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Message Botnet Identification by Dynamic Analysis

Researchers at the Computer Department of TMU managed to develop a new method based on dynamic analysis for message botnet identification on new generation mobile phones that makes use of textual and behavioral features in identification process.

In recent years, botnets introduced themselves as one of the most serious threats to virtual world. After development of botnets for personal computers and infecting millions of hosts, botnets managers began developing them for new generation cell phones. These botnets use a variety of media, including internet and sms to communicate with the manager(s).

Seyed Farnood Faghihi, said:” in this project, we first investigated the various message botnets, and then they were divided into three types of information; data stealing, message stealing, and spammers. In the next step, a new approach based on dynamic analysis was proposed to identify SMS botnets on mobile phones of the new generation, which uses textual and behavioral features in the identification process.

He explained; in the proposed method, a set of textual and behavioral features is first defined and normal and abnormal behavior patterns are made by single-handed or multi-handed categories. Then sent and received text messages of bots and normal users are simulated and the required data set is made up of behavioral and textual features. Faghihi added that the evaluation of the proposed method was carried out with the help of two tests. In the first experiment, textual features and single-handed categories were used to identify the text messaging botnets. In this experiment it was revealed that by using text features alone, only some messages belonging to stealers and spammers can be identified. In the second experiment, the identification of message botnets was done by using text features along with behavioral features.

He concluded: the results of this experiment showed that behavioral features for identifying message botnets of stealing SMS are essential, and by using textual attributes along with behavioral features, one can well distinguish between botnets-related messages from normal SMSs. It is worth mentioning that this research was conducted with the guidance of Dr. Mehdiabadi, faculty member of electrical and computer engineering of the university.

The national transcranial direct-current laboratory holds an introduction workshop on “introduction to electrical stimulation and clinical applications, research and cognitive studies” on 13th and 14th of July, 2018.

This workshop with a re-training purpose, aims at introducing health experts to the use of transcranial direct current stimulation, methods in promoting neural function, familiarizing psychiatrists with the use of transcranial direct current stimulation methods in psychiatry disorders treatments, and the familiarity of neuroscientists with the dimensions of transcranial electrical stimulation and leading research horizons.

Transcranial electric stimulation (TES) in recent years has been considered as one of the leading therapies in mental health and instrumental research for neuroscientists. Different subtypes of electric stimulations and its various therapeutic effects, as well as research importance of each of these subcategories have raised the need for more supplementary training.

This training course which includes transcranial direct current stimulation, transcranial alternating current stimulation, transcranial random noise stimulation, relevant methodological considerations, how to use electric stimulation devices in the workshop, research strategies and clinical applications, and finally designing of relevant projects and promoting neural function, will be held with the presence of international and domestic instructors.

Interested individuals can visit the website of the National Transcranial Lab at for more information.

16:43 - 2018/07/07    /    number : 7686    /    Show Count : 199