scholarly journals Reduct ECOC Framework for Network Intrusion Detection System

2020 ◽  
Vol 9 (3) ◽  
pp. 258-266
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Class Imbalance ◽  
Classification Problem ◽  
Network Intrusion ◽  
Wide Range ◽  
Multi Class Classification ◽  
Network Technologies ◽  
Better Than

Now a day’s network security is major concern for e-government and e-commerce applications. A wide range of malicious activities are increasing with the usage of internet and network technologies. Identifying novel threats and finding modern solutions for network to prevent from these threats are important. Designing an effective intrusion detection system is significant to continuously look out the network activities to efficiently thwart malicious attacks or to identify the intruders. To tackle multi class imbalance classification problem in networks, a reduct based ECOC ensemble framework for NIDS is proposed to efficiently identify attacks in a multi class scenario. The Reduct-ECOC classifier is validated on highly imbalanced benchmark NSL-KDD intrusion datasets as well as other UCI-ML datasets. The experimental results on eight highly imbalanced datasets show that Reduct-ECOC classifier performs better than many other state-of-art multi-class classification ECOC learning methods.

scholarly journals Network Intrusion Detection System using XG Boost

2019 ◽  
Vol 9 (1) ◽  
pp. 4070-4073
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Personal Privacy ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
The World ◽  
Better Than

Internet is the most widely used commodity throughout the world. Such widescale adoption of internet has resulted in drastic developments across various facets of life. Several studies indicate a surge in cybercrimes including incidents of personal privacy thefts. Network intrusion is any illegitimate and/or unidentified activity taking place over a network. So, an effective intrusion detection system is required to be developed. Through this paper, we propose an intrusion detection system that uses XG Boost algorithm to detect intrusions. To implement this approach, KDD-99 dataset has been used for inputs. This paper demonstrates that the efficiency and accuracy of intrusion detection system deployed using XG Boost algorithm is better than contemporary algorithms.

An Entropy-Based Architecture for Intrusion Detection in LAN Traffic

2011 ◽  
pp. 94-121
Author(s):  
P. Velarde-Alvarado ◽  
A. Martinez-Herrera ◽  
C. Vargas-Rosales ◽  
D. Torres-Roman
Keyword(s):  
Early Detection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Anomalous Behavior ◽  
Primary Concern ◽  
System A ◽  
Network Intrusion ◽  
Normal Behavior ◽  
Wide Range

Information security has become a primary concern in enterprise and government networks. In this respect, Network-based Intrusion Detection System (NIDS) is a critical component of an organization’s security strategy. This chapter is the result of the effort to design an Anomaly-based Network Intrusion Detection System (A-NIDS), which is capable of detecting network attacks using entropy-based behavioral traffic profiles. These profiles are used as a baseline to define the normal behavior of certain traffic features. The Method of Remaining Elements (MRE) is the core for the task of traffic profiling. In this method, a new measure of uncertainty called Proportional Uncertainty (PU) is proposed, which provides an important characteristic: the exposure of anomalies for those traffic slots related to anomalous behavior. Moreover, PU increases the sensitivity for early detection, and allows detection of a wide range of attacks with respect to naïve entropy estimation. The performance evaluation of the proposed architecture was accomplished through MIT-DARPA dataset and also on an academic LAN by implementing real attacks. The results show that this architecture is effective in the early detection of intrusions, as well as some attacks designed to bypass detection measures.

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

2020 ◽  
Vol 38 (1B) ◽  
pp. 6-14
Author(s):  
ٍٍSarah M. Shareef ◽  
Soukaena H. Hashim
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Multinomial Logistic Regression ◽  
Network Intrusion Detection ◽  
Limiting Factor ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Normal Behavior

Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate.

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