LAUSR

Social Network sites have become incredibly important in the present day. This popularity attracts the attacker to easily approach a large population and to have access to massive information for performing intrusion activities in Online Social Networks (OSN) including spamming. Spammers not only spread unsolicited messages but also perform malicious activities that harm the user’s financial or personal life and tarnish the reputation of social network platforms. Efficient spam detection requires the selection of relevant features to portray spammer behavior. Most of the existing feature selection techniques use any one of the evaluation measures such as, distance, dependence, consistency, information, and classifier error rate. The feature selection techniques select features from different perspectives based on the evaluation measures. Each evaluation measure produces different subset, and the detection rate differs accordingly. The majority of the existing works focus on the individual feature ranking, and discard the lowest weight feature. Lowest weight feature may produce more accurate prediction if, it is combined with other features. So, there is a need for the feature selection technique that considers the characteristics of all the evaluation measures to produce the appropriate subset, which increases the spam detection rate and assigns a weight for the combination of features. In regard to this, the paper proposes a new multi evaluation measure combined with feature subset selection based on the genetic algorithm, GAMEFEST. The performance of the proposed work has been evaluated using Twitter, Apontador, and YouTube datasets. Experimental results prove that our proposed GAMEFEST with Minimum Surplus Crossover (MSC) improves the efficiency of the learning process and increases the spam detection rate.