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A Digital Forensics Investigation Model for Social Networking Site

Published: 04 March 2016 Publication History

Abstract

Social Networking is fundamentally shifting the way we communicate, sharing idea and form opinions. All people try to use social media for there need, people from every age group are involved in social media site or e-commerce site. Nowadays almost every illegal activity is happened using the social network and instant messages. It means that present system is not capable to found all suspicious words. In this paper, we provided a brief description of problem and review on the different framework developed so far. Propose a better system which can be indentify criminal activity through social networking more efficiently. Use Ontology Based Information Extraction (OBIE) technique to identify domain of word and Association Rule mining to generate rules. Heuristic method checks in user database for malicious users according to predefine elements and Naïve Bayes method is use to identify the context behind the message or post. The experimental result is used for further action on victim by cyber crime department.

References

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M. Mahmood Ali, and Lakshmi Rajamani, "Framework for Surveillance of Emails to Detect Multilingual Spam and Suspicious Messages," IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions, IIT Kanpur, India, pp. 42--56, 2013.
[2]
Mohd Mahmood Ali, And Lakshmi Rajamani,"APD: ARM Deceptive Phishing Detector SystemPhishing Detection In Instant Messengers Using Data Mining Approach", Springer-Verlag Berlin Heidelberg, in 2012.
[3]
Daya C. Wimalasuriya, and Dejing Dou,"Ontology-Based Information Extraction: An Introduction and a Survey of Current Approaches," Journal of Information Science, Volume 36, No. 3, pp. 306--323, 2010.
[4]
M. Mahmood Ali, and L. Rajamani, "Framework for surveillance of instant messages," published by inderscience in IJITST, vol. 5, 2013.
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Michael Robertson, Yin Pan, and Bo Yuan, "A Social Approach to Security: Using Social Networks to Help Detect Malicious Web Content," published by IEEE in 2010.
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Wang Wei, Payam Barnaghi, and Andrzej Bargiela, "Probabilistic Topic Models for Learning terminological ontologies," published by IEEE Tran., on Knowledge and data engineering, vol 22, no. 7 in july, 2010.
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Anagha R Kulkarnia, Vrinda Tokekarb, Parag Kulkarnic, "Identifying Context of Text Documents using Naïve Bayes Classification and Apriori Association Rule Mining" published by IEEE in 2009.
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Ankita M.Shendurkar and Nitin R.Chopde" A Review on Various Approaches for Detection of Cyber Crimes via Instant Messaging Filters" International Journal of Current Engineering and Technology, Vol. 5, No. 1 (Feb 2015)
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Jer Lang Hong, " Data Extraction For Deep Web Using Word Net", Published By IEEE Transactions On Systems, Man And Cybernetics.2011
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Appavu, And Et Al," Data Mining Based Intelligent Analysis Of Threatening E-Mail", Published By Elsevier In Knowledge-Based Systems. In 2009.
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Sunitha Ramanujam, And Et Al," A Relational Wrapper For RDF Reification", IFIP International Federation For Information Processing IFIP AICT 300, in 2009.

Cited By

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  • (2023)Advanced Machine Learning Approach for Suspicious Coded Message Detection using Enigma Cipher2023 Second International Conference on Electronics and Renewable Systems (ICEARS)10.1109/ICEARS56392.2023.10085339(800-803)Online publication date: 2-Mar-2023
  • (2022)A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social NetworksSensors10.3390/s2203111522:3(1115)Online publication date: 1-Feb-2022
  • (2022)Research Perspective on Digital Forensic Tools and Investigation ProcessIllumination of Artificial Intelligence in Cybersecurity and Forensics10.1007/978-3-030-93453-8_4(71-95)Online publication date: 8-Feb-2022
  • Show More Cited By

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Published In

cover image ACM Other conferences
ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies
March 2016
843 pages
ISBN:9781450339629
DOI:10.1145/2905055
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2016

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Author Tags

  1. IMS
  2. Information Extraction
  3. NLP
  4. Social Networking Site
  5. Suspicious Words

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ICTCS '16

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Overall Acceptance Rate 97 of 270 submissions, 36%

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Cited By

View all
  • (2023)Advanced Machine Learning Approach for Suspicious Coded Message Detection using Enigma Cipher2023 Second International Conference on Electronics and Renewable Systems (ICEARS)10.1109/ICEARS56392.2023.10085339(800-803)Online publication date: 2-Mar-2023
  • (2022)A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social NetworksSensors10.3390/s2203111522:3(1115)Online publication date: 1-Feb-2022
  • (2022)Research Perspective on Digital Forensic Tools and Investigation ProcessIllumination of Artificial Intelligence in Cybersecurity and Forensics10.1007/978-3-030-93453-8_4(71-95)Online publication date: 8-Feb-2022
  • (2021)Digital Forensics of Cybercrimes and the Use of Cyber Forensics Tools to Obtain Digital EvidenceCyber Security Auditing, Assurance, and Awareness Through CSAM and CATRAM10.4018/978-1-7998-4162-3.ch003(45-68)Online publication date: 2021
  • (2020)Formal knowledge model for online social network forensicsComputers and Security10.1016/j.cose.2019.10167589:COnline publication date: 1-Feb-2020
  • (2018)Digital Forensic Analysis of CybercrimesDigital Multimedia10.4018/978-1-5225-3822-6.ch029(588-600)Online publication date: 2018

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