An Effective Technique to Identify Anomalous Accounts on Social Networks using Bloom Filter

The anomaly detection is the technique which is applied to detect malicious activities from the social network data. The existing technique is based on to classify the Facebook accounts into three classes which are fake, genuine and moderate. To increase accuracy of account classification is increased when bloom filter is being applied in the algorithm. The bloom filter is the algorithm which learns from the previous experiences and drive new values. When the bloom filter is applied the accounts are classified into two classes. The simulation is being performed in MATLAB and it is being analyzed that accuracy is increased and execution time is reduced. General Terms Anomaly detection, Bloom Filter, Classification, Fake accounts, Online social Networks.