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An efficient selective miner consensus protocol in blockchain oriented iot smart monitoring

- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne; Australia; 13th-15th February 2019 Vol. 2019-February, p. 1135-1142
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  • Description: Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
  • Description: Proceedings of the IEEE International Conference on Industrial Technology

Comparative study on object tracking algorithms for mobile robot navigation in GPS-denied environment

- Hewawasam, Hasitha, Ibrahim, Yousif, Kahandawa, Gayan, Choudhury, Tanveer

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousif , Kahandawa, Gayan , Choudhury, Tanveer
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 19-26
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  • Description: This paper presents a comparative study conducted on the performance of the commonly used object tracking and location prediction algorithms for mobile robot navigation in a dynamically cluttered and GPS-denied mining environment. The study was done to test the different algorithms for the same set criteria (such as accuracy and computational time) under the same conditions.The identified commonly used algorithms for object tracking and location prediction of moving objects used in this investigation are Kalman filter (KF), extended Kalman filter (EKF) and particle filter (PF). The study results of those algorithms are analyzed and discussed in this paper. A trade-off was apparent. However, in overall performance KF has shown its competitiveness.The result from the study has found that the KF based algorithm provides better performance in terms of accuracy in tracking dynamic objects under commonly used benchmarks. This finding can be used in development of an efficient robot navigation algorithm.
  • Description: Proceedings of the IEEE International Conference on Industrial Technology

Cybersecurity indexes for eHealth

- Burke, Wendy, Oseni, Taiwo, Jolfaei, Alireza, Gondal, Iqbal

  • Authors: Burke, Wendy , Oseni, Taiwo , Jolfaei, Alireza , Gondal, Iqbal
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 2019 Australasian Computer Science Week Multiconference, ACSW 2019; Sydney, Australia; 29th-31st January 2019 p. 1-8
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  • Description: This study aimed to explore the cybersecurity landscape to identify cybersecurity indexes that may be relevant to the health industry. While the healthcare sector poses security concerns regarding patients' records, cybersecurity in the healthcare sector has not been given much consideration. Cybersecurity indexes are a survey that measures security preparedness and capabilities of a country or organisation. An index is made up of a series of questions, often broken into categories. These categories target areas such as law, technical responses, organisational threats, capacity building and social context. Some indexes provide ranking capabilities against other countries, while others directly evaluate what it means to be cyber-ready. In this paper, cybersecurity indexes were reviewed regarding the level of assessment (country level/organisation level), and their consideration of the wider community, the health sector, and their appearance in academic literature. Results from this study found that there was no consistency between the indexes investigated, with each index having a diverse number of categories and indicators. Some indexes resulted in a score; others did not rank their results in league tables. Evidence to calculate the level of adherence was often obtained from secondary sources, with four of the country indexes using both primary and secondary sources. Eight (out of fourteen) indexes measured wider community indicators and only one index specifically measured eHealth services. Findings from the initial systematic review suggest that hardly any peer-reviewed journal articles exist on the topic of cybersecurity indexes. The paper concludes that most of the indexes studied are broad and do not consider the eHealth sector specifically. Each index relies on a different process to gauge cybersecurity, with little to no academic rigour. It is expected that this research will contribute to the current (limited) literature addressing cybersecurity indexes.
  • Description: ACM International Conference Proceeding Series

Detection and compensation of covert service-degrading intrusions in cyber physical systems through intelligent adaptive control

- Farivar, Faezeh, Haghighi, Mohammad, Barchinezhad, Soheila, Jolfaei, Alireza

  • Authors: Farivar, Faezeh , Haghighi, Mohammad , Barchinezhad, Soheila , Jolfaei, Alireza
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1143-1148
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  • Description: Cyber-Physical Systems (CPS) are playing important roles in the critical infrastructure now. A prominent family of CPSs are networked control systems in which the control and feedback signals are carried over computer networks like the Internet. Communication over insecure networks make system vulnerable to cyber attacks. In this article, we design an intrusion detection and compensation framework based on system/plant identification to fight covert attacks. We collect error statistics of the output estimation during the learning phase of system operation and after that, monitor the system behavior to see if it significantly deviates from the expected outputs. A compensating controller is further designed to intervene and replace the classic controller once the attack is detected. The proposed model is tested on a DC motor as the plant and is put against a deception signal amplification attack over the forward link. Simulation results show that the detection algorithm well detects the intrusion and the compensator is also successful in alleviating the attack effects.

Discovering regularities from traditional chinese medicine prescriptions via bipartite embedding model

- Ruan, Chunyang, Ma, Jiangang, Wang, Ye, Zhang, Yanchun, Yang, Yun

  • Authors: Ruan, Chunyang , Ma, Jiangang , Wang, Ye , Zhang, Yanchun , Yang, Yun
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: International Joint Conferences on Artificial Intelligence (IJCAI-49); Macao, China; 10th-16th August 2019; published in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) p. 3346-3352
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  • Description: Regularities analysis for prescriptions is a significant task for traditional Chinese medicine (TCM), both in inheritance of clinical experience and in improvement of clinical quality. Recently, many methods have been proposed for regularities discovery, but this task is challenging due to the quantity, sparsity and free-style of prescriptions. In this paper, we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions. We model this task as a relation prediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relationships. Specifically, we first establish a heterogeneous network with herbs and symptoms as its nodes. We develop a bipartite embedding model termed HS2Vec to detect regularities, which explores multiple relations of herbherb, and herb-symptom based on the heterogeneous network. Experiments on four real-world datasets demonstrate that the proposed framework is very effective for regularities discovery.

Generating linked data repositories using UML artifacts

- Khan, Aqsa, Malik, Saleem

  • Authors: Khan, Aqsa , Malik, Saleem
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 1st Intelligent Technologies and Applications, Intap 2018; Bahawalpur, Pakistan; 23rd-25th October 2018; published in Communications in Computer and Information Science book Series Vol. 932, p. 142-152
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  • Description: The usability of diagrams and models is increasing day by day, because of this we experience problem in searching and accessing from large size repositories of diagrams and models of software systems. This research might be helpful to search and access the diagrams and models in bigger repositories. For this purpose, this research developed linked data repositories which contain UML (Unified Modeling Language) artifacts, these artifacts are being organized with using UML class model. In particular, UML is being broadly applied to data modeling in many application domains, and generating linked data repositories from the UML class model is becoming a challenging task in the context of semantic web. This paper proposes an approach, in which we will build a construction tool by joining the characteristics of RDF (Resource Description Framework) and UML. Firstly we will formally define design artifacts and linked data repositories. After that we will propose a construction tool in which we will extract UML artifacts, these UML class model further transforms into the corresponding RDFs. The generated RDF linked data then will be verified by using W3C RDF, this is a validating service used to generate and verify the RDF triples and graphs. Finally, the proposed construction tool will be implemented with few experiments and research is validated using W3C RDF validating service. The proposed approach aims to give such a design that may facilitate the users to customize linked data repositories so that diagrams and models could be examined from large size data.

Integrating biological heuristics and gene expression data for gene regulatory network inference

- Zarnegar, Armita, Jelinek, Herbert, Vamplew, Peter, Stranieri, Andrew

  • Authors: Zarnegar, Armita , Jelinek, Herbert , Vamplew, Peter , Stranieri, Andrew
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2019 Australasian Computer Science Week Multiconference, ACSW 2019; Sydney, Australia; 29th-31st January 2019 p. 1-10
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  • Description: Gene Regulatory Networks (GRNs) offer enhanced insight into the biological functions and biochemical pathways of cells associated with gene regulatory mechanisms. However, obtaining accurate GRNs that explain gene expressions and functional associations remains a difficult task. Only a few studies have incorporated heuristics into a GRN discovery process. Doing so has the potential to improve accuracy and reduce the search space and computational time. A technique for GRN discovery that integrates heuristic information into the discovery process is advanced. The approach incorporates three elements: 1) a novel 2D visualized coexpression function that measures the association between genes; 2) a post-processing step that improves detection of up, down and self-regulation and 3) the application of heuristics to generate a Hub network as the backbone of the GRN. Using available microarray and next generation sequencing data from Escherichia coli, six synthetic benchmark GRN datasets were generated with the neighborhood addition and cluster addition methods available in SynTReN. Results of the novel 2D-visualization co-expression function were compared with results obtained using Pearson's correlation and mutual information. The performance of the biological genetics-based heuristics consisting of the 2D-Visualized Co-expression function, post-processing and Hub network was then evaluated by comparing the performance to the GRNs obtained by ARACNe and CLR. The 2D-Visualized Co-expression function significantly improved gene-gene association matching compared to Pearson's correlation coefficient (t = 3.46, df = 5, p = 0.02) and Mutual Information (t = 4.42, df = 5, p = 0.007). The heuristics model gave a 60% improvement against ARACNe (p = 0.02) and CLR (p = 0.019). Analysis of Escherichia coli data suggests that the GRN discovery technique proposed is capable of identifying significant transcriptional regulatory interactions and the corresponding regulatory networks.

Isolation set-kernel and its application to multi-instance learning

- Xu, Bi-Cun, Ting, Kaiming, Zhou, Zhi-Hua

  • Authors: Xu, Bi-Cun , Ting, Kaiming , Zhou, Zhi-Hua
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 25th ACM SIGKDD International Conferencce on Knowledge Discovery and Data Mining, KDD 2019; Anchorage, United States; 4th-8th August 2019 p. 941-949
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  • Description: Set-level problems are as important as instance-level problems. The core in solving set-level problems is: how to measure the similarity between two sets. This paper investigates data-dependent kernels that are derived directly from data. We introduce Isolation Set Kernel which is solely dependent on data distribution, requiring neither class information nor explicit learning. In contrast, most current set-similarities are not dependent on the underlying data distribution. We theoretically analyze the characteristic of Isolation Set-Kernel. As the set-kernel has a finite feature map, we show that it can be used to speed up the set-kernel computation significantly. We apply Isolation Set-Kernel to Multi-Instance Learning (MIL) using SVM classifier, and demonstrate that it outperforms other set-kernels or other solutions to the MIL problem.

Measuring soil strain using fibre optic sensors

- Costa, Susanga, Kahandawa, Gayan, Chen, Jian, Xue, Jianfeng

  • Authors: Costa, Susanga , Kahandawa, Gayan , Chen, Jian , Xue, Jianfeng
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 8th International Congress on Environmental Geotechnics, ICEG 2018; Hangzhou, China; 28th October-1st November 2018; part of the Environmental Science and Engineering book series p. 43-50
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  • Description: Monitoring subsurface soil movement is important in many geotechnical engineering applications such as stability of slopes, road embankments and settlement in foundations. Soil displacement measurement is also helpful in understanding the formation of shrinkage cracks. Clay soils undergo shrinkage during drying and experience substantial stresses and strains, which results in shrinkage cracks. This paper presents a novel approach to measure soil strain using Fibre Bragg grating (FBG) sensors. In the experiments described, FBG sensors have been used to investigate the strain development in clay during drying. FBG sensors are fabricated in the core region of specially fabricated single mode low-loss germanium doped silicate optical fibres. The grating is the laser-inscribed region with a periodically varying refractive index, which reflects a specific light wavelength. Due to the applied strain, ε, there is a change in the wavelength which can be measured and is directly proposal to strain. Kaolin clay, mixed with water close to the liquid limit, was allowed to dry under room temperature. The specimens were prepared in thin, long linear shrinkage moulds. FBG sensors were placed inside soil at the centre of the specimen. The strain development during drying underwent four phases moving from compression to tension. An oscillating nature of strain was also observed throughout the drying process. Results obtained are useful to develop analytical solutions to describe stress-strain behavior of drying soil. © Springer Nature Singapore Pte Ltd. 2019.

Multi-source cyber-attacks detection using machine learning

- Taheri, Sona, Gondal, Iqbal, Bagirov, Adil, Harkness, Greg, Brown, Simon, Chi, Chihung

  • Authors: Taheri, Sona , Gondal, Iqbal , Bagirov, Adil , Harkness, Greg , Brown, Simon , Chi, Chihung
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1167-1172
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  • Description: The Internet of Things (IoT) has significantly increased the number of devices connected to the Internet ranging from sensors to multi-source data information. As the IoT continues to evolve with new technologies number of threats and attacks against IoT devices are on the increase. Analyzing and detecting these attacks originating from different sources needs machine learning models. These models provide proactive solutions for detecting attacks and their sources. In this paper, we propose to apply a supervised machine learning classification technique to identify cyber-attacks from each source. More precisely, we apply the incremental piecewise linear classifier that constructs boundary between sources/classes incrementally starting with one hyperplane and adding more hyperplanes at each iteration. The algorithm terminates when no further significant improvement of the separation of sources/classes is possible. The construction and usage of piecewise linear boundaries allows us to avoid any possible overfitting. We apply the incremental piecewise linear classifier on the multi-source real world cyber security data set to identify cyber-attacks and their sources.
  • Description: Proceedings of the IEEE International Conference on Industrial Technology

PU-shapelets : Towards pattern-based positive unlabeled classification of time series

- Liang, Shen, Zhang, Yanchun, Ma, Jiangang

Reversible data hiding in encrypted images based on image partition and spatial correlation

- Song, Chang, Zhang, Yifeng, Lu, Guojun

  • Authors: Song, Chang , Zhang, Yifeng , Lu, Guojun
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 17th International Workshop on Digital Forensics and Watermarking, IWDW 2018; Jeju Island, South Korea; 22nd-24th October 2018; Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11378 LNCS, p. 180-194
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  • Description: Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images because of its better protection of privacy compared with traditional RDH methods directly operated in original images. In several RDH algorithms, prediction-error expansion (PEE) is proved to be superior to other methods in terms of embedding capacity and distortion of marked image and multiple histograms modification (MHM) can realize adaptive selection of expansion bins which depends on image content in the modification of a sequence of histograms. Therefore, in this paper, we propose an efficient RDH method in encrypted images by combining PEE and MHM, and design corresponding mode of image partition. We first divide the image into three parts: W (for embedding secret data), B (for embedding the least significant bit(LSB) of W) and G (for generating prediction-error histograms). Then, we apply PEE and MHM to embed the LSB of W to reserve space for secret data. Next, we encrypt the image and change the LSB of W to realize the embedding of secret data. In the process of extraction, the reversibility of image and secret data can be guaranteed. The utilization of correlation between neighbor pixels and embedded order decided by the smoothness of pixel in part W contribute to the performance of our method. Compared to the existing algorithms, experimental results show that the proposed method can reduce distortion to the image at given embedding capacity especially at low embedding capacity.

Security hardening of implantable cardioverter defibrillators

- Jaffar, Iram, Usman, Muhammad, Jolfaei, Alireza

  • Authors: Jaffar, Iram , Usman, Muhammad , Jolfaei, Alireza
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1173-1178
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  • Description: Contemporary healthcare has witnessed a wide deployment of Implantable Cardioverter Defibrillators (ICDs), which have the capability to be controlled remotely, making them equally accessible from both home and hospitals. The therapeutic benefits of ICDs seem to outweigh potential security concerns, yet overlooking the presence of malicious attacks cannot be justified. This study investigates the scenario where an adversary falsifies a controller command and sends instructions to issue high electric shocks in succession. We propose a novel security hardening mechanism to protect data communications between ICD and controller from malicious data manipulations. Our proposed method verifies the correctness of an external command with respect to the history of heart rhythms. The proposed method is evaluated using real data. Multi-aspect analyses show the effectiveness of the proposed scheme.
  • Description: Proceedings of the IEEE International Conference on Industrial Technology

Vulnerability modelling for hybrid IT systems

- Ur-Rehman, Attiq, Gondal, Iqbal, Kamruzzuman, Joarder, Jolfaei, Alireza

  • Authors: Ur-Rehman, Attiq , Gondal, Iqbal , Kamruzzuman, Joarder , Jolfaei, Alireza
  • Date: 2019
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1186-1191
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  • Description: Common vulnerability scoring system (CVSS) is an industry standard that can assess the vulnerability of nodes in traditional computer systems. The metrics computed by CVSS would determine critical nodes and attack paths. However, traditional IT security models would not fit IoT embedded networks due to distinct nature and unique characteristics of IoT systems. This paper analyses the application of CVSS for IoT embedded systems and proposes an improved vulnerability scoring system based on CVSS v3 framework. The proposed framework, named CVSSIoT, is applied to a realistic IT supply chain system and the results are compared with the actual vulnerabilities from the national vulnerability database. The comparison result validates the proposed model. CVSSIoT is not only effective, simple and capable of vulnerability evaluation for traditional IT system, but also exploits unique characteristics of IoT devices.
  • Description: Proceedings of the IEEE International Conference on Industrial Technology

A kernel-based approach for content-based image retrieval

- Karmakar, Priyabrata, Teng, Shyh, Lu, Guojun, Zhang, Dengsheng

  • Authors: Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun , Zhang, Dengsheng
  • Date: 2018
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 2018 International Conference on Image and Vision Computing New Zealand; Auckland, New Zealand; 19th-21st November 2018 p. 1-6
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  • Description: Content-based image retrieval (CBIR) is a popular approach to retrieve images based on a query. In CBIR, retrieval is executed based on the properties of image contents (e.g. gradient, shape, color, texture) which are generally encoded into image descriptors. Among the various image descriptors, histogram-based descriptors are very popular. However, they suffer from the limitation of coarse quantization. In contrast, the use of kernel descriptors (KDES) is proven to be more effective than histogram-based descriptors in other applications, e.g. image classification. This is because, in the KDES framework, instead of the quantization of pixel attributes, each pixel equally takes part in the similarity measurement between two images. In this paper, we propose an approach for how the conventional KDES and its improved version can be used for CBIR. In addition, we have provided a detailed insight into the effectiveness of improved kernel descriptors. Finally, our experiment results will show that kernel descriptors are significantly more effective than histogram-based descriptors in CBIR.

A novel no-reference subjective quality metric for free viewpoint video using human eye movement

- Podder, Pallab, Paul, Manoranjan, Murshed, Manzur

  • Authors: Podder, Pallab , Paul, Manoranjan , Murshed, Manzur
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 8th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2017; Wuhan, China; 20th-24th November 2017; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10749 LNCS, p. 237-251
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  • Description: The free viewpoint video (FVV) allows users to interactively control the viewpoint and generate new views of a dynamic scene from any 3D position for better 3D visual experience with depth perception. Multiview video coding exploits both texture and depth video information from various angles to encode a number of views to facilitate FVV. The usual practice for the single view or multiview quality assessment is characterized by evolving the objective quality assessment metrics due to their simplicity and real time applications such as the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM). However, the PSNR or SSIM requires reference image for quality evaluation and could not be successfully employed in FVV as the new view in FVV does not have any reference view to compare with. Conversely, the widely used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain knowledge, and many other factors that may actively influence on actual assessment. To address this limitation, in this work, we devise a no-reference subjective quality assessment metric by simply exploiting the pattern of human eye browsing on FVV. Over different quality contents of FVV, the participants eye-tracker recorded spatio-temporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the proposed QMET performs better than the SSIM and MOS in terms of assessing different aspects of coded video quality for a wide range of FVV contents.
  • Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A novel perceptual dissimilarity measure for image retrieval

- Shojanazeri, Hamid, Zhang, Dengsheng, Teng, Shyh, Aryal, Sunil, Lu, Guojun

  • Authors: Shojanazeri, Hamid , Zhang, Dengsheng , Teng, Shyh , Aryal, Sunil , Lu, Guojun
  • Date: 2018
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 2018 International Conference on Image and Vision Computing New Zealand, IVCNZ 2018; Auckland, New Zealand; 19th-21st November 2018 Vol. 2018-November, p. 1-6
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  • Description: Similarity measure is an important research topic in image classification and retrieval. Given a type of image features, a good similarity measure should be able to retrieve similar images from the database while discard irrelevant images from the retrieval. Similarity measures in literature are typically distance based which measure the spatial distance between two feature vectors in high dimensional feature space. However, this type of similarity measures do not have any perceptual meaning and ignore the neighborhood influence in the similarity decision making process. In this paper, we propose a novel dissimilarity measure, which can measure both the distance and perceptual similarity of two image features in feature space. Results show the proposed similarity measure has a significant improvement over the traditional distance based similarity measure commonly used in literature.
  • Description: International Conference Image and Vision Computing New Zealand

A patient agent to manage blockchains for remote patient monitoring

- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 7th International Conference on Global Telehealth, GT 2018; Colombo, Sri Lanka; 10th-11th October 2018; published in Studies in Health Technology and Informatics Vol. 254, p. 105-115
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  • Description: Continuous monitoring of patient's physiological signs has the potential to augment traditional medical practice, particularly in developing countries that have a shortage of healthcare professionals. However, continuously streamed data presents additional security, storage and retrieval challenges and further inhibits initiatives to integrate data to form electronic health record systems. Blockchain technologies enable data to be stored securely and inexpensively without recourse to a trusted authority. Blockchain technologies also promise to provide architectures for electronic health records that do not require huge government expenditure that challenge developing nations. However, Blockchain deployment, particularly with streamed data challenges existing Blockchain algorithms that take too long to place data in a block, and have no mechanism to determine whether every data point in every stream should be stored in such a secure way. This article presents an architecture that involves a Patient Agent, coordinating the insertion of continuous data streams into Blockchains to form an electronic health record.
  • Description: Studies in Health Technology and Informatics

A server side solution for detecting webInject : A machine learning approach

- Moniruzzaman, Md, Bagirov, Adil, Gondal, Iqbal, Brown, Simon

Alignment-free cancellable template generation for fingerprint based authentication

- Nazmul, Rumana, Islam, Rafiqul, Chowdhury, Ahsan

  • Authors: Nazmul, Rumana , Islam, Rafiqul , Chowdhury, Ahsan
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 4th International Conference on Information Systems Security and Privacy, ICISSP 2018; Madeira, Portugal; 22nd-24th January 2018 Vol. 2018-January, p. 361-366
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  • Description: With the emergence and extensive deployment of biometric based user authentication system, ensuring the security of biometric template is becoming a growing concern in research community. One approach of securing biometric data is cancellable biometric which transforms the original biometric features into a non-invertible form for enrolment and matching. However, most of the schemes for generating cancellable template are alignment-based requiring an accurate alignment of query and enrolled images, which is very difficult to achieve. In this paper, we propose an alignment-free technique for generating revocable fingerprint template that exploits the local features i.e., minutiae details in a fingerprint image. A rotation and translation invariant values are extracted from the neighbouring region of each minutia. The invariant values are then used as inputs in a transformation function and combined with a stored and a user-specific key based random vectors using the type and orientation information of the minutiae. Hence, by varying the stored and user-specific keys in the transformation, multiple application-specific templates can be generated to preserve users’ privacy. Besides, if the transformed template is compromised, a new template can be reissued by assigning different keys for transformation to achieve revocability. Furthermore, the proposed approach preserves the actual geometric relationships between the enrolled and query templates even after transformation and offers reasonable recognition rate. Experiments conducted on FVC2000 DB1 demonstrate that the proposed method exhibits promising performance in terms of recognition accuracy, computational complexity, security along with diversity, revocability and non-invertibility that are the key issues of cancellable template generation.
  • Description: ICISSP 2018 - Proceedings of the 4th International Conference on Information Systems Security and Privacy