Research Cell: An International Journal of Engineering Sciences

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Cloning Attack Detection in Online Social Networks Using Improved Clustering and Similarity Measures


Affiliations
1 University Institute of Engineering and Technology (U.I.E.T), Kurukshetra University, Kurukshetra (K.U.K)-136119, Haryana, India
2 Department of Computer Science & Engineering, University Institute of Engineering and Technology (U.I.E.T), Kurukshetra University, Kurukshetra (K.U.K)-136119, Haryana, India
 

Today the attractiveness of online social networks is increasing speedily. Users spend their time in popular social networking sites like Facebook, Instagram and Twitter to share the personal data. Clone attack is one of the dangerous attacks in social profile. Attacker stole the personal data and create fake profile in social called cloned profile once cloned profile is created by attacker they sends a friend request using cloned profile. In the proposed system the cloning attack detection using improved clustering (Ikmeans) and similarity measure (Jaro-Winkler) to find out the similarity between cloned profile and real one. After that the clustered similarity data is again tested using similarity function as Euclidean distance to pick up top closest members to be cloned and find out precision, recall and F-measure.

Keywords

Cloning Attack, Detection, K-Means Clustering, Social Networks, Similarity, Online Social Network (OSN).
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  • Cloning Attack Detection in Online Social Networks Using Improved Clustering and Similarity Measures

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Authors

Sanjeev Dhawan
University Institute of Engineering and Technology (U.I.E.T), Kurukshetra University, Kurukshetra (K.U.K)-136119, Haryana, India
Kulvinder Singh
University Institute of Engineering and Technology (U.I.E.T), Kurukshetra University, Kurukshetra (K.U.K)-136119, Haryana, India
Akshay Sharma
Department of Computer Science & Engineering, University Institute of Engineering and Technology (U.I.E.T), Kurukshetra University, Kurukshetra (K.U.K)-136119, Haryana, India

Abstract


Today the attractiveness of online social networks is increasing speedily. Users spend their time in popular social networking sites like Facebook, Instagram and Twitter to share the personal data. Clone attack is one of the dangerous attacks in social profile. Attacker stole the personal data and create fake profile in social called cloned profile once cloned profile is created by attacker they sends a friend request using cloned profile. In the proposed system the cloning attack detection using improved clustering (Ikmeans) and similarity measure (Jaro-Winkler) to find out the similarity between cloned profile and real one. After that the clustered similarity data is again tested using similarity function as Euclidean distance to pick up top closest members to be cloned and find out precision, recall and F-measure.

Keywords


Cloning Attack, Detection, K-Means Clustering, Social Networks, Similarity, Online Social Network (OSN).