Spam Detection using ANN and ABC Algorithm

Social network becomes an effective method to engage with our friends. It enables the users to extract a number of in-formation and its usage are increasing day by day. Amidst all the social networking sites, Twitter is one of the interactive social networking services. To change the authorized user accounts, many spammers are utilized a vast amount of spam. Machine Learning (ML) technique is utilized for spam detection system in social sites and for the detection of spammer. Data collection is usually done from H-Spam 14 site with the help of pre-processing mechanism, the data is transforming into lowercase. After the first step, pre-processed data comes under feature extraction phase, which utilizes tokenization process to breakdown each sentence into word group in order to extract the best feature from the raw data. The optimization algorithm referred as Artificial Bee Colony (ABC) is used to pick the optimized value from extracted set of features. It is also utilized to get the optimal sets of features from spam and non-spam data. At the end, performance measure criterion and comparing the existing and proposed work in order to look over the progress of the proposed work. In this work, spam detection system is having higher accuracy, precision, recall, and F-measure as compares to classifiers used previously such as, Naïve Bayes and Support Vector Machine (SVM).