Publications

JournalS

Title: Deepfake Detection: A Systematic Literature Review

Authors: Md. Shohel Rana, Mohammad Nur Nobi, Beddhu Murali and Andrew H. Sung

Journal: IEEE Access, (IF: 3.367)

Indexed by: Current Contents/Engineering, Computing and Technology Edition, Directory of Open Access Journals, EBSCOhost, Ei Compendex, Google Scholar, IET Inspec, Journal Citation Reports/Science Edition, Science Citation Index Expanded, Scopus, Web of Science

AbstractOver the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc., malicious users have also exploited them for unlawful or nefarious purposes. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people. The manipulated, high-quality and realistic videos have become known recently as Deepfake. Various approaches have since been described in the literature to deal with the problems raised by Deepfake. To provide an updated overview of the research works in Deepfake detection, we conduct a systematic literature review (SLR) in this paper, summarizing 112 relevant articles from 2018 to 2020 that presented a variety of methodologies. We analyze them by grouping them into four different categories: deep learning-based techniques, classical machine learning-based methods, statistical techniques, and blockchain-based techniques. We also evaluate the performance of the detection capability of the various methods with respect to different datasets and conclude that the deep learning-based methods outperform other methods in Deepfake detection.

Title: Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

Authors: Md. Shohel Rana and Andrew H. Sung

Journal: Vietnam Journal of Computer Science, 2020

Indexed by: World Scientific

Abstract—Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cyber criminals to create malware for different purposes. As of late, wide-running inquiries about have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble based learning techniques acquire preferred outcome over recently revealed outcome by directing the DREBIN dataset, and in this manner gives a solid premise to building compelling instruments for Android malware detection.

Title: Malware Analysis on Android using Supervised Machine Learning Techniques

Authors: Md. Shohel Rana and Andrew H. Sung

Journal: International Journal of Computer and Communication Engineering, Volume 7, Number 4, 2018

Indexed by: EI -(INSPEC, IET), Google Scholar, Crossref, Engineering & Technology Digital Library, ProQuest, and Electronic Journals Library

Abstract—In recent years, a widespread research is conducted with the growth of malware resulted in the domain of malware analysis and detection in Android devices. Android, a mobile-based operating system currently having more than one billion active users with a high market impact that have inspired the expansion of malware by cyber criminals. Android implements a different architecture and security controls to solve the problems caused by malware, such as unique user ID (UID) for each application, system permissions, and its distribution platform Google Play. There are numerous ways to violate that fortification, and how the complexity of creating a new solution is enlarged while cybercriminals progress their skills to develop malware. A community including developer and researcher has been evolving substitutes aimed at refining the level of safety where numerous machine learning algorithms already been proposed or applied to classify or cluster malware including analysis techniques, frameworks, sandboxes, and systems security. One of the most promising techniques is the implementation of artificial intelligence solutions for malware analysis. In this paper, we evaluate numerous supervised machine learning algorithms by implementing a static analysis framework to make predictions for detecting malware on Android

Title: Distributed Database Problems, Approaches and Solutions – A Study

Authors: Md. Shohel Rana, Mohammad Khaled Sohel and Md. Shohel Arman

Journal: International Journal of Machine Learning and Computing (IJMLC), Volume 8, Number 5, 2018

Indexed by: Scopus, EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library

Abstract—The distributed database system is the combination of two fully divergent approaches to data processing: database systems and computer network to deliver transparency of distributed and replicated data. The key determination of this paper is to achieve data integration and data distribution transparency, study and recognize the problems and approaches of the distributed database system. The distributed database is evolving technology to store and retrieve data from several location or sites with maintaining the dependability and obtain ability of the data. In the paper we learn numerous problems in distributed database concurrency switch, design, transaction management problem etc. Distributed database allows to end worker to store and retrieve data anywhere in the network where database is located, during storing and accessing any data from distributed database through computer network faces numerous difficulties happens e.g. deadlock, concurrency and data allocation using fragmentation, clustering with multiple or single nodes, replication to overcome these difficulties it is essential to design the distributed database sensibly way.

Title: An Enhanced Model for Inpainting on Digital Images Using Dynamic Masking

Authors: Md. Shohel Rana, Md. Maruf Hassan and Touhid Bhuiyan

Journal: Journal of Communications, Vol. 12, no. 4, pp. 248-253, 2017

Indexed by: Scopus, DBLP, ULRICH's Periodicals Directory, IET INSPEC, Engineering Village, Google Scholar

Abstract—In the digital world, inpainting is the algorithm used to replace or reconstruct lost, corrupted, or deteriorated parts of image data. Of the various proposed inpainting methods, convolution methods are the simplest and most efficient. In this paper, an enhanced inpainting model based on convolution theorem is proposed for digital images that preserves the edge and effectively estimates the lost or damaged parts of an image. In the proposed algorithm, a mask image is created dynamically to detect the image area to inpaint where most of the algorithms detect the missing parts of the image manually. Studies confirm the simplicity and effectiveness of our method, which also produces results that are comparable to those produced using other methods.

Title: A New Filtering Technique for denoising Speckle Noise from Medical Images Based on Adaptive and Anisotropic Diffusion Filter

Authors: Mohammad Motiur Rahman, Md. Shohel Rana, Md. Aminul Islam, Mohammad Masudur Rahman and Mehedi Hasan Talukder

Journal: International Journal of Research in Computer and Communication Technology, Vol. 2, Issue 9, 2013 (IF: 3.751)

Indexed by: Thomson Reuters Web of Science, Index Copernicus International, INFORMATICS, J-Gate, CiteSeer, WorldCat, BASE, Computer Science Directory, etc.

Abstract—This is a preliminary study and the objective of this study has been to compare the performance of some of the primitive and fundamentally different post acquisition image enhancement algorithms as applied to different ultrasound (US) images. Such a comparison would help to decide as to which algorithm could be useful for clinicians, and in evaluating the role of different US images enhancement in a soft-copy environment. In this study, 3 types of US images (Liver, kidney & Abdomen) were taken, and 5 fundamentally different and widely employed image enhancement techniques were applied on these images. As the principal objective of image enhancement is to obtain an image with a high content of visual detail, a multi point rank-order method was used to identify small differences or trends in observations. Among the different algorithms, the proposed modified Anisotropic diffusion filtering outperformed than other techniques .

Book Chapters

Title: Evaluating Machine Learning Models on the Ethereum Blockchain for Android Malware Detection

Authors: Md. Shohel Rana, Charan Gudla and Andrew H. Sung

Book: Arai K., Bhatia R., Kapoor S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 998. Springer, Cham. DOI: 10.1007/978-3-030-22868-2_34

Indexed by: ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and SpringerLink.

Abstract—Android, a most popular mobile operating system having more than billions of active users with a high market impression that have encouraged the cyber-criminals to push the malware into this operating system. In recent years, an extensive research is conducted in the domain of malware analysis and detection in Android devices. And Android already have developed and implemented numerous security controls to solve the problems. In this paper, we apply a new Blockchain technology to evaluate and exchange various machine learning model for a particular dataset by interacting with smart contracts that offer a reward. This allows contributors to submit their solution to the Blockchain by training with selected machine learning models for a reward in a trustless manner. This experimentation leads to provide a strong basis for building effective tools for Android malware detection.

Title: Evaluation of Tree-Based Machine Learning Classifiers for Android Malware Detection

Authors: Md. Shohel Rana, Sheikh Shah Mohammad Motiur Rahman and Andrew H. Sung

Book: Nguyen N., Pimenidis E., Khan Z., Trawiński B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science, vol 11056. Springer, Cham. DOI: 10.1007/978-3-319-98446-9_35

Indexed by: ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and SpringerLink.

Abstract—Android is a most popular mobile-based operating system with billions of active users, which has encouraged hackers and cyber-criminals to push the malware into this operating system. Accordingly, extensive research has been conducted on malware analysis and detection for Android in recent years; and Android has developed and implemented numerous security controls to deal with the problems, including unique ID (UID) for each application, system permissions, and its distribution platform Google Play. In this paper, we evaluate four tree-based machine learning algorithms for detecting Android malware in conjunction with a substring-based feature selection method for the classifiers. In the experiments 11,120 apps of the DREBIN dataset were used where 5,560 contain malware samples and the rest are benign. It is found that the Random Forest classifier outperforms the best previously reported result (around 94% accuracy, obtained by SVM) with 97.24% accuracy, and thus provides a strong basis for building effective tools for Android malware detection.

Title: e-School: Design and Implementation of Web-Based Teaching Institution for Enhancing E-Learning Experiences

Authors: Md. Shohel Rana, Touhid Bhuiyan and A. K. M. Z. Satter

Book: Nguyen N., Pimenidis E., Khan Z., Trawiński B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science, vol 11055. Springer, Cham, DOI: 10.1007/978-3-319-98443-8_10

Indexed by: ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and SpringerLink.

Abstract—Numerous technological improvements have found in the academic setting which removes the binding of educators and students from time and space. Day by day, the rate of drop-out is increasing by separation of them from learning. The main effort of our project is to fulfill a mission allowing individuals to learn or educate without physically attending. In this paper, we build an innovative web-based application enabling the teachers and students with numerous educational exercises using computer/smart devices. Using this application teacher, students and parents can collaborate on a single podium, while teachers can counsel with students in a real-time and share the performance and actions with parents as well as administrators. A method to modification of our traditional education system but not the replacement of teaching, it’s only the enhancement idea for teaching helps to learn easily and fill up their liking.

Conferences

Title: Deepfake Detection Using Machine Learning Algorithms

Authors: Md. Shohel Rana, Beddhu Murali and Andrew H. Sung

Proceedings: 10th International Congress on Advanced Applied Informatics (IIAI-AAI 2021), July 11th-16th, 2021, Japan (Online)

Indexed by: EI Compendex, Web of Science (ISI), Inspec, DBLP, and Scopus.

Abstract—Deepfake, a new video manipulation technique, has drawn much attention recently. Among the unlawful or nefarious applications, Deepfake has been used for spreading misinformation, fomenting political discord, smearing opponents, or even blackmailing. As the technology becomes ever more sophisticated and the apps for creating them more readily available, detecting Deepfake has become a challenging task and researchers have proposed various deep learning (DL) methods for detection. Though the DL-based approach can achieve good solutions, this paper presents the results of our study indicating that traditional machine learning (ML) techniques alone can obtain superior performance in detecting Deepfake. The ML-based approach is based on the standard methods of feature development and feature selection, followed by training, tuning, and testing an ML classifier. The advantage of the ML approach is that it allows better understandability and interpretability of the model with reduced computational cost. We present results on several Deepfake datasets that are obtained relatively fast with comparable or superior performance to the state-of-the-art DLbased methods: 99.84% accuracy on FaceForecics++, 99.38% accuracy on DFDC, 99.66% accuracy on VDFD, and 99.43% on Celeb-DF datasets. Our results suggest that an effective system for detecting Deepfakes can be built using traditional ML methods.

Title: DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection

Authors: Md. Shohel Rana and Andrew H. Sung

Proceedings: 7th IEEE International Conference on Cyber Security and Cloud Computing (IEEE CSCloud 2020), August 1st-3rd, 2020, New York, USA

Indexed by: EI Compendex, Web of Science, and Scopus.

Abstract—Recent advances in technology have made the deep learning (DL) models available for use in a wide variety of novel applications; for example, generative adversarial network (GAN) models are capable of producing hyper-realistic images, speech, and even videos, such as the so-called “Deepfake” produced by GANs with manipulated audio and/or video clips, which are so realistic as to be indistinguishable from the real ones in human perception. Aside from innovative and legitimate applications, there are numerous nefarious or unlawful ways to use such counterfeit contents in propaganda, political campaigns, cybercrimes, extortion, etc. To meet the challenges posed by Deepfake multimedia, we propose a deep ensemble learning technique called DeepfakeStack for detecting such manipulated videos. The proposed technique combines a series of DL based state-of-art classification models and creates an improved composite classifier. Based on our experiments, it is shown that DeepfakeStack outperforms other classifiers by achieving an accuracy of 99.65% and AUROC of 1.0 score in detecting Deepfake. Therefore, our method provides a solid basis for building a Realtime Deepfake detector.

Title: Evaluating Machine Learning Models for Android Malware Detection – A Comparison Study

Authors: Md. Shohel Rana, Charan Gudla and Andrew H. Sung

Proceedings: International Conference on Network, Communication, and Computing, December 14-16, 2018, Taipei, Taiwan

Indexed by: Scopus, EI Compendex, Google Scholar, etc.

Abstract—Android is a most widespread mobile-based operating system having more than billions of active users with a high market impression that stimulated the cyber criminals to impulsion the malware into this operating system. During a couple of recent years, wide-ranging researches are conducted in the domain of malware analysis and detection in Android devices while Android already have implemented various security controls to solve the problems includes unique user ID (UID) for each application, system permissions, and its distribution platform Google Play. In this paper, we optimize and evaluate different types of machine learning algorithms by implementing a static analysis training different classifier in order to separate malware from non-malware applications running on Android OS. In our evaluation, we use 11,120 applications where 5,560 malware samples and 5,560 benign samples of DREBIN dataset and the accuracy we get more than 94% that will help to detect an application is malicious or not. This experiment also leads to developing a real-time malware scanner to decide whether an Android app can be installed or executed on Android devices.

Title: MinFinder: A New Approach in Sorting Algorithm

Authors: Md. Shohel Rana, Md Altab Hossin, S M Hasan Mahmud, Hosney Jahan, A. K. M. Zaidi Satter, Touhid Bhuiyan

Proceedings: 9th Annual International Conference of Information and Communication Technology, January 11-13, 2019, Guangxi, China, DOI: 10.1016/j.procs.2019.06.020

Indexed by: ISI Proceedings (ISTP/CPCI), EI, DBLP, SCOPUS, Google Scholar

Abstract—Sorting a set of unsorted items is a task that happens in computer programming while a computer program has to follow a sequence of precise directions to accomplish that task. In order to find things quickly by making extreme values easy to see, sorting algorithm refers to specifying a technique to arrange the data in a particular order or format where maximum of communal orders is in arithmetic or lexicographical order. A lot of sorting algorithms has already been developed and these algorithms have enhanced the performance in the factors including time and space complexity, stability, correctness, definiteness, finiteness, effectiveness, etc. A new approach has been proposed in this paper in sorting algorithm called MinFinder to overcome some of the downsides and performs better compared to some conventional algorithms in terms of stability, computational time, complexity analysis.

Title: A new method to handle Facebook users in the distributed database system

Authors: Md. Shohel Rana, Md Altab Hossin, S M Hasan Mahmud and Hosney Jahan

Proceedings: 9th IEEE International Conference on Software Engineering and Service Science, November 23-25, 2018, Beijing, China

Indexed by: EI Compendex, Google Scholar, etc.

Abstract—The hasty growth of technology and social media has carried momentous changes to humanoid communication. Facebook, the largest online social media in the last few years has more than 200 million active users where more than 3.5 billion minutes are spent on Facebook daily. Since the competence of Facebook is subject to mostly on the processing of the massive volume of data. The volume of data is increasing day to day as well as the number of inactive and fake users. In this paper, we propose a new model using distributed data-base concept for management of users and their activities. This proposed model helps to keep the system scalable, reliable, and faster and let the Facebook accessible from anywhere with high accessibility.

Title: Android Malware Detection using Stacked Generalization

Authors: Md. Shohel Rana, Charan Gudla and Andrew H. Sung

Proceedings: 27th International Conference on Software Engineering and Data Engineering, October 8-10, 2018, New Orleans, United States

Indexed by: Scopus, EI, INSPEC, and DBLP

Abstract—Malware detection plays a key role in Android device security due to the popularity of Android with billions of active users that encouraging cyber criminals to push the malware into this operating system. The growth of malware is now becoming a serious problem. Recently, extensive research has been conducted to detect malware on Android devices using machine learning based methods profoundly depending on domain knowledge for manually extracting malicious features. In this paper, we evaluate tree-based machine learning algorithms by Stacked Generalization concept for detecting malware on Android in conjunction with implementing a substring based method for training the algorithms. We perform experiments on 11,120 samples containing 5,560 malware samples and 5,560 benign samples provided by DREBIN dataset on 8 malware families. The evaluation results show how stacked generalization achieves 97.92% validation accuracy for malware detection on DREBIN dataset.

Title: Defense Techniques Against Cyber Attacks on Unmanned Aerial Vehicles

Authors: Charan Gudla, Md. Shohel Rana and Andrew H. Sung

Proceedings: 16th International Conference on Embedded Systems, Cyber-physical Systems and Applications, July 30-August 2, Las Vegas, United States

Indexed by: ACM, EBSCO, ACSE, CSREA

Abstract—Unmanned aerial vehicles (UAVs) or drones serve a wide range of applications from surveillance to combat missions. UAVs carry, collect, or communicate sensitive information which becomes a target for the attacks. Securing the communication network between the operator and the UAV is therefore crucial. So far, the networks used in most UAV applications are static, which allows more time and opportunity for the adversary to perform cyber-attacks on the UAV. In this paper we propose to study Moving Target Defense (MTD) technique against cyber-attacks on the drones including wireless network encryption and intrusion detection system. MTD technique change the static nature of the systems to increase both the difficulty and the cost (effort, time, and resources) of mounting attacks. For illustration purpose, a well-known cyber attack is performed on a popular commercial drone and results are presented to show the network vulnerabilities, damages caused due to the attacks and defense techniques to prevent the attacks .

Title: Inpainting on Digital Images using Convolution based Method – A Comparison Study

Authors: Md. Shohel Rana, A. K. M. Zaidi Satter, Zaman Wahid and Touhid Bhuiyan

Proceedings: International Conference on Biomedical Engineering and Bioinformatics, September 14-16, 2017, Bangkok, Thailand

Indexed by: EI Compendex, Scopus and ISI CPCS

Abstract—The goals and applications of inpainting are numerous, from the restoration of damaged paintings and photographs to the removal/replacement of selected object. Reconstruction of missing or damaged portions of images is an ancient practice used extensively in artwork restoration. Also known as inpainting or retouching, this activity consists of filling in the missing areas or modifying the damaged ones in a non-detectable way by an observer not familiar with the original images. Applications of image inpainting range from restoration of photographs, films and paintings, to removal of occlusions, such as text, subtitles, stamps and publicity from images. Inpainting, the technique of modifying an image in an undetectable form, is as ancient as art itself.

Title: A Proposed PST Model for Enhancing E-Learning Experiences

Authors: Touhid Bhuyian, Md. Shohel Rana, Kaushik Sarker and Zaman Wahid

Proceedings: International Conference on Education and Distance Learning, July 4 – 6, 2017, Maldives

Indexed by: EI Compendex, Scopus and ISI CPCS

Abstract—The advent of 21st century has brought various technological improvements across several areas. This movement has transformed the way material was used to be connected. The outcome is predominantly noticeable in the academic setting where educators and students are no more bound by time and space. Using current innovative tools students can learn in a more time-efficient way than eternally. As a result, they are untying from learning, and the rate of drop-out is increasing day by day. At present, a large number of educators are going for technology driven academic classrooms where they can switch the teaching and learning activities more productively. In this paper, we are proposing a model for building innovative education products which enables the teachers to engage students with different educational exercises and interactive activities using computer/smart devices. This Parent-Student-Teacher model will enable educators to create, organize and share their curricula, lesson plans and classroom materials; also, the teachers to share knowledge, exchange ideas and get feedback from other educators where teachers can give performance points to students in a real-time manner and share the performance and behavior of their students with the parents and administrators. It enables teachers, students and parents to collaborate on a single platform by interactive white boards where they can give lessons write notes and save their work with just the tip of finger.

Title: Comparing the performance of different ultrasonic images enhancement for speckle noise reduction in ultrasound images using techniques: a preference study

Authors: Md. Shohel Rana, Kaushik Sarker, Touhid Bhuiyan and Md. Maruf Hassan

Proceedings: Second International Workshop on Pattern Recognition (SPIE 10443), 104430 W (June 19, 2017), doi: 10.1117/12.2280277

Indexed by: EI Compendex, Scopus and ISI Proceedings

Abstract—Diagnostic ultrasound (US) is an important tool in today's sophisticated medical diagnostics. Nearly every medical discipline benefits itself from this relatively inexpensive method that provides a view of the inner organs of the human body without exposing the patient to any harmful radiations. Medical diagnostic images are usually corrupted by noise during their acquisition and most of the noise is speckle noise. To solve this problem, instead of using adaptive filters which are widely used, No-Local Means based filters have been used to de-noise the images. Ultrasound images of four organs such as Abdomen, Ortho, Liver, Kidney, Brest and Prostrate of a Human body have been used and applied comparative analysis study to find out the output. These images were taken from Siemens SONOLINE G60 S System and the output was compared by matrices like SNR, RMSE, PSNR IMGQ and SSIM. The significance and compared results were shown in a tabular format.

Title: An Enhanced Model for Inpainting on Digital Images Using Dynamic Masking (Awarded by Best Paper and Best Presenter)

Authors: Md. Shohel Rana, Md. Maruf Hassan and Touhid Bhuiyan

Proceedings: International Conference on Frontiers of Image Processing, 2017 March 3-5, Kathmandu, Nepal

Indexed by: EI Compendex, Scopus and ISI CPCS

Abstract—In the digital world, inpainting is the algorithm used to replace or reconstruct lost, corrupted, or deteriorated parts of image data. Of the various proposed inpainting methods, convolution methods are the simplest and most efficient. In this paper, an enhanced inpainting model based on convolution theorem is proposed for digital images that preserves the edge and effectively estimates the lost or damaged parts of an image. In the proposed algorithm, a mask image is created dynamically to detect the image area to inpaint where most of the algorithms detect the missing parts of the image manually. Studies confirm the simplicity and effectiveness of our method, which also produces results that are comparable to those produced using other methods.