Spam E-mail Filtering using ECOS Algorithms

Almomani, Ammar; Obeidat, Atef A.; Al-Saedi, Karim Hashim; Al-Hazaimeh, Obaida M.; Al-Betar, Mohammed Azmi

doi:10.17485/ijst/2015/v8is9/55320

Cited by 9 publications

(1 citation statement)

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“…Botnets for example are used for launching attacks of distributed denial-of-service (DDoS). They are used for identifying thefts, sending spam and phishing emails (Ammar et al, 2018;Almomani et al, 2015Almomani et al, , 2013Almomani, 2018;Alieyan et al, 2018;Alauthaman et al, 2018;Alauthman et al, 2020;Alieyan et al, 2019;Al-Kasassbeh et al, 2020;Alauthman and Albesani, 2019;Al-Nawasrah et al, 2020). Fast flux networks (FFNs) serve as a kind of botnet.…”

Section: Introductionmentioning

confidence: 99%

Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm

Almomani

Nawasrah

Meziane

et al. 2021

IJAHUC

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A botnet refers to a group of machines. These machines are controlled distantly by a specific attacker. It represents a threat facing the web and data security. Fast-flux service network (FFSN) has been engaged by bot herders for cover malicious botnet activities. It has been engaged by bot herders for increasing the lifetime of malicious servers through changing the IP addresses of the domain name quickly. In the present research, we aimed to propose a new system. This system is named fast flux botnet catcher system (FFBCS). This system can detect FF-domains in an online mode using an adaptive dynamic evolving spiking neural network algorithm. Comparing with two other related approaches the proposed system shows a high level of detection accuracy, low false positive and negative rates, respectively. It shows a high performance. The algorithm's proposed adaptation increased the accuracy of the detection. For instance, this accuracy reached (98.76%) approximately.

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