Real Time Intrusion Detection System for Big Data

Intrusion detection is one of the major necessities of the current networked environment, where every information is available in its corresponding digital form. This paper presents an enhanced tree based approach that can be used to perform intrusion detection faster and with better accuracy. The training data is subject to the random forest algorithm. This algorithm is a combination of tree predictors, and each tree depends upon the random vector generated. Spark based implementations of the Random Forest algorithm is used in a Hadoop cluster on datasets with varied imbalance to obtain the results. It has been observed that the classifier provided results in real time with an accuracy >90%, hence is more appropriate for online intrusion detection.

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A Model-Based Real-Time Intrusion Detection System for Large Scale Heterogeneous Networks

10.21236/ada420824 ◽

2003 ◽

Cited By ~ 1

Author(s):

Richard A. Kemmer ◽

Giovanni Vigna

Keyword(s):

Intrusion Detection ◽

Real Time ◽

Heterogeneous Networks ◽

Intrusion Detection System ◽

Large Scale ◽

Detection System ◽

Model Based

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Machine Learning-Based Intrusion Detection System for Big Data Analytics in VANET

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) ◽

10.1109/vtc2021-spring51267.2021.9448878 ◽

2021 ◽

Author(s):

Mingyuan Zang ◽

Ying Yan

Keyword(s):

Machine Learning ◽

Big Data ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Data Analytics ◽

Detection System ◽

Big Data Analytics

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Big Data Analytic for Intrusion Detection System

2018 International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) ◽

10.1109/icecocs.2018.8610578 ◽

2018 ◽

Author(s):

Merieme Britel

Keyword(s):

Big Data ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Data Analytic

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Real-time Support Vector Machine based Network Intrusion Detection system using Apache Storm

2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) ◽

10.1109/iemcon.2016.7746264 ◽

2016 ◽

Cited By ~ 5

Author(s):

Muhammad Asif Manzoor ◽

Yasser Morgan

Keyword(s):

Support Vector Machine ◽

Intrusion Detection ◽

Real Time ◽

Intrusion Detection System ◽

Detection System ◽

Network Intrusion Detection ◽

Support Vector ◽

Network Intrusion ◽

Network Intrusion Detection System ◽

Apache Storm

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Intrusion Detection Based on Big Data Fuzzy Analytics

10.5772/intechopen.99636 ◽

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.

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