2020
DOI: 10.1007/s11227-020-03233-x
|View full text |Cite
|
Sign up to set email alerts
|

A smart adaptive particle swarm optimization–support vector machine: android botnet detection application

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
5
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
7
1
1

Relationship

0
9

Authors

Journals

citations
Cited by 14 publications
(5 citation statements)
references
References 31 publications
0
5
0
Order By: Relevance
“…Moodi et al [31], presented an approach to detect Android botnets based on traffic features. Their method was based on SVM where a new approach called smart adaptive particle swarm optimization support vector machine (SAPSO-SVM) is developed to adapt the parameters of the optimization algorithm.…”
Section: Botnet Detection On Androidmentioning
confidence: 99%
“…Moodi et al [31], presented an approach to detect Android botnets based on traffic features. Their method was based on SVM where a new approach called smart adaptive particle swarm optimization support vector machine (SAPSO-SVM) is developed to adapt the parameters of the optimization algorithm.…”
Section: Botnet Detection On Androidmentioning
confidence: 99%
“…The system achieved 97.2% accuracy on 1800 ISCX botnet applications and 3650 benign application. Moodi et al [16], utilized traffic features for the detection of Android botnets based on SVM. They presented an approach called smart adaptive particle swarm optimization support vector machine (SAPSO-SVM) based on the top 20 traffic features from the 28-SABD Android botnet dataset.…”
Section: Related Workmentioning
confidence: 99%
“…On Permission, NB produced the best results among the other classifiers. Both [21] and [23] employed the Information Gain (IG) feature selection algorithm on statically extracted features, whereas [13] used and emphasized the merits of the Fuzzy SAPSO selection algorithm on dynamically generated features. Another work that employed a feature selection technique is [20].…”
Section: A Ml-based Detectionmentioning
confidence: 99%
“…The primary detecting feature of [18], [28], [29], [44], and [13] was Network Traffic. The researchers in [18] selected the top four features based on the [46] study and then added seven additional features based on the accuracy of their own assessment.…”
Section: A Ml-based Detectionmentioning
confidence: 99%