SpamBayes: Effective open-source, Bayesian based, email classification system

By Tony Andrew Meyer, Brendon Whateley. 2004

176 citations recorded by the source corpus.

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Citations in the corpus

Poisoning Attacks against Support Vector Machines (2012, methodology): “…have already been used or proposed as solutions to a number of such security-sensitive tasks including spam, worm, intrusion and fraud detection (Meyer & Whateley, 2004; Biggio et al., 2010; Stolfo et al., 2003; Forrest et al., 1996; Bolton & Hand, 2002; Cova et al., 2010; Rieck et al., 2010;…”
The security of machine learning (2010, methodology): “2The estimator used by Meyer and Whateley (2004) for conditional likelihood deviates slightly from a traditional maximum likelihood estimator.”
Secure and resilient distributed machine learning under adversarial environments (2015, background): “For example, an attacker can strategically craft training data for the spam filter to increase its misclassification rates and evade spam detectors [4].”
Bounding an Attack ’ s Complexity for a Simple Learning Model (2006, methodology): “This trend has emerged in several fields including spam filtering [8, 11], virus detection [13, 14], and adaptive intrusion detection [7].”
Exploiting Machine Learning to Subvert Your Spam Filter (2008, methodology): “SpamBayes classifies using token scores based on a simple model of spam status proposed by Robinson [ 14 , 17], based on ideas by Graham [7] together with Fisher’s method for combining independent significance tests [6].”
A survey and experimental evaluation of image spam filtering techniques (2011, methodology): “…techniques based on machine learning have been widely investigated by several authors, to implement textual content analysis (Sahami et al., 1998; Drucker et al., 1999; Androutsopoulos et al., 2000; Graham, 2002a; Robinson, 2003; Meyer and Whateley, 2004; Koprinska et al., 2007; Lai et al., 2009).”
Machine Learning in the Presence of an Adversary: Attacking and Defending the SpamBayes Spam Filter (2008, methodology): “This thesis focuses on SpamBayes as it uses a pure machine learning algorithm for spam filtering, and it has received prior attention in the academic community [16].”
A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning (2022, methodology): “For example, in literature [89], the adversary tries to perturb the SpamBayes learning method [81] via embedding more words likely to occur in a legitimate email into attack emails.”
Adversarial machine learning (2019, background): “ing large-scale decision-making problems in many systems and networking domains, including spam filtering, network intrusion detection, and virus detection [36, 45, 60].”
Multiple classifier systems for robust classifier design in adversarial environments (2010, methodology): “Pattern recognition systems are increasingly being used in applications like biometric authentication and verification, intrusion detection in computer networks, spam filtering, Web page ranking and network protocol verification [17, 23, 25, 32, 33, 42 , 46], usually to discriminate between two pat”
Behavior of Machine Learning Algorithms in Adversarial Environments (2010, methodology): “Learning approaches to malfeasance detection have also played a prominent role in modern spam filtering [e.g., Meyer and Whateley, 2004, Segal et al., 2004] and also have been proposed as elements in virus and worm detectors [e.g., Newsome et al., 2005, Stolfo et al., 2003, 2004], host-based…”
Misleading Learners: Co-opting Your Spam Filter (2009, methodology): “We target SpamBayes because it uses a pure machine learning method, it is familiar to the academic community [17], and it is popular with over 700,000 downloads.”