Advanced Methods for Botnet Intrusion Detection Systems

Vuong, Son T.; Alam, M. Shahria

doi:10.5772/15401

Search citation statements

Order By: Relevance

Paper Sections

Select...

Preface1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2014

2021

Publication Types

Select...

Book1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research work presented in Chapter 3 was published in [2,4]. The initial idea to use agent based systems for intrusion detection using machine learning approaches was published in [77].…”

Section: Prefacementioning

confidence: 99%

An Intelligent Multi-Agent Based Detection Framework for Classification of Android Malware

Alam

Vuong

2014

Active Media Technology

View full text Add to dashboard Cite

show abstract

Section: Prefacementioning

confidence: 99%

An Intelligent Multi-Agent Based Detection Framework for Classification of Android Malware

Alam

Vuong

2014

Active Media Technology

View full text Add to dashboard Cite

show abstract

Genetic Algorithm-Conditional Mutual Information Maximization based feature selection for Bot Attack Classification in IoT devices

Kavitha

Elango

2021

JMM

View full text Add to dashboard Cite

The evolution of computing is increasing in a vast manner that will integrate many physical objects and the internet to generate a new interconnection, such as the Internet of Things (IoT). It is estimated that the number of devices that will be interconnected to the internet will be more than trillions until 2025. Due to the lack of interoperability when these devices are interconnected in a vast heterogeneous network, it is tough to define and apply security mechanisms. The IoT networks have been exposed to many vulnerable attacks that disturb the network. Therefore, designing an intrusion detection system that provides additional security tools specific to IoT is needed to apply security mechanisms to detect the attacks in the network. In this paper, we propose a novel hybrid GA-CMIM machine learning algorithm that improves the efficiency in detecting the botnet intrusions with the set of optimal features that are selected from the dataset using a feature selection method.

show abstract

Advanced Methods for Botnet Intrusion Detection Systems

Cited by 2 publications

References 34 publications

An Intelligent Multi-Agent Based Detection Framework for Classification of Android Malware

An Intelligent Multi-Agent Based Detection Framework for Classification of Android Malware

Genetic Algorithm-Conditional Mutual Information Maximization based feature selection for Bot Attack Classification in IoT devices

Contact Info

Product

Resources

About