Spam Deobfuscation using a Hidden Markov Model

58 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Adversarial Feature Selection Against Evasion Attacks (2016, background): “Previous work on evasion attacks has mainly investigated the vulnerability of supervised [7], [11], [20] and unsupervised learning techniques [21], [22] in different applications, including spam filtering [5], [23], [24], intrusion detection [25], and biometric recognition [1], [2].”
A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters (2008, background): “Recently, an efficient complementary filter (Lee and Ng, 2005) has been demonstrated to be able to effectively deobfuscate text with high accuracy.”
Combating Adversarial Misspellings with Robust Word Recognition (2019, background): “Spammers continually bombard email servers, subtly misspelling words in efforts to evade spam detection while preserving the emails’ intended meaning (Lee and Ng, 2005; Fumera et al., 2006).”
A survey of learning-based techniques of email spam filtering (2008, methodology): “A way to solve this problem using hidden Markov model is proposed by Lee and Ng [55].”