A Trust Aware Behavioral Based Intrusion Detection in Cloud Environment Using Ensemble Service Centric Featured Neural Network

Ahmed, Nasaf; Durga, R.

doi:10.1109/iccct53315.2021.9711827

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analyzing the behavior of a network's users can be very useful for detecting malicious activity, Ahmed and Durga [128] propose an algorithm TABIS to detect the risk level associated with each user and to perform intrusion detection. For selecting the best features of the KDD-Cup dataset used for training the model, an Ensemble Service-Centric Feature Selection algorithm was used, and for the prediction step, a Sigmoid Recurrent neural network was applied.…”

Section: G Work Published In 2021mentioning

confidence: 99%

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Lucas,

de Figueiredo,

Tojeiro

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Machine learning algorithms present a robust alternative for building Intrusion Detection Systems due to their ability to recognize attacks in computer network traffic by recognizing patterns in large amounts of data. Typically, classifiers are trained for this task. Together, ensemble learning algorithms have increased the performance of these detectors, reducing classification errors and allowing computer networks to be more protected. This research presents a comprehensive Systematic Review of the Literature where works related to intrusion detection with ensemble learning were obtained from the most relevant scientific bases. We offer 188 works, several compilations of datasets, classifiers, and ensemble algorithms, and document the experiments that stood out in their performance. A characteristic of this research is its originality. We found two surveys in the literature specifically focusing on the relationship between ensemble techniques and intrusion detection. We present for the last eight years covered by this survey a timeline-based view of the works studied to highlight evolutions and trends. The results obtained by our survey show a growing area, with excellent results in detecting attacks but with needs for improvement in pruning for choosing classifiers, which makes this work unprecedented for this context.

show abstract

Section: G Work Published In 2021mentioning

confidence: 99%