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Dynamically recommending repositories for health data : a machine learning model

- Ashraf Uddin, Md, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venkicopyright

  • Authors: Ashraf Uddin, Md , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venkicopyright
  • Date: 2020
  • Type: Text , Conference proceedings
  • Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
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  • Description: Recently, a wide range of digital health record repositories has emerged. These include Electronic Health record managed by the government, Electronic Medical Record (EMR) managed by healthcare providers, Personal Health Record (PHR) managed directly by the patient and new Blockchain-based systems mainly managed by technologies. Health record repositories differ from one another on the level of security, privacy, and quality of services (QoS) they provide. Health data stored in these repositories also varies from patient to patient in sensitivity, and significance depending on medical, personal preference, and other factors. Decisions regarding which digital record repository is most appropriate for the storage of each data item at every point in time are complex and nuanced. The challenges are exacerbated with health data continuously streamed from wearable sensors. In this paper, we propose a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The model maps health data to be stored in the repositories. The mapping between health data features and characteristics of each repository is learned using a machine learning-based classifier mediated through clinical rules. Evaluation results demonstrate the model's feasibility. © 2020 ACM.
  • Description: E1

Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine

- Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter, Kamruzzaman, Joarder, Alazab, Ammar

  • Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 9, no. 1 (2020), p.
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  • Description: Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Partial undersampling of imbalanced data for cyber threats detection

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

  • Authors: Moniruzzaman, Md , Bagirov, Adil , Gondal, Iqbal
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
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  • Description: Real-time detection of cyber threats is a challenging task in cyber security. With the advancement of technology and ease of access to the internet, more and more individuals and organizations are becoming the target for various cyber attacks such as malware, ransomware, spyware. The target of these attacks is to steal money or valuable information from the victims. Signature-based detection methods fail to keep up with the constantly evolving new threats. Machine learning based detection has drawn more attention of researchers due to its capability of detecting new and modified attacks based on previous attack's behaviour. The number of malicious activities in a certain domain is significantly low compared to the number of normal activities. Therefore, cyber threats detection data sets are imbalanced. In this paper, we proposed a partial undersampling method to deal with imbalanced data for detecting cyber threats. © 2020 ACM.
  • Description: E1

A Decentralized Patient Agent Controlled Blockchain for Remote Patient Monitoring

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

  • Authors: Uddin, Md Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2019
  • Type: Text , Conference proceedings
  • Relation: 15th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2019 Vol. 2019-October, p. 207-214
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  • Description: Blockchain emerging for healthcare provides a secure, decentralized and patient driven record management system. However, the storage of data generated from IoT devices in remote patient management applications requires a fast consensus mechanism. In this paper, we propose a lightweight consensus mechanism and a decentralized patient software agent to control a remote patient monitoring (RPM) system. The decentralized RPM architecture includes devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) software replicated on the Smartphone, Fog and Cloud servers processes medical data to ensure reliable, secure and private communication. Performance analysis has been conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled remote patient monitoring system. © 2019 IEEE.
  • Description: E1

A lightweight blockchain based framework for underwater ioT

- Uddin, Md, Stranieri, Andrew, Gondal, Iqbal, Balasurbramanian, Venki

  • Authors: Uddin, Md , Stranieri, Andrew , Gondal, Iqbal , Balasurbramanian, Venki
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 8, no. 12 (2019), p.
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  • Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks

- Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter, Kamruzzaman, Joarder, Alazab, Ammar

  • Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 8, no. 11 (2019), p.
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  • Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

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

Blockchain leveraged task migration in body area sensor networks

- 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: 25th Asia-Pacific Conference on Communications, APCC 2019 p. 177-184
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  • Description: Blockchain technologies emerging for healthcare support secure health data sharing with greater interoperability among different heterogeneous systems. However, the collection and storage of data generated from Body Area Sensor Net-works(BASN) for migration to high processing power computing services requires an efficient BASN architecture. We present a decentralized BASN architecture that involves devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) replicated on the Smartphone, Fog and Cloud servers processes medical data and execute a task offloading algorithm by leveraging a Blockchain. Performance analysis is conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled BASN. © 2019 IEEE.
  • Description: E1

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
  • 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

Generative malware outbreak detection

- Park, Sean, Gondal, Iqbal, Kamruzzaman, Joarder, Oliver, Jon

  • Authors: Park, Sean , Gondal, Iqbal , Kamruzzaman, Joarder , Oliver, Jon
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019 Vol. 2019-February, p. 1149-1154
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  • Description: Recently several deep learning approaches have been attempted to detect malware binaries using convolutional neural networks and stacked deep autoencoders. Although they have shown respectable performance on a large corpus of dataset, practical defense systems require precise detection during the malware outbreaks where only a handful of samples are available. This paper demonstrates the effectiveness of the latent representations obtained through the adversarial autoencoder for malware outbreak detection. Using instruction sequence distribution mapped to a semantic latent vector, the model provides a highly effective neural signature that helps detecting variants of a previously identified malware within a campaign mutated with minor functional upgrade, function shuffling, or slightly modified obfuscations. The method demonstrates how adversarial autoencoder can turn a multiclass classification task into a clustering problem when the sample set size is limited and the distribution is biased. The model performance is evaluated on OS X malware dataset against traditional machine learning models. © 2019 IEEE.
  • Description: E1

Instruction cognitive one-shot malware outbreak detection

- Park, Sean, Gondal, Iqbal, Kamruzzaman, Joarder, Oliver, Jon

  • Authors: Park, Sean , Gondal, Iqbal , Kamruzzaman, Joarder , Oliver, Jon
  • Date: 2019
  • Type: Text , Conference paper
  • Relation: 26th International Conference on Neural Information Processing [ICONIP 2019] December 12-15 2019, Proceedings, Part IV Vol. 1142, p. 769-778
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  • Description: New malware outbreaks cannot provide thousands of training samples which are required to counter malware campaigns. In some cases, there could be just one sample. So, the defense system at the firing line must be able to quickly detect many automatically generated variants using a single malware instance observed from the initial outbreak by tatically inspecting the binary executables. As previous research works show, statistical features such as term frequency-inverse document frequency and n-gram are significantly vulnerable to attacks by mutation through reinforcement learning. Recent studies focus on raw binary executable as a base feature which contains instructions describing the core logic of the sample. However, many approaches using image-matching neural networks are insufficient due to the malware mutation technique that generates a large number of samples with high entropy data. Deriving instruction cognitive representation that disambiguates legitimate instructions from the context is necessary for accurate detection over raw binary executables. In this paper, we present a novel method of detecting semantically similar malware variants within a campaign using a single raw binary malware executable. We utilize Discrete Fourier Transform of instruction cognitive representation extracted from self-attention transformer network. The experiments were conducted with in-the-wild malware samples from ransomware and banking Trojan campaigns. The proposed method outperforms several state of the art binary classification models.
  • Description: E1

Mobile malware detection : an analysis of deep learning model

- Khoda, Mahbub, Kamruzzaman, Joarder, Gondal, Iqbal, Imam, Tasadduq, Rahman, Ashfaqur, IEEE

  • Authors: Khoda, Mahbub , Kamruzzaman, Joarder , Gondal, Iqbal , Imam, Tasadduq , Rahman, Ashfaqur , IEEE
  • Date: 2019
  • Type: Text , Book chapter
  • Relation: 2019 IEEE International Conference on Industrial Technology p. 1161-1166
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  • Description: Due to its widespread use, with numerous applications deployed everyday, smartphones have become an inevitable target of the malware developers. This huge number of applications renders manual inspection of codes infeasible; as such, researchers have proposed several malware detection techniques based on automatic machine learning tools. Deep learning has gained a lot of attention from the malware researchers due to its ability of capture complex relationships among inputs and outputs. However, deep learning models depend largely on several hyper-parameters (i.e., learning rate, batch size, dropout rate). Hence, it is of utmost importance to analyze the effect of these parameters on classifier performance. In this paper, we systematically studied the effect of these parameters along with the effect of network architecture. We showed that building arbitrary deep networks does not always improve classifier performance. We also determined the combination of hyper-parameters that yields best result. This study will be useful in building better deep neural network based model for malware classification.

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

One-shot malware outbreak detection using spatio-temporal isomorphic dynamic features

- Park, Sean, Gondal, Iqbal, Kamruzzaman, Joarder, Zhang, Leo

Robust malware defense in industrial IoT applications using machine learning with selective adversarial samples

- Khoda, Mahbub, Imam, Tasadduq, Kamruzzaman, Joarder, Gondal, Iqbal, Rahman, Ashfaqur

  • Authors: Khoda, Mahbub , Imam, Tasadduq , Kamruzzaman, Joarder , Gondal, Iqbal , Rahman, Ashfaqur
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industry Applications Vol. , no. (2019), p.
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  • Description: Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between sensors and actuators and application servers or cloud services. Machine learning models have been widely used to thwart malware attacks in such edge devices. However, these models are vulnerable to adversarial attacks where attackers craft adversarial samples by introducing small perturbations to malware samples to fool a classifier to misclassify them as benign applications. Literature on deep learning networks proposes adversarial retraining as a defense mechanism where adversarial samples are combined with legitimate samples to retrain the classifier. However, existing works select such adversarial samples in a random fashion which degrades the classifier's performance. This work proposes two novel approaches for selecting adversarial samples to retrain a classifier. One, based on the distance from malware cluster center, and the other, based on a probability measure derived from a kernel based learning (KBL). Our experiments show that both of our sample selection methods outperform the random selection method and the KBL selection method improves detection accuracy by 6%. Also, while existing works focus on deep neural networks with respect to adversarial retraining, we additionally assess the impact of such adversarial samples on other classifiers and our proposed selective adversarial retraining approaches show similar performance improvement for these classifiers as well. The outcomes from the study can assist in designing robust security systems for IIoT applications.

Selective adversarial learning for mobile malware

- Khoda, Mahbub, Imam, Tasadduq, Kamruzzaman, Joarder, Gondal, Iqbal, Rahman, Ashfaqur

  • Authors: Khoda, Mahbub , Imam, Tasadduq , Kamruzzaman, Joarder , Gondal, Iqbal , Rahman, Ashfaqur
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019 p. 272-279
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  • Description: Machine learning models, including deep neural networks, have been shown to be vulnerable to adversarial attacks. Adversarial samples are crafted from legitimate inputs by carefully introducing small perturbation to the input so that the classifier is fooled. Adversarial retraining, which involves retraining the classifier using adversarial samples, has been shown to improve the robustness of the classifier against adversarial attacks. However, it has been also shown that retraining with too many samples can lead to performance degradation. Hence, a careful selection of the adversarial samples that are used to retrain the classifier is necessary, yet existing works select these samples in a randomized fashion. In our work, we propose two novel approaches for selecting adversarial samples: based on the distance from cluster center of malware and based on the probability derived from a kernel based learning (KBL). Our experiment results show that both of our selective mechanisms for adversarial retraining outperform the random selection technique and significantly improve the classifier performance against adversarial attacks. In particular, selection with KBL delivers above 6% improvement in detection accuracy compared to random selection. The method proposed here has greater impact in designing robust machine learning system for security applications. © 2019 IEEE.
  • Description: E1

Vulnerability modelling for hybrid IT systems

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

  • Authors: Ur-Rehman, Attiq , Gondal, Iqbal , Kamruzzuman, Joarder , Jolfaei, Alireza , IEEE
  • Date: 2019
  • Type: Text , Book chapter
  • Relation: 2019 IEEE International Conference on Industrial Technology 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.

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 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