M
Muttukrishnan Rajarajan
Researcher at City University London
Publications - 279
Citations - 5737
Muttukrishnan Rajarajan is an academic researcher from City University London. The author has contributed to research in topics: Cloud computing & Encryption. The author has an hindex of 32, co-authored 267 publications receiving 4817 citations. Previous affiliations of Muttukrishnan Rajarajan include Universities UK & University College London.
Papers
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Journal ArticleDOI
Patient Privacy Protection Using Anonymous Access Control Techniques
TL;DR: The proposed protocol protects the patient privacy with a secure anonymous authentication to healthcare services and medical record registries according to the European and the UK legislations, where the patient real identity is not disclosed with the distributed patient medical records.
Proceedings ArticleDOI
Privacy in Mobile Web Services eHealth
TL;DR: A novel architecture that integrates 3GPP UMTS mobile technology with Web services is proposed that will help address authentication and privacy concern within eHealth environment.
Journal ArticleDOI
Optical sensor for pH monitoring using a layer-by-layer deposition technique emphasizing enhanced stability and re-usability
Nahid Raoufi,Nahid Raoufi,Frederic Surre,Muttukrishnan Rajarajan,Tong Sun,Kenneth T. V. Grattan +5 more
TL;DR: In this paper, the performance of three different stabilization approaches used for the development of an effective wavelength-dependent pH-sensitive optical sensor was compared and an improvement in performance and in shelf-life, stability and reusability of the sensor was achieved by the addition of two bilayers of APTMS/SiO2 (3-Aminopropyl-trimethoxy silane/Silica nanoparticle) in the work carried out and the results of the investigation undertaken are reported.
Proceedings ArticleDOI
SmartARM: A smartphone-based group activity recognition and monitoring scheme for military applications
Anandarup Mukherjee,Sudip Misra,P. Mangrulkar,Muttukrishnan Rajarajan,Yogachandran Rahulamathavan +4 more
TL;DR: This work establishes the optimum position of smartphone placement on a soldier, the optimum classifier to use from a given set of options, and the minimum sensors or sensor combinations to use for reliable detection of physical activities, while reducing the data-load on the network.
Journal ArticleDOI
Ransomware detection using deep learning based unsupervised feature extraction and a cost sensitive Pareto Ensemble classifier
Umme Zahoora,Asifullah Khan,Muttukrishnan Rajarajan,Saddam Hussain Khan,Muhammad Asam,Tauseef Jamal +5 more
TL;DR: In this article , a cost-sensitive Pareto ensemble strategy, CSPE-R, was proposed to detect zero-day ransomware attacks by exploiting the unsupervised deep Contractive Auto Encoder (CAE) to transform the underlying varying feature space to a more uniform and core semantic feature space.