Real time intrusion detection system for ultra-high-speed big data environments

2016 ◽  
Vol 72 (9) ◽  
pp. 3489-3510 ◽  
Author(s):  
M. Mazhar Rathore ◽  
Awais Ahmad ◽  
Anand Paul
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
High Speed ◽  
Detection System ◽  
Ultra High Speed

scholarly journals Enhanced Tree Based Real Time Intrusion Detection System in Big Data

2016 ◽  
Vol 15 (3) ◽  
pp. 6563-6569
Author(s):  
S.J.SATHISH AARON JOSEPH ◽  
R. BALASUBRAMANIAN
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Digital Form ◽  
Hadoop Cluster

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.

Efficient Intrusion Detection for High-Speed Networks

2012 ◽  
Vol 263-266 ◽  
pp. 2915-2919
Author(s):  
Gao Long Ma ◽  
Wen Tang
Keyword(s):  
Intrusion Detection ◽  
Error Detection ◽  
Intrusion Detection System ◽  
High Speed ◽  
Detection System ◽  
Heavy Traffic ◽  
High Speed Networks ◽  
Network Intrusion ◽  
Traffic Loads ◽  
Rule Sets

With the great increasing of high-speed networks,the traditional network intrusion detection system(NIDS) has a serious problem with handling heavy traffic loads in real-time ,which may result in packets loss and error detection . In this paper we will introduce the efficient load balancing scheme into NIDS and improve rule sets of the detection engine so as to make NIDS more suitable to high-speed networks environment.

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.

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