# Detecting Suspicious Texts Using Machine Learning Techniques
## 1 Introduction  
Due to the effortless access of the Internet, world wide web, blogs, social media, discussion forums, and online platforms via digital gadgets have been producing a massive volume of digital text contents in recent years. It is observed that all the contents are not genuine or authentic; instead, some contents are faked, fabricated, forged, or even suspicious. It is very unprotitious with this rapid growth of digital contents that the ill-usage of the Internet has also been multiplied which governs the boost in suspicious activities [1]. Suspicious contents are increasing day by day because of ill-usage of the Internet by a few individuals to promulgate fierceness, share illegal activities, bullying other people, perform smishing, publicize incitement related contents, spread fake news, and so on. According to the FBI's Internet Crime Complaint Center (IC3) report, a total of 467,361 complaints received in the year 2019 related to internet-facilitated criminal activity [2]. Moreover, several extremist users usesocial media or blogs to spread suspicious and violent contents which can be considered one kind of threat to national security [3]. Around 245 million people are speaking in Bengali as their native tongue, which makes it the 7 th most spoken language in the world [4]. However, research on Bengali Language Processing (BLP) is currently in its initial stage, and there are no significant amount of works that have been conducted yet like English, Arabic, Chinese, or other European languages that make Bengali a resource constrained language [5]. As far as we are concerned, there has been no research conducted up to now on suspicious text detection in the Bengali language. However, such systems are required to ensure the security as well as mitigate national threats in cyber-space. Suspicious contents are those contents that hurt religious feelings, provoke people against government and law enforcement agencies, motivate people to perform acts of terrorism, perform criminal acts by phishing, smishing, and pharming, instigate a community without any reason, and execute extortion acts [6; 7; 8; 9]. As examples, social media has already used as a medium of communication in Boston attack and the revolution in Egypt [10]. The suspicious contents can be available in the form of video, audio, images, graphics, and text. However, text plays an essential role in this context as it is the most widely used medium of communication in cyber-space. Moreover, the semantic meaning of a conversation can be retrieved by analyzing text contents which is difficult in other forms of content. In this work, we focus on _analyzing text content_ and classifying the content into suspicious or non-suspicious. A text could be detected as suspicious if it contained suspicious contents. It is impossible to detect suspicious texts from the enormous amount of internet text contents manually [11]. Therefore, the automatic detection of suspicious text contents should be developed. Responsible agencies have been demanding some smart tool/system that can detect suspicious text automatically. It will also be helpful to identify potential threats in the cyber-world which are communicated by text contents. Automatic detection of suspicious text system can easily and promptly detect the fishy or threatening texts. Law and enforcement authority can take appropriate measures immediately, which in turn helps to reduce virtual harassment, and suspicious and criminal activities mediated through online. However, it is a quite challenging task to classify the Bengali text contents into suspicious or non-suspicious class due to its complex morphological structure, enormous numbers of synonym, and rich variations of verb auxiliary with the subject, person, tense, aspect, and gender. Moreover, scarcity of resources and lack of benchmark Bengali text dataset are the major barriers to build a suspicious text detection system and make it more difficult to implement compared to other languages.