IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering (TKDE) is an archival journal published monthly designed to inform researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area. Read the full scope of TKDE
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From the November 2018 issue
NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection in Online Social Networks
By Vishal Sharma, Ravinder Kumar, Wen-Huang Cheng, Mohammed Atiquzzaman, Kathiravan Srinivasan, and Albert Y. Zomaya
Use of social network is the basic functionality of today's life. With the advent of more and more online social media, the information available and its utilization have come under the threat of several anomalies. Anomalies are the major cause of online frauds which allow information access by unauthorized users as well as information forging. One of the anomalies that act as a silent attacker is the horizontal anomaly. These are the anomalies caused by a user because of his/her variable behavior towards different sources. Horizontal anomalies are difficult to detect and hazardous for any network. In this paper, a self-healing neuro-fuzzy approach (NHAD) is used for the detection, recovery, and removal of horizontal anomalies efficiently and accurately. The proposed approach operates over the five paradigms, namely, missing links, reputation gain, significant difference, trust properties, and trust score. The proposed approach is evaluated with three datasets: DARPA'98 benchmark dataset, synthetic dataset, and real-time traffic. Results show that the accuracy of the proposed NHAD model for 10 to 30 percent anomalies in synthetic dataset ranges between 98.08 and 99.88 percent. The evaluation over DARPA'98 dataset demonstrates that the proposed approach is better than the existing solutions as it provides 99.97 percent detection rate for anomalous class. For real-time traffic, the proposed NHAD model operates with an average accuracy of 99.42 at 99.90 percent detection rate.
Editorials and Announcements
- TKDE now offers authors access to Code Ocean. Code Ocean is a cloud-based executable research platform that allows authors to share their algorithms in an effort to make the world’s scientific code more open and reproducible. Learn more or sign up for free.
- We are pleased to announce that Xuemin Lin, a Scientia Professor in the School of Computer Science and Engineering at the University of New South Wales, Australia, has been named the new Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering starting in 2017.
- New EIC Editorial (March 2017)
- Editorial (January 2017)
- EIC Editorial (October 2016)
- In Memoriam: Chittoor V. Ramamoorthy, PhD 1926-2016 (June 2016)
- State of the Journal (January 2016)
- Editorial (August 2015)
- State of the Journal Editorial (January 2015)
- Special Section on the International Conference on Data Engineering 2015 (March 2017)
- Special Section on the International Conference on Data Engineering (February 2016)
- Special Section on the International Conference on Data Engineering (July 2015)
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