Intrusion Detection System Using New Ensemble Boosting Approach

Dongre, Snehlata; Wankhade, Kapil

doi:10.7763/ijmo.2012.v2.168

Cited by 15 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is true that the security of machines located on a site is provided locally at the same site, but the task of ensuring the security of resources at the level of the distributed system is the responsibility of the resource management system and scheduling. Therefore, any ideal resource management and scheduling system should provide the necessary security of access to the covered resources at the level of the distributed system [14,15].…”

Section: Related Work Of Anomaly Detection Techniquesmentioning

confidence: 99%

Development of an Anomaly Detection Algorithm in Distributed Systems based on Runtime and System Status

Asgarnezhad

alwali

Alsaedi

et al. 2022

WJCMS

View full text Add to dashboard Cite

Computational grades have emerged as a new approach to solving large-scale problems in the fields of science, engineering, and commerce. The computing grid is a hardware and software infrastructure that provides affordable, reliable, comprehensive, and affordable access to the computational abilities of others. A computational grid is associated with a set of resources on a large scale. Computational grades have emerged as a new approach to solving large-scale problems in the fields of science, engineering, and commerce. The computing grid is a hardware and software infrastructure that provides affordable, reliable, comprehensive, and affordable access to the computational abilities of others. A computational grid is associated with a set of resources on a large scale. The purpose of this thesis is to provide a method that optimizes resource management and scheduling in at least one direction. The main focus of the research is on the time criterion, the deadline for doing things and receiving the response from the grid are parameters that can be examined. From a more general perspective, the aim of the research is to get the answer as quickly as possible from the calculation grid. The proposed algorithm improves the scheduling and resource management of the grid in the direction of improvement, and the structure and form of this problem have not yet been resolved. The proposed solution has been considered hypothesis and removed some of the definitions of grid scheduling, such as cost, quality of service, architectures, and others, but ultimately heed it and timed the grid in some ways.

show abstract

Section: Related Work Of Anomaly Detection Techniquesmentioning

confidence: 99%

Development of an Anomaly Detection Algorithm in Distributed Systems based on Runtime and System Status

Asgarnezhad

alwali

Alsaedi

et al. 2022

WJCMS

View full text Add to dashboard Cite

show abstract

“…A novel proficiency-based detection with effective safeguards are incorporated using Random Forest and Decision Tree algorithm for intrusion detection [2]. Random Forest and Decision Tree procedure is a frequently used machine learning and statistics withdrawal method, thus effective in deception discovery, configuration appreciation and outlier recognition [3]. The serious issue is focused towards normal data to train and give a new piece of test data to find the exact location of intrusion [4].…”

Section: Introductionmentioning

confidence: 99%

Host Intrusion Detection System Using Novel Predefined Signature Patterns by Comparing Random Forest over Decision Tree Algorithm

Chowdary

Vinod

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

View full text Add to dashboard Cite

The objective of the work is to identify anomaly intrusion detection in a user network environment using information accessing and retrieval from homogeneous network databases. Here machine learning algorithms namely Random Forest and Decision Tree are been used to categorize passive and active attack. To accomplish focused accuracy, sample size of n=10 in Random Forest and n=10 in Decision Tree was repeated for 20 intervals for well-organized and precise investigation on categorized images with G power in 80% and threshold 0.05%, CI 95% mean and standard deviation. The existing works proves the sequential implemented in focus to intrusion detection, while comparing Random Forest and Decision Tree has classified and predicted the values from the network intrusion to generate accuracy with Random Forest has higher accuracy (76.37%) compared to Decision Tree accuracy (71.57%) with a significance of P<0.001 (2-tailed). Prediction in identifying anomaly intrusion detection systems shows that Random Forest has higher accuracy over Decision Tree.

show abstract

“…Misclassification of minority samples has very high risk in the field of Medical Science. Likewise the intrusion detection [2], fault detection [3], anomaly detection [4], detection of fraudulent telephone calls etc. are the other examples of imbalanced datasets.…”

Section: Introductionmentioning

confidence: 99%

Rare Class Problem in Data Mining: Review

Dongre

2017

ijarcs

View full text Add to dashboard Cite

Class imbalance problem is getting so much attention of researchers now a days. In real life there are number of applications that generates imbalanced data sets. Imbalance nature of data makes classification task difficult. Dealing with these kinds of imbalanced dataset is the one of the biggest challenge in the data mining. Imbalanced dataset means the ratio of positive and negative classes is not balanced. The class that is having more number of samples is known as majority class and the class that is having less number of samples is known as minority class samples. Minority class samples are less but important. In the classification task, most of the times, we are ignoring minority class samples and more concentrating on majority class samples. This leads to good overall accuracy but poor minority class detection rate. Many algorithms have been proposed to deal with the imbalanced data problem but each has its prons and corns. Different techniques used for handling imbalance data are discussed here.

show abstract

Intrusion Detection System Using New Ensemble Boosting Approach

Cited by 15 publications

References 13 publications

Development of an Anomaly Detection Algorithm in Distributed Systems based on Runtime and System Status

Development of an Anomaly Detection Algorithm in Distributed Systems based on Runtime and System Status

Host Intrusion Detection System Using Novel Predefined Signature Patterns by Comparing Random Forest over Decision Tree Algorithm

Rare Class Problem in Data Mining: Review

Contact Info

Product

Resources

About