Detecting Streaming of Twitter Spam Using Hybrid Method

Twitter, the social network which evolving faster and regular usage by millions of people and who become addicted to it. So spam playing a major role for Twitter users to distract them and grab their attention over them. Spammers actually detailed like who send unwanted and irrelevant messages or websites and promote them to several users. To overcome the problem many researchers proposed some ideas using some machine learning algorithms to detect the spammers. In this research work, a new hybrid approach is proposed to detect the streaming of Twitter spam in a real-time using the combination of a Decision tree, Particle Swarm Optimization and Genetic algorithm. Twitter has given access to the researchers to get tweets from its Twitter-API for real-time streaming of tweet data which they can get direct access to public tweets. Here 600 million tweets are created by using URL based security tool and further some features are extracted for representation of tweets in real-time detection of spam. In addition, our research results are compared with other hybrid algorithms which a better detection rate is given by our proposed work.

[1]  Usha Devi Gandhi,et al.  Enhanced DTLS with CoAP-based authentication scheme for the internet of things in healthcare application , 2017, The Journal of Supercomputing.

[2]  Xiao Chen,et al.  6 million spam tweets: A large ground truth for timely Twitter spam detection , 2015, 2015 IEEE International Conference on Communications (ICC).

[3]  Ching-Hsien Hsu,et al.  Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering , 2017, Wireless Personal Communications.

[4]  Mohamed A. El-Sharkawi,et al.  Modern Heuristic Optimization Techniques , 2008 .

[5]  G. Usha Devi,et al.  Fuzzy Based Intrusion Detection Systems in MANET , 2015 .

[6]  Gunasekaran Manogaran,et al.  Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System , 2018 .

[7]  Jun Zhang,et al.  A Performance Evaluation of Machine Learning-Based Streaming Spam Tweets Detection , 2015, IEEE Transactions on Computational Social Systems.

[8]  Guofei Gu,et al.  Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter , 2012, WWW.

[9]  Dawn Xiaodong Song,et al.  Design and Evaluation of a Real-Time URL Spam Filtering Service , 2011, 2011 IEEE Symposium on Security and Privacy.

[10]  Jun Zhang,et al.  Network Traffic Classification Using Correlation Information , 2013, IEEE Transactions on Parallel and Distributed Systems.

[11]  Vern Paxson,et al.  @spam: the underground on 140 characters or less , 2010, CCS '10.

[12]  Usha Devi Gandhi,et al.  A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases , 2017, Comput. Electr. Eng..

[13]  Muhammad Abulaish,et al.  Community-based features for identifying spammers in Online Social Networks , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[14]  Jan Bohacik,et al.  Detecting compromised accounts on the Pokec online social network , 2017, 2017 International Conference on Information and Digital Technologies (IDT).

[15]  Ali Selamat,et al.  Enhanced genetic algorithm for spam detection in email , 2011, 2011 IEEE 2nd International Conference on Software Engineering and Service Science.

[16]  E. Vishnu Balan,et al.  Hybrid architecture with misuse and anomaly detection techniques for wireless networks , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).

[17]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[18]  Marc Dacier,et al.  Spammers operations: a multifaceted strategic analysis , 2016, Secur. Commun. Networks.

[19]  Jong Kim,et al.  Spam Filtering in Twitter Using Sender-Receiver Relationship , 2011, RAID.

[20]  Gunasekaran Manogaran,et al.  Big Data Knowledge System in Healthcare , 2017 .

[21]  G. Usha Devi,et al.  Mutual Authentication Scheme for IoT Application , 2015 .

[22]  Gunasekaran Manogaran,et al.  RETRACTED ARTICLE: A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing , 2017, Multimedia Tools and Applications.

[23]  Yen-Liang Chen,et al.  A Novel Decision-Tree Method for Structured Continuous-Label Classification , 2013, IEEE Transactions on Cybernetics.

[24]  Gunasekaran Manogaran,et al.  Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm , 2018, Cluster Computing.

[25]  S. Venkatasubramanian,et al.  An Information-Theoretic Approach to Detecting Changes in Multi-Dimensional Data Streams , 2006 .

[26]  Jun Hu,et al.  Detecting and characterizing social spam campaigns , 2010, IMC '10.

[27]  Xiao Zhi Gao,et al.  An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images , 2017, Comput. Electr. Eng..

[28]  Xianchao Zhang,et al.  Detecting Spam and Promoting Campaigns in the Twitter Social Network , 2012, 2012 IEEE 12th International Conference on Data Mining.

[29]  Naveen K. Chilamkurti,et al.  Secure Disintegration Protocol for Privacy Preserving Cloud Storage , 2018, Wirel. Pers. Commun..

[30]  Gunasekaran Manogaran,et al.  A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system , 2017, Future Gener. Comput. Syst..

[31]  Jong Kim,et al.  WarningBird: A Near Real-Time Detection System for Suspicious URLs in Twitter Stream , 2013, IEEE Transactions on Dependable and Secure Computing.

[32]  Mohamed A. El-Sharkawi,et al.  Fundamentals of Particle Swarm Optimization Techniques , 2008 .

[33]  Danah Boyd,et al.  Detecting Spam in a Twitter Network , 2009, First Monday.