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Filtering Spam: Current Trends and Techniques
Geerthik S.,Anish T. P.
International Journal of Mechatronics, Electrical and Computer Technology , 2013,
Abstract: This article gives an overview about latest trend and techniques in spam filtering. We analyzed the problems which is introduced by spam ,what spam actually do and how to measure the spam .This article mainly focuses on automated, non-interactive filters, with a broad review ranging from commercial implementations to ideas confined to current research papers. The solutions using both machine and non –machine learning approaches are reviewed and taxonomy of different approaches is presented. While a range of different techniques have and continue to be evaluated in academic research, heuristic and Bayesian filtering, along with its variants provide the greatest potential for future spam prevention.
Survey on image-based spam filtering
图像型垃圾邮件过滤技术综述

WAN Ming-cheng,GENG Ji,CHENG Hong-rong,CHEN Jia,
万明成
,耿技,程红蓉,陈佳

计算机应用研究 , 2008,
Abstract: This paper analyzed the difficulties of detecting image-based spam with the features of images in detail.The features of spam images were divided into eight categories such as file attribute features,image metadata and so on.Then,discussed and compared five classification algorithms which have been used in image-based spam filtering were outlined,including support vector machines,decision tree method,maximum entropy model,the DS evidence theory,Bayesian algorithm and the effect of these algorithms.Finally,gave some future directions of research on the techniques of image-based spam filtering.
Email Spam Filtering using Supervised Machine Learning Techniques  [PDF]
V.Christina,,S.Karpagavalli,G.Suganya
International Journal on Computer Science and Engineering , 2010,
Abstract: E-mail spam, known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted e-mail messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. Spam is prevalent on the Internet because the transaction cost of electronic communications is radically less than any alternate form of communication. There are many spam filters using different approaches to identify the incoming message as spam, ranging from white list / black list, Bayesian analysis, keyword matching, mail header analysis, postage, legislation, and content scanning etc. Even though we are still flooded with spam emails everyday. This is not because the filters are not powerful enough, it is due to the swift adoption of new techniques by the spammers and the inflexibility of spamfilters to adapt the changes. In our work, we employed supervised machine learning techniques to filter the email spam messages. Widely used supervised machine learning techniques namely C 4.5 Decision tree classifier, Multilayer Perceptron, Na ve Bayes Classifier are used for learning the features of spam emails and the model is built by training with known spam emails and legitimate emails. The results of the models are discussed.
A Survey of Collaborative Filtering Techniques  [PDF]
Xiaoyuan Su,Taghi M. Khoshgoftaar
Advances in Artificial Intelligence , 2009, DOI: 10.1155/2009/421425
Abstract: As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Survey on spam filtering technology
垃圾邮件过滤技术研究综述*

CHEN Zhi-xian,
陈志贤

计算机应用研究 , 2009,
Abstract: Spam filtering is an important technology of active security defense. This paper firstly introduced the development of spam filtering technology and its basic concept. And then classified spam filtering technology according to different stan-dards, meanwhile analyzed and evaluated the popular and major spam filtering methods and technologies. Finally forecasted the development direction of spam filtering technology and its products.
Hybrid Spam Filtering for Mobile Communication  [PDF]
Ji Won Yoon,Hyoungshick Kim,Jun Ho Huh
Computer Science , 2009,
Abstract: Spam messages are an increasing threat to mobile communication. Several mitigation techniques have been proposed, including white and black listing, challenge-response and content-based filtering. However, none are perfect and it makes sense to use a combination rather than just one. We propose an anti-spam framework based on the hybrid of content-based filtering and challenge-response. There is the trade-offs between accuracy of anti-spam classifiers and the communication overhead. Experimental results show how, depending on the proportion of spam messages, different filtering %%@ parameters should be set.
Survey of Application of Bayesian Classifying Method to Spam Filtering
垃圾邮件过滤的贝叶斯方法综述*

ZHANG Ming-feng,LI Yun-chun,LI Wei,
张铭锋
,李云春,李巍

计算机应用研究 , 2005,
Abstract: The study on content-based spam filtering is one of the hot topics in the Internet security research area all over the world recently. Since spam filtering is a classification problem, all classifying method in machine-learning could be applied to the field. Among them, Bayesian classify method has expressed high accuracy. In this paper, the basic theory of Bayesian classifying method is described in detail, and different implementation methods are compared and summarized. Also, limitation of Bayesian method is evaluated and the research direction in future is proposed according to the limitation.
A Trust Based System for Enhanced Spam Filtering  [cached]
Jimmy McGibney,Dmitri Botvich
Journal of Software , 2008, DOI: 10.4304/jsw.3.5.55-64
Abstract: The effectiveness of current anti-spam systems is limited by the ability of spammers to adapt to filtering techniques and the lack of incentive for mail servers to filter outgoing spam. A new approach, based on decentralised trust management, is described in this paper. An architecture and protocol, called TOPAS (Trust Overlay Protocol for Anti Spam), are presented. Each mail server records trust measures relating to each other mail server of which it is aware. Trust by one mail server in another is influenced by direct experience as well as recommendations issued by collaborating mail servers. The TOPAS protocol specifies how these experiences and recommendations are communicated between each spam filter and its associated trust manager, and between trust managers of different mail servers. A technique for improving mail filtering performance and the TOPAS protocol using these trust measures is also described. Finally, experimental work is presented that illustrates use of the protocol in a simulated network scenario. Initial results illustrate the dynamics of this system, and indicate the potential of this approach to significantly improve rates of false positives and false negatives in anti-spam systems. This is an extended version of a paper presented at the Availability, Reliability and Security Conference in April 2007.
Privacy Preserving Spam Filtering  [PDF]
Manas A. Pathak,Mehrbod Sharifi,Bhiksha Raj
Computer Science , 2011,
Abstract: Email is a private medium of communication, and the inherent privacy constraints form a major obstacle in developing effective spam filtering methods which require access to a large amount of email data belonging to multiple users. To mitigate this problem, we envision a privacy preserving spam filtering system, where the server is able to train and evaluate a logistic regression based spam classifier on the combined email data of all users without being able to observe any emails using primitives such as homomorphic encryption and randomization. We analyze the protocols for correctness and security, and perform experiments of a prototype system on a large scale spam filtering task. State of the art spam filters often use character n-grams as features which result in large sparse data representation, which is not feasible to be used directly with our training and evaluation protocols. We explore various data independent dimensionality reduction which decrease the running time of the protocol making it feasible to use in practice while achieving high accuracy.
SMS Spam Filtering Technique Based on Artificial Immune System
Tarek M Mahmoud,Ahmed M Mahfouz
International Journal of Computer Science Issues , 2012,
Abstract: The Short Message Service (SMS) have an important economic impact for end users and service providers. Spam is a serious universal problem that causes problems for almost all users. Several studies have been presented, including implementations of spam filters that prevent spam from reaching their destination. Nave Bayesian algorithm is one of the most effective approaches used in filtering techniques. The computational power of smart phones are increasing, making increasingly possible to perform spam filtering at these devices as a mobile agent application, leading to better personalization and effectiveness. The challenge of filtering SMS spam is that the short messages often consist of few words composed of abbreviations and idioms. In this paper, we propose an anti-spam technique based on Artificial Immune System (AIS) for filtering SMS spam messages. The proposed technique utilizes a set of some features that can be used as inputs to spam detection model. The idea is to classify message using trained dataset that contains Phone Numbers, Spam Words, and Detectors. Our proposed technique utilizes a double collection of bulk SMS messages Spam and Ham in the training process. We state a set of stages that help us to build dataset such as tokenizer, stop word filter, and training process. Experimental results presented in this paper are based on iPhone Operating System (iOS). The results applied to the testing messages show that the proposed system can classify the SMS spam and ham with accurate compared with Nave Bayesian algorithm.
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