# Unmasking Spam in Messages

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3 A) Boolean Weighting: It is the simplest of the term weighting methods where all the data is represented using Boolean values. Mathematically it can be represented as [7] Boolean _ W ij 0 otherwise..eq(1) A term will get a weight of 0 in j if it is not present otherwise it will get a weight of 1. Boolean weighting makes the computation easy but does not consider the actual statistics of terms in the s that s why it does not achieve as high accuracy as some of the other weighting methods does. B) Term : Term frequency counts the number of occurrences of term in a text document. Mathematically it can be represented as: Term W ij = tf ij.eq(2) where, tf ij as the frequency of term i in document j C) Term Document inverse document frequency(tf-idf): Tf-Idf weighting represent that those terms whose presence is in lesser number of text documents( s) can discriminate well between the classes [8]. In Tf-Idf, we have found normalized term frequency, inverse document frequency and Tf-Idf of each word in document. TFIDF _W ij =tf ij idf TFIDF _W ij =tf ij log (N/ n )..Eq(3) i where, tf ij is normalized term frequency tf ij= Number of times term t appears in a document) / (Total number of terms in the document) N is the total number of documents or s in the corpus n i is the number of documents in the corpus where term i appears. 3) Classification: In Simple terms classification is a task of learning data patterns that are present in the data from the previous known instances and associating those data patterns with the classes. Later on when given an unknown instance it will search for data patterns and thus will predict the class based on the absence or presence of data patterns. There are many classification techniques that can classify message as a spam or non spam. Some of the classification techniques that can used in spam detection algorithm are discussed below: A) Naive Bayes classifier: The Naive Bayes classifier is a simple machine learning technique used to classify spam s. It is a probabilistic classifier; words probabilities play the main rule here. It calculates and uses the probability of certain words occurring in the known examples (messages) to categorize new examples = 1 if tf ij > 0 (messages). Every word has certain probability of occurring in spam or ham in its database. If the total of words probabilities exceeds a certain limit, the filter will mark the to either category [9]. Here, only two categories are necessary: spam or ham. Instead of using explicit rules (as in heuristic filtering or rule-based learning) or storing all instances and delaying generalization, the Bayesian approach creates a database that contains all terms (or message attributes) and their associated conditional probabilities during the training phase. B) Support Vector Machines: The Support Vector Machines (SVM) has successes at using as classifying text documents. SVM has encouraged important researches into applying them to spam filtering. SVM are used to embed the data indicating the text documents into a vector space where geometry and linear algebra can be performed [10]. SVM try to create a linear separation between the two classes in the vector space. Fig 2: Support Vector Machine An example is shown above. In this example, the objects belong either to BLUE (ham) class or PINK (spam) class. A boundary is defined using the separating line. On the left side of boundary line where all objects are PINK and to the right side of boundary line where all objects are BLUE are represented as in the Fig2. Any new object falling to the left is labelled, i.e., classified, as PINK (or classified as BLUE should it fall to the right of the separating line). C) K-Neareast Neighbour Classifier: The k-nearest neighbour (K-NN) classifier is considered an examplebased classifier, that means that the training documents are used for comparison rather than an explicit category representation, such as the category profiles used by other classifiers[12]. When a new document needs to be categorized, the k most similar documents (neighbours) are found and if a large enough proportion of them have been assigned to a certain category, the new document is also assigned to this category, otherwise not. To decide whether a message is spam or ham, we look at the class of the messages that are closest to it. The comparison between the vectors is a real time process. D) Artificial Neural Network: An artificial neural network is a group of interconnected nodes these nodes are called as neurons. The well known example of artificial Copyright to IJARCCE DOI /IJARCCE

5 REFERENCES [1] Masurah Mohamad, Khairulliza Ahmad Salleh, Independent Feature Selection as Spam-Filtering Technique: An Evaluation of Neural Network, Malaysia. [2] El-Sayed M. El-Alfy, Learning Methods For Spam Filtering, College of Computer Sciences and Engineering King Fahd University of Petroleum and Minerals, Saudi. [3] Upasna Attri & Harpreet Kaur, Comparative Study of Gaussian and Nearest Mean Classifiers for Filtering Spam s, Global Journal of Computer Science and Technology Network, Web & Security, USA, Volume 12 Issue 11 Version June [4] Enrique Puertas Sanz, José María Gómez Hidalgo,José Carlos Cortizo Pérez, Spam Filtering, Universidad Europea de Madrid Villaviciosa de Odón, Madrid, SPAIN. [5] Nouman Azam, Comparative Study of Features Space Reduction Techniques for Spam Detection, Department of Computer Engineering College of Electrical and Mechanical Engineering National University of Sciences and Technology. [6] Thamarai Subramaniam, Hamid A. Jalab and Alaa Y. Taqa, Overview of textual anti-spam filtering techniques, International Journal of the Physical Sciences Vol. 5(12), pp , 4 October, [7] Nick Evangelopoulos, Introduction to the vector space model, Text Mining Notes, University of North Texas, January [8] M. Basavaraju, Dr. R. Prabhakar, A Novel Method of Spam Mail Detection using Text BasedClustering Approach, International Journal of Computer Applications ( ) Volume 5 No.4, August [9] Ann Nosseir, Khaled Nagati and Islam Taj-Eddin, Intelligent Word-Based Spam Filter Detection Using Multi-Neural Networks IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 2, No 1, March [10] Alia Taha Sabri, Adel Hamdan Mohammads, Bassam Al-Shargabi, Maher Abu Hamdeh, Developing New Continuous Learning Approach for SpamDetection using Artificial Neural Network (CLA_ANN), European Journal of Scientific Research, ISSN X Vol.42 No.3 (2010), pp [11] Ann Nosseir, Khaled Nagati and Islam Taj-Eddin, Intelligent Word-Based Spam Filter Detection Using Multi-Neural Networks, IJCSI International Journal of Computer Science Issues, Egypt, Vol. 10, Issue 2, No 1, March [12] A. K. Uysal, S. Gunal, S. Ergin, E. Sora Gunal, The Impact of Feature Extraction and Selection on SMS Spam Filtering, ELEKTRONIKA IR ELEKTROTECHNIKA, ISSN , VOL. 19, NO. 5, Copyright to IJARCCE DOI /IJARCCE

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