Symbiotic filtering for spam email detection

Abstract

This paper presents a novel spam filtering technique called Symbiotic Filtering (SF) that aggregates distinct local filters from several users to improve the overall performance of spam detection. SF is an hybrid approach combining some features from both Collaborative (CF) and Content-Based Filtering (CBF). It allows for the use of social networks to personalize and tailor the set of filters that serve as input to the filtering. A comparison is performed against the commonly used Naive Bayes CBF algorithm. Several experiments were held with the well-known Enron data, under both fixed and incremental symbiotic groups. We show that our system is competitive in performance and is robust against both dictionary and focused contamination attacks. Moreover, it can be implemented and deployed with few effort and low communication costs, while assuring privacy.

Analysis of health professional security behaviors in a real clinical setting: An empirical study

“…Spam is the most common method of virus infiltration [62] and accounted for about 80% of the 182.9 billion e-mails sent and received per day in 2013. Spam consumes resources (time spent reading messages, bandwidth, CPU time and disk memory), but is also used to spread malicious content such as online fraud or viruses [63,64]. According to a recent study by the Radicati Group, the amount of spam received between 2013 and 2017 is expected to remain at roughly 15% of the e-mails received, thanks to anti-spam technology [65].…”

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“…Unlike ham (legitimate email), spam is commonly defined as unsolicited bulk email -email that is not asked for by multiple recipients [1]. Since spam causes serious problems for internet users and providers, email classification becomes an important task in filtering spam [2].…”

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“…Researchers have explored various approaches with Content Based Filters (CBF) 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.…”

“…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 Schneider 2003) research has a long history which grew from the commercialization of the internet in mid 1990s (Cranor and LaMacchia 1998). Researchers have explored various approaches with Content Based Filters (CBF) or Bayesian filters being the most popular anti-spam systems (Lopes et al 2011).…”