Related work:

Many application system are been created
and many research have been done to detect suspicious words messages, chat,
profile, email spam, URL malicious web content also to detect phishing attacks
in social media. The social approach to detect spam or malicious on Facebook,
Twitter, Myspace was based on information found in social media by identifying
or detecting malicious URL link, email with spam content, suspicious words from
image and video text. There’s few application that detect suspicious words embedded
in images or video with help of image processing, image retrieval techniques.
Previous study monitoring messages sent through social networking sites and instant
messages. The designed framework that prevent, predicts, provide evidence of cyber-attacks
profile when suspicious messages sent between users, but fail to detect
suspicious messages in short form and coded words form sent via IM and SNM in
real time. They also fail to detect suspicious words in all kind of long and
short words form and coded words embedded in image contents.

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Mohd Mahmood Ali, Khaja Moizuddin Mohd
and Lakshmi Rajamani are researchers that found out SMD framework to detect
suspicious word from messages stored in database after users made communication
via social media{Ali2014}. This paper was not focus on coded words and short
form chat messages. It was to prevent, predict and provide evidence of
suspicious words and tracing the profile of individual or group committing
crime and report to E-crime department. However the approaches used such as data
mining, ontology structure divided the websites texts semantically with the
assistance of Word Net database into different attributes of threats categories
for example: murder, kidnap and sexual{Ali2014}. But ontology wasn’t refreshed
frequently with new code words that are discovered utilizing data mining approach.

 Rajamani, Lakshmi Ali, Mohammed Mahmood
Rasheed, Mohammed Abdul presented a system designed with a thought of text
secure framework that recognizes suspicious messages which prompts unlawful
movements by criminals. A framework was not concentrated on securing messages
by utilizing encryption approaches and furthermore does not focus on short form
messages. This paper gives different thoughts regarding stemming calculation
and priority algorithm{Rajamani2013}.

Murugesan, Devi, Deepthi, Lavanya and
Annie Princy. They proposed the system monitoring suspicious discussions
automatically on online forum, they used text analysis to detect suspicious
post in online forums. They focus on automated classification to identify more
important suspicious discussion{Murugesan2016}.

Thivya Shilpa. Gv proposed a framework
which provide security, predicts, detect coded words and short form of
suspicious words with the help of association rule mining techniques and ontology
concept that provide security for the stored chat messages by using encryption
technique. But this paper doesn’t detect suspicious word attached in image
contents {Thivya2015}.

Salim Almi Omar Beqqali, a researcher
discovered automatic system for detecting suspicious profile in social media
through identifying suspicious performance and concern of users. The techniques
presented is mainly based on the calculation of similarity distance to
differentiate suspicious posts using text analysis. The limitations found
executing time, development automated classifications and using techniques.

John Resig Ankur Teredesai 11 in their
paper explore framework for IM and various data mining issues and how they
relate to Instant Messaging and current Counter-Terrorism efforts. And this
paper does not tend to fully detect suspicious messages, not even detection of
topics and social network analysis.

Those above articles explains approaches
and limitations of capture suspicious word. Currently, clients are using
different tricks of sending short form or coded words which is not so easy to recognise
by crime departments or admin. Our proposed algorithms is to detect suspicious
word in short form or coded words attached in images that are sent by users {Vijayarajan2016}.