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Cyber Defence and Countermeasures
Published in Stanislav Abaimov, Maurizio Martellini, Cyber Arms, 2020
Stanislav Abaimov, Maurizio Martellini
The primary solution to remove bots from the system is to deploy an anti-malware solution. The following solutions against being a target for botnets are used by the organizations: Anti-spam solutions allow for the reduction of the volume of advertisements and fraudulent emails, received by companies.Anti-DDoS solutions block suspicious traffic, which threaten to overload the external network connections.Load-balancing solutions for data centres allow for the reduction of the volume of traffic on a single node in the network, as well as preventing bruteforce (password guessing) attacks.
Security: Basics and Security Analytics
Published in Rakesh M. Verma, David J. Marchette, Cybersecurity Analytics, 2019
Rakesh M. Verma, David J. Marchette
attack!spam Spam usually refers to emails containing advertisements and is usually an irritant and a time/productivity sink more than anything else. It is estimated that almost 80% of emails nowadays is spam. Authors of spam emails usually do not disguise their emails, hence they are easier to detect. The best defense against spam is a good anti-spam filter and care in selecting services when signing up for something. These filters usually work by learning keywords in spam that occur frequently [312]. Some of them also use other techniques such as analyzing email headers.11 An example of a spam email is shown in the box below. Observe how it has been marked as spam by the spam filter employed. Note also that the email header is abbreviated.
VGI and crowdsourced data credibility analysis using spam email detection techniques
Published in International Journal of Digital Earth, 2018
Saman Koswatte, Kevin McDougall, Xiaoye Liu
Spam email detection (Pantel and Lin 1998; Cranor and LaMacchia 1998; Metsis, Androutsopoulos, and Paliouras 2006; Robinson 2003; Lopes et al. 2011), junk-email detection (Sahami et al. 1998) or anti-spam filtering (Androutsopoulos et al. 2000; Schneider 2003) research has a long history which grew from the commercialisation of the internet in mid-1990s (Cranor and LaMacchia 1998). Researchers have explored various approaches with content-based filters or Bayesian filters being the most popular anti-spam systems (Lopes et al. 2011). Wang (2010) tested a Bayesian classifier for spam detection in Twitter and confirmed that Bayesian classifiers performed highly in terms of weighted recall and precision, and outperformed the decision tree, neural network, support vector machines and k-nearest neighbour’s classifications.