Content-based Cybercrime Detection: A Concise Review
Amanpreet Singh1, Maninder Kaur2
1Amanpreet Singh, Department of Computer Science & Engineering, Thapar Institute of Engineering and Technology, Patiala, India.
2Maninder Kaur, Department of Computer Science & Engineering, Thapar Institute of Engineering and Technology, Patiala, India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 1193-1207 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6926068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the recent past, the issues of Content-based Cybercrime have gained considerable attention. Social media providers seek for accurate and efficient way of recognizing offensive content for shielding their users. Content-based Cybercrime detection is one of the conspicuous area of data mining that deals with the recognition and examination of bully contents usually presented at social media. The current work emphasizes on cyberbullying, one of the prominent problems that arose due to the increasing fame of social network and its fast acceptance in our day-to-day survives. The social network provides a convenient platform for the cyber predators to bull their preys especially targeting young youth. In severe cases, the victims have attempted suicide due to humiliation, insult, and hostile messages left by the predators. This work presents a systematic critical study to accumulate, investigate, apprehend and explore the patterns and study gaps in a well-organized manner. The study portrays a comprehensive systematic literature review of strategies proposed in the field of content-based cybercrime. In this review, precise investigation methodology is utilized based on a total selected 27 research papers out of 51 research papers published in preeminent workshops, symposiums and conferences and conspicuous journals. The survey relates to several data preprocessing techniques, content-based feature, machine learning methodology, online social networking datasets and evaluation parameter used in context of detecting content-based cybercrime. This Methodical analysis of the research work acts as an assistant for the researchers to discover the significant characteristics of content-based Cybercrime detection techniques.
Keyword: Content based cybercrime, Cyberbullying, Machine learning, Deep learning.
Scope of the Article: Vision-based applications.