Mitigating Botnet Attack Using Encapsulated Detection Mechanism (EDM)

Main Article Content

Maxwell Scale Uwadia Osagie
C. I. Okoye
Amenze Joy Osagie

Abstract

Botnet as it is popularly called became fashionable in recent times owing to it embedded force on network servers. Botnet has an exponential growth of about 170, 000 within network server and client infrastructures per day. The networking environment on monthly basis battle over 5 million bots. Nigeria as a country loses above one hundred and twenty five (N125) billion naira to network fraud annually, end users such as Banks and other financial institutions battle daily the botnet threats. The most worrisome part of the botmaster’s botnet is it propagation as an entity even when it is known to be large pool of malicious threats. The attacks leave end users (clients) to the risk of losing valuable credentials when connected to the affected infrastructure. It is on the above premise that this paper sort to expose the botnet method of propagation through proactive mechanism called Encapsulated Detection Mechanism (EDM) for botnet on Server Systems with further operations on conceptual framework, structural modules, usability and application of botnet. The mechanism uses one dimensional data stream evolutionary window approach of Distance Base Model (DBM) as an Outlier Analysis (OA). The Captcha, Username password and EDM Analyzer act as the front end of the data stream checker using Bot-Stream OutlieR Miner (B-STORM) algorithm and B-Exact Algorithm. The research work showed high level of data entering compliance efficiency on the server end network by neutralizing and mitigating botnet attack that falls short of the predefined data order within the networking signature.

 

Keywords:
Propagation, pool, botnet, networking, server and client

Article Details

How to Cite
Scale Uwadia Osagie, M., I. Okoye, C., & Joy Osagie, A. (2018). Mitigating Botnet Attack Using Encapsulated Detection Mechanism (EDM). Asian Journal of Research in Computer Science, 1(2), 1-16. https://doi.org/10.9734/ajrcos/2018/v1i224731
Section
Original Research Article