Botnet detection technique for corporate area network

Savenko, Oleg; Lysenko, Sergii; Kryshchuk, Andrii; Klots, Yuriy

doi:10.1109/idaacs.2013.6662707

Cited by 10 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detection is performed in the situations of priori uncertainty of the botnet presence in the corporate area network with taking into account the botnet demonstrations in the several computer systems available in the network. 43,44 With the usage of fuzzy logic, the analysis of the botnets' actions demonstrations in the situation of the intentionally computer system reconnection is performed. Fuzzy expert system for making conclusion about botnet presence degree in computer systems is developed.…”

Section: Intellectual Methods For Securitymentioning

confidence: 99%

Formal and Intelligent Methods for Security and Resilience: Education and Training Issues

Pomorova¹,

Lysenko²

2016

IISIJ

Self Cite

View full text Add to dashboard Cite

This article presents the results of the implementation of TEMPUS SEREIN project in the Khmelnitsky National University. The main challenge was to develop curriculum and course materials for masters and PhD students. The authors introduce the module description of the course "Formal and Intelligent Methods for Security and Resilience." The module deals with the issues of development and usage of formal methods for designing secure software systems and implementation of formal methods for assuring security of computer networks. We also present some issues on the usage of intelligent systems for security and address the questions of enhancing systems' resilience. The implementation of the developed training course will improve the quality of education and will make graduates more successful on the labour market.

show abstract

Section: Intellectual Methods For Securitymentioning

confidence: 99%

Formal and Intelligent Methods for Security and Resilience: Education and Training Issues

Pomorova¹,

Lysenko²

2016

IISIJ

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is based on network flow identifiers that can track suspicious activities of botnets. In [20][21][22] the methods for botnets detection in corporate area networks (CAN), which include the use of a multi-agent system, are presented. A botnet detection is based on the analysis of the botnets' behavior in the CAN.…”

Section: Related Workmentioning

confidence: 99%

Botnet Detection Approach Based on the Distributed Systems

Savenko¹,

Sachenko²,

Lysenko³

et al. 2020

IJC

Self Cite

View full text Add to dashboard Cite

The paper presents a botnet detection approach for the distributed systems. It is based on the developed three level model, which includes botnet's components: command and control center, control centers, basic elements of the botnet (bots). The novel framework provides the ability to detect known and unknown botnets, and consists of the host and the network levels. At the host level, the detection procedure is based on the implementation of the Bayes classification. The network level extends the results obtained at the host level to the rest of the local area network. Proposed approach provides the exchange of the results obtained by the Bayes classification for further use by other program units of the distributed system. The results of the developed classifier show that representation of the botnets' samples for different classes and subclasses is sufficient for efficient botnet detection. Proposed technique demonstrates promising results concerning botnet detection in the distributed systems.

show abstract

“…Although Botnets are used to carry out such attacks, they are mostly used for spam attacks to steal sensitive information like login credentials and credit card details. Not only this, but they are also used to execute fraudulent attacks on banks and other organisations [6].…”

Section: Botnet Definitionmentioning

confidence: 99%

Similarity Based Instance Transfer Learning for Botnet Detection

Alothman¹

2018

IJICR

View full text Add to dashboard Cite

Botnet Detection has been an active research area over the last few decades. Researchers have been working hard to develop effective techniques to detect Botnets. From reviewing existing approaches, it can be noticed that many of them target specific Botnets and many others try to identify any Botnet activity by analysing network traffic. They achieve this by concatenating existing Botnet datasets to obtain larger datasets, building predictive models, and then employing these models to predict whether network traffic is safe or harmful. The problem with the first approaches is that data is usually scarce and costly to obtain. By using small amounts of data, the quality of predictive models will be questionable. On the other hand, the problem with the second approaches is that it is not always correct to concatenate datasets from different Botnets. Datasets can have different distributions which means they can downgrade the predictive performance of machine learning models. This paper introduces a transfer learning approach that utilises datasets from different but related domains. The idea is instead of concatenating datasets, transfer learning can be used to carefully decide what data to use. The hypothesis is that predictive performance can be improved by using transfer learning across datasets containing network traffic from different Botnets. The approach is compared to a classical open source transfer learning algorithm. Experiments show that the proposed method outperforms this approach and produces higher accuracy. Not only this, but it is also faster which gives it another advantage.

show abstract

Botnet detection technique for corporate area network

Cited by 10 publications

References 5 publications

Formal and Intelligent Methods for Security and Resilience: Education and Training Issues

Formal and Intelligent Methods for Security and Resilience: Education and Training Issues

Botnet Detection Approach Based on the Distributed Systems

Similarity Based Instance Transfer Learning for Botnet Detection

Contact Info

Product

Resources

About