A neural network based approach to automated e-mail classification

Clark, James J.; Koprinska, Irena; Poon, Josiah

doi:10.1109/wi.2003.1241300

Cited by 124 publications

(73 citation statements)

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“…The performance of spam filtering techniques is determined by two well-known measures used in text classification. These measures are precision and recall [24,25]. Here four metric have been used for evaluating the performance of proposed method such as precision, accuracy, recall and F1 score.…”

Section: Results Simulationmentioning

confidence: 99%

Spam filtering by using Genetic based Feature Selection

kalaibar¹,

Razavi²

2014

IJCATR

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Abstract:Spam is defined as redundant and unwanted electronical letters, and nowadays, it has created many problems in business life such as occupying networks bandwidth and the space of user's mailbox. Due to these problems, much research has been carried out in this regard by using classification technique. The resent research show that feature selection can have positive effect on the efficiency of machine learning algorithm. Most algorithms try to present a data model depending on certain detection of small set of features. Unrelated features in the process of making model result in weak estimation and more computations. In this research it has been tried to evaluate spam detection in legal electronica letters, and their effect on several Machin learning algorithms through presenting a feature selection method based on genetic algorithm. Bayesian network and KNN classifiers have been taken into account in classification phase and spam base dataset is used.

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Section: Results Simulationmentioning

confidence: 99%

Spam filtering by using Genetic based Feature Selection

kalaibar¹,

Razavi²

2014

IJCATR

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“…Then SVM separates spam and ham by a maximummargin hyperplane (a hyperplane with the largest distance to the nearest data points in both classes). Other well-known supervised learning paradigms include neural networks [8], maximum entropy models [51], and RuleFit (used by the SNARE system [16]). …”

Section: Supervised Learning In Traditional Spam Filtering Systemsmentioning

confidence: 99%

“…The FPR and FNR according to statistics in SA vary between 0.06-0.70% and 1.49-7.63%, respectively [35]. 8 We query six public blacklists, and an email is classified as spam if its IP is blocked by at least 2 DNSBLs.…”

Section: The Visibility Challengementioning

confidence: 99%

A case for unsupervised-learning-based spam filtering

Qian-Feng

PathakAbhinav

Charlie

et al. 2010

SIGMETRICS Perform. Eval. Rev.

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Spam filtering has traditionally relied on extracting spam signatures via supervised learning, i.e., using emails explicitly manually labeled as spam or ham. Such supervised learning is labor-intensive and costly, more importantly cannot adapt to new spamming behavior quickly enough. The fundamental reason for needing labeled training corpus is that the learning, e.g., the process of extracting signatures, is carried out by examining individual emails. In this paper, we study the feasibility of unsupervised learning-based spam filtering that can more effectively identify new spamming behavior. Our study is motivated by three key observations of today's Internet spam: (1) the vast majority of emails are spam, (2) a spam email should always belong to some campaign, (3) spam from the same campaign are generated from some template that obfuscates some parts of the spam, e.g., sensitive terms, leaving other parts unchanged.We present the design of an online, unsupervised spam learning and detection scheme. The key component of our scheme is a novel text-mining-based campaign identification framework that clusters spam into campaigns and extracts the invariant textual fragments from spam as campaign signatures. While the individual terms in the invariant fragments can also appear in ham, the key insight behind our unsupervised scheme is that our learning algorithm is effective in extracting co-occurrences of terms that are generated by campaign templates and rarely appear in ham. Using large traces containing about 2 million emails from three sources, we show our unsupervised scheme alone achieves a false negative ratio of 3.5% and a false positive ratio of at most 0.4%. These detection accuracies are comparable to those of the de-facto supervised-learning-based filtering systems such as SpamAssassin (SA), suggesting that unsupervised spam filtering holds high promise in battling today's Internet spam.

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“…There are existing spam filtering methods, such as SVM [1]、naive Bayesian 、K-Nearest Neighborhood [2] and other text classification methods can be effective to achieve the spam detection and filtering function. But for the characteristics of variation of the mail or the emergence of new features are often unable to find and extract the characteristics of the mail, and the information is not interactive timely.…”

Section: Introductionmentioning

confidence: 99%

A Spam Filtering Model of Immune Based on Multi-Agent

Jiang¹,

Hao²,

Guo³

2017

Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 20

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Abstract. According to the traditional spam filtering method effectively identify unknown characteristics and variability of the ability is not strong, according to the basic principle of biological immune system and multi agent technology proposed based on immune multi-agent spam filtering model. The model can realize the information exchange of each Agent, enhance the whole model "memory" mechanism, and effectively extract the information and variation characteristics of spam. Spam experimental simulation results show that the model and other models compared has better performance, can effectively improve the correct rate of spam model characteristics and reduce the false alarm rate.

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A neural network based approach to automated e-mail classification

Abstract: In

Cited by 124 publications

References 1 publication

Spam filtering by using Genetic based Feature Selection

Spam filtering by using Genetic based Feature Selection

A case for unsupervised-learning-based spam filtering

A Spam Filtering Model of Immune Based on Multi-Agent

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