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A survey of blockchain-based IoT eHealthcare : applications, research issues, and challenges

- Rahman, Md Shafiur, Islam, Md Amirul, Uddin, Md Ashraf, Stea, Giovanni

  • Authors: Rahman, Md Shafiur , Islam, Md Amirul , Uddin, Md Ashraf , Stea, Giovanni
  • Date: 2022
  • Type: Text , Journal article , Review
  • Relation: Internet of Things (Netherlands) Vol. 19, no. (2022), p.
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  • Description: Blockchain (BC) technology has recently emerged as an essential component for different applications, including healthcare and IoT, because of its decentralized ledger, source provenance, and tamper-proof nature. The Internet of Things (IoT) and BC have enabled health systems to expand their scalability and maintain consistency on a decentralized platform. As a result, many researchers have developed BC-enabled IoT eHealth systems and explored the application of BC technology in diverse fields of eHealthcare. This paper conducts a comprehensive survey on the emerging applications of BC technology in healthcare. We summarize applications, research issues, security threats, research challenges, opportunities, and the future scope of BC technologies in the IoT-enabled healthcare system when BC is adopted to handle the privacy and storage of current and future medical records. Furthermore, we analyze the state-of-the-art BC works in the medical area, assessing their benefits-drawbacks, and guiding future researchers to overcome the limitations of the existing articles. © 2022 Elsevier B.V.

An efficient hybrid system for anomaly detection in social networks

- Rahman, Md Shafiur, Halder, Sajal, Uddin, Ashraf, Acharjee, Uzzal

  • Authors: Rahman, Md Shafiur , Halder, Sajal , Uddin, Ashraf , Acharjee, Uzzal
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
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  • Description: Anomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system. © 2021, The Author(s).

Link prediction by correlation on social network

- Rahman, Md Shafiur, Dey, Leema Rani, Haider, Sajal, Uddin, Md Ashraf, Islam, Manowarul

  • Authors: Rahman, Md Shafiur , Dey, Leema Rani , Haider, Sajal , Uddin, Md Ashraf , Islam, Manowarul
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 2017 20th International Conference of Computer and Information Technology (ICCIT); Dhaka, Bangladesh; 22-24 December 2017 p. 1-6
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  • Description: In a social network, the topology of the network grows through the formation of the link. the connection between two nodes in a social network indicates a confidence in terms of the similarity of some activities. Generally, a new link in the social network is created from different perspectives such as familiarity, cohesiveness, geographical locations etc. The concept of the link in the social network has been utilized to discover the hidden meaning of different fields such as e-commerce, bioinformatics and information retrieval. The prediction of a new link between two nodes in the social network is normally accomplished based on the nature of the topology and the similarity function among the nodes is defined with the help of the number of common friends. In this paper, we propose two link prediction algorithms: Local Link Prediction Algorithm and Global Link prediction by taking into consideration of user's activities as well as the common friends. We apply two formulas called correlation based cScore and influential score based iScore to measure the similarity between the two predicted nodes. Finally, we analyze the performance of the proposed algorithms by using DBLP, PPI, PB, and USAir data sets and the experimental result attests that our link predicted algorithm outperforms over the existing algorithms.
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