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Research Interests
1. Application and Mobile Security
2. Network Security
3. Cyber Physical Systems
4. Malware analysis.
5. API security
6. Blockchain based Encryption
Research Papers
1.Hasan M.R., Begum A., Zamal F.B., Rawshan L., Bhuiyan T. (2020) Android Malware Detection by Machine Learning Apprehension and Static Feature Characterization. In: Bhuiyan T., Rahman M., Ali M. (eds) Cyber Security and Computer Science. ICONCS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-52856-0_5
Abstract: The increased usage and popularity of Android devices encourage malware developers to generate newer ways to launch malware in different packaged forms in different applications. These malware causes various information leakage and money lost. For example, only in Canada, McAfee, which surveyed 1,000 Canadians and found 65 per cent of them, had lost more than $100 and almost a third had lost more than $500 to various cyber scams so far this year. Moreover, after identifying software as malware, unethical developer repackages the detected one and again launches the software. Unfortunately, repackaged software remains undetected mostly. In this research three different tasks were done. Comparing to the existing work we have used source code based analysis using bag-of words algorithm in machine learning. By modifying Bag-of-word procedure and adding some additional preprocessing of dataset the evaluation results represent 0.55% better than the existing work in this field. In that case re-packaging was included and this is a new edition in this field of research. Moreover in this research, a vocabulary was also created to identify the malicious code. Here with existing 69 malicious patterns more 12 malicious patterns were added. In addition to these two contributions, we have also implemented our model in a web application to test. This paper represents such a model, which will help the developers or antivirus launcher to detect malware if it is repackaged. This vocabulary will also help to do so.
Presenter Certificate from ICONCS 2020
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2.Md Rashedul Hasan, & Prof. Dr. Md. Fokhray Hossain. (2020, October). Predicting COVID 19 Infection Ratio Based On Behavioral And Demographic Observations Of Bangladesh [Paper presentation]. Online Multidisciplinary International Conference on Transformation and Survival Post Pandemic, Pune, India. https://www.adypu.edu.in/international-conference-2020
Abstract: Coronavirus has become a catastrophic phenomena specially due to a particular variant SARS-CoV-2 which has been familiarized as COVID-19. Initially there were several symptoms of COVID 19 such as fever, coughing, shortness of breath, diarrhea etc. However due to mutation the virus kept changing its behavior and symptom pattern. Bangladesh has become a fatal victim of this pandemic. After the initial discovery in the first weeks of March 2020 Bangladesh is seeing a dramatic surge in infection ratio in staggering numbers. All of Bangladesh currently has COVID-19 infected patients seeking proper medical and healthcare treatment. In this research our focus was to determine which area in a particular city had the most infection ratio based on certain behavioral and hygienic factors. By properly identifying an area which can potentially have more infection ratio significant measures can be taken to protecting the citizens of that area as well as institutional approaches can be instantiated to reduce the number of infection ratio. The proposed model can be implemented for all over Bangladesh to understand to rate of infection , ultimately to characterize the behavioral pattern of people against COVID-19 and take necessary measures in due time.
Presenter Certificate from Ajeenkya D Y Patil University, Pune, India.
Publication Certificate from Shodh Sarita : Journal of Arts, Humanities and Social Sciences
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3. Hasan, M. R. (2022). Android Malware Classification Addressing Repackaged Entities By The Evaluation Of Static Features And Multiple Machine Learning Algorithms. Springer LNNS. ISSN: 2367-3370.
(Presented paper at ICICT 2022 London, UK - Conference Dates February 23-24)
Abstract. Expanded usage and prevalence of Android apps allows developers of malware to create new ways in various applications to unleash malware in various packaged types. This malware causes various leakage of information and a loss of revenue. In addition, The discovered software is repeatedly launched by unethical developers after classifying the program as malware. Unluckily, The program still remains undetected even after being repackaged. In this research the topic of repackaging was discussed, emphasizing the implementation based on source code using the Bag-of-Words algorithm and testing the findings through machine learning. The findings of the assessment demonstrate comparatively improved result in this aspect than the existing implantation based on source code by adapting the Bag-of-words strategy and implementing some supplementary dataset pre-processing. A vocabulary for identifying the malicious code has been developed in this study. Bag-of-words was used to classify malware trends using custom implementation. The findings were instantiated using various algorithms of machine learning. The concept was eventually implemented in a practical application too. The suggested method sets out a fairly new methodology for examining source code for Android malware to tackle repackaging of malware.
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