuzzy Analytics

2021 ◽  
Author(s):  
Farah Jemili ◽  
Hajer Bouras
Keyword(s):  
Big Data ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
High Performance ◽  
Detection System ◽  
Training Dataset ◽  
False Alarms ◽  
Massive Datasets ◽  
Detection Rates

In today’s world, Intrusion Detection System (IDS) is one of the significant tools used to the improvement of network security, by detecting attacks or abnormal data accesses. Most of existing IDS have many disadvantages such as high false alarm rates and low detection rates. For the IDS, dealing with distributed and massive data constitutes a challenge. Besides, dealing with imprecise data is another challenge. This paper proposes an Intrusion Detection System based on big data fuzzy analytics; Fuzzy C-Means (FCM) method is used to cluster and classify the pre-processed training dataset. The CTU-13 and the UNSW-NB15 are used as distributed and massive datasets to prove the feasibility of the method. The proposed system shows high performance in terms of accuracy, precision, detection rates, and false alarms.

Big Data Analytics for Intrusion Detection

2020 ◽  
pp. 292-316 ◽  
Author(s):  
Luis Filipe Dias ◽  
Miguel Correia
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Semantic Gap ◽  
Behavioral Analysis ◽  
Event Management ◽  
Security Monitoring ◽  
Hidden Correlations ◽  
Security Information

Intrusion detection has become a problem of big data, with a semantic gap between vast security data sources and real knowledge about threats. The use of machine learning (ML) algorithms on big data has already been successfully applied in other domains. Hence, this approach is promising for dealing with cyber security's big data problem. Rather than relying on human analysts to create signatures or classify huge volumes of data, ML can be used. ML allows the implementation of advanced algorithms to extract information from data using behavioral analysis or to find hidden correlations. However, the adversarial setting and the dynamism of the cyber threat landscape stand as difficult challenges when applying ML. The next generation security information and event management (SIEM) systems should provide security monitoring with the means for automation, orchestration and real-time contextual threat awareness. However, recent research shows that further work is needed to fulfill these requirements. This chapter presents a survey on recent work on big data analytics for intrusion detection.

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