- Truong, Mandy, Gibbs, Lisa, Pradel, Veronika, Morris, Michal, Gwatirisa, Pauline, Tadic, Maryanne, De Silva, Andrea, Hall, Martin, Young, Dana, Riggs, Elisha, Calache, Hanny, Gussy, Mark, Watt, Richard, Gondal, Iqbal, Waters, Elizabeth
- Authors: Truong, Mandy , Gibbs, Lisa , Pradel, Veronika , Morris, Michal , Gwatirisa, Pauline , Tadic, Maryanne , De Silva, Andrea , Hall, Martin , Young, Dana , Riggs, Elisha , Calache, Hanny , Gussy, Mark , Watt, Richard , Gondal, Iqbal , Waters, Elizabeth
- Date: 2017
- Type: Text , Journal article
- Relation: Health Promotion Practice Vol. 18, no. 3 (2017), p. 466-475
- Full Text: false
- Reviewed:
- Description: Cultural competence is an important aspect of health service access and delivery in health promotion and community health. Although a number of frameworks and tools are available to assist health service organizations improve their services to diverse communities, there are few published studies describing organizational cultural competence assessments and the extent to which these tools facilitate cultural competence. This article addresses this gap by describing the development of a cultural competence assessment, intervention, and evaluation tool called the Cultural Competence Organizational Review (CORe) and its implementation in three community sector organizations. Baseline and follow-up staff surveys and document audits were conducted at each participating organization. Process data and organizational documentation were used to evaluate and monitor the experience of CORe within the organizations. Results at follow-up indicated an overall positive trend in organizational cultural competence at each organization in terms of both policy and practice. Organizations that are able to embed actions to improve organizational cultural competence within broader organizational plans increase the likelihood of sustainable changes to policies, procedures, and practice within the organization. The benefits and lessons learned from the implementation of CORe are discussed. © 2017, Society for Public Health Education.
A data mining approach for machine fault diagnosis based on associated frequency patterns
- Rashid, Md. Mamunur, Amar, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder
- Authors: Rashid, Md. Mamunur , Amar, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2016
- Type: Text , Journal article
- Relation: Applied Intelligence Vol. 45, no. 3 (2016), p. 638-651
- Full Text: false
- Reviewed:
- Description: Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis. © 2016, Springer Science+Business Media New York.
A framework for data privacy and security accountability in data breach communications
- Thomas, Louise, Gondal, Iqbal, Oseni, Taiwo, Firmin, Sally
- Authors: Thomas, Louise , Gondal, Iqbal , Oseni, Taiwo , Firmin, Sally
- Date: 2022
- Type: Text , Journal article
- Relation: Computers and Security Vol. 116, no. (2022), p.
- Full Text: false
- Reviewed:
- Description: Organisations need to take steps to protect the privacy and security of the personal information they hold. However, when data is breached, how do individuals know whether the organisation took reasonable steps to protect their data? When breached organisations notify affected individuals, this communication is likely to be one of the few windows into the incident from the outside and can become an important artefact for research. This desktop study aimed to consider the extent to which publicly available Australian data breach communications reflect data privacy and security best practices. This paper presents a brief review of literature and government guidance on data security and privacy best practices, along with the results of a qualitative content analysis of 33 publicly available Australian data breach communications. This analysis illustrated that there was little reflection of data privacy and security practices. Literature, government guidance and the content analysis were used to inform and develop a new voluntary framework for organisations. This consists of a series of evaluation questions divided into two broad categories: responsible data management and responsible portrayal of the breach. The framework has the potential to help organisations plan the inclusion of data privacy and security management aspects in their data breach communications. This could assist organisations to address their legal and ethical responsibility to account for their actions in managing privacy and security of the personal data they hold. © 2022
A lightweight blockchain based framework for underwater ioT
- Uddin, Md, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Md , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 8, no. 12 (2019), p.
- Full Text:
- Reviewed:
- 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.
- Authors: Uddin, Md , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 8, no. 12 (2019), p.
- Full Text:
- Reviewed:
- 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.
- Full Text:
- Reviewed:
- 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.
- 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.
- Full Text:
- Reviewed:
- 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.
A smart priority-based traffic control system for emergency vehicles
- Karmakar, Gour, Chowdhury, Abdullahi, Kamruzzaman, Joarder, Gondal, Iqbal
- Authors: Karmakar, Gour , Chowdhury, Abdullahi , Kamruzzaman, Joarder , Gondal, Iqbal
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 21, no. 14 (2021), p. 15849-15858
- Full Text: false
- Reviewed:
- Description: Unwanted events on roads, such as incidents and increased traffic jams, can cause human lives and economic loss. For efficient incident management, it is essential to send Emergency Vehicles (EVs) to the incident place as quickly as possible. To reduce incidence clearance time, several approaches exist to provide a clear pathway to EVs mainly fitted with RFID sensors in the urban areas. However, they neither assign priority to the EVs based on the type and severity of an incident nor consider the effect on other on-road traffic. To address this issue, in this paper, we introduce an Emergency Vehicle Priority System (EVPS) by determining the priority level of an EV based on the type and the severity of an incident, and estimating the number of necessary signal interventions while considering the impact of those interventions on the traffic in the roads surrounding the EV's travel path. We present how EVPS determines the priority code and a new algorithm to estimate the number of green signal interventions to attain the quickest incident response while concomitantly reducing impact on others. A simulation model is developed in Simulation of Urban Mobility (SUMO) using the real traffic data of Melbourne, Australia, captured by various sensors. Results show that our system recommends appropriate number of intervention that can reduce emergency response time significantly. © 2001-2012 IEEE.
Abrasion modeling of multiple-point defect dynamics for machine condition monitoring
- Yaqub, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder, Loparo, Kenneth
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Loparo, Kenneth
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Reliability Vol. 62, no. 1 (2013), p. 171-182
- Full Text: false
- Reviewed:
- Description: Multiple-point defects and abraded surfaces in rotary machinery induce complex vibration signatures, and have a tendency to mislead defect diagnosis models. A challenging problem in machine defect diagnosis is to model and study defect signature dynamics in the case of multiple-point defects and surface abrasion. In this study, a multiple-point defect model (MPDM) that characterizes the dynamics of n-point bearing defects is proposed. MPDM is further extended to model degradation in a rotating machine as a special case of multiple-point defects. Analytical and experimental results for multiple-point defects and abrasions show that the location of the fundamental defect frequency shifts depending upon the relative location of the defects and width of the abrasive region. This variation in the defect frequency results in a degradation of the defect detection accuracy of the defect diagnostic model. Based on envelope detection analysis, a modification in existing defect diagnostic models is recommended to nullify the impact of multiple-point defects, and general abrasion in machine components.
An adaptive self-configuration scheme for severity invariant machine fault diagnosis
- Yaqub, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Reliability Vol. 62, no. 1 (2013), p. 116-126
- Full Text: false
- Reviewed:
- Description: Vibration signals, used for abnormality detection in machine health monitoring (MHM), exhibit significant variation with varying fault severity. This signal variation causes overlap among the features characterizing different types of faults, which results in severe performance degradation of the fault diagnostic model. In this paper, a wavelet based adaptive training set and feature selection (WATF) self-configuration scheme is presented, which selects the optimum wavelet decomposition level, and employs adaptive selection of the training set and features. Optimal wavelet decomposition level selection is such that the maximum fault signature-signal energy bands are achieved. The severity variant features, which could cause detrimental class overlap for MHM, are avoided using adaptive selection of the training set and features based on the location of a test data in feature space. WATF uses Support Vector Machines (SVM) to build the fault diagnostic model, and its performance and robustness has been tested with data having different severity levels. Comparative studies of WATF with eight existing fault diagnosis schemes show that, for publicly available data sets, WATF achieves higher fault detection accuracy, even when training and testing data sets belong to different severity levels.
An exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds : A protocol paper for Teeth Tales
- Gibbs, Lisa, Waters, Elizabeth, De Silva, Andrea, Riggs, Elisha, Moore, Laurence, Armit, Christine, Johnson, Britt, Morris, Michal, Calache, Hanny, Gussy, Mark, Young, Dana, Tadic, Maryanne, Christian, Bradley, Gondal, Iqbal, Watt, Richard, Pradel, Veronika, Truong, Mandy, Gold, Lisa
- Authors: Gibbs, Lisa , Waters, Elizabeth , De Silva, Andrea , Riggs, Elisha , Moore, Laurence , Armit, Christine , Johnson, Britt , Morris, Michal , Calache, Hanny , Gussy, Mark , Young, Dana , Tadic, Maryanne , Christian, Bradley , Gondal, Iqbal , Watt, Richard , Pradel, Veronika , Truong, Mandy , Gold, Lisa
- Date: 2014
- Type: Text , Journal article
- Relation: BMJ Open Vol. 4, no. 3 (2014), p. 1-14
- Full Text:
- Reviewed:
- Description: Introduction: Inequalities are evident in early childhood caries rates with the socially disadvantaged experiencing greater burden of disease. This study builds on formative qualitative research, conducted in the Moreland/Hume local government areas of Melbourne, Victoria 2006-2009, in response to community concerns for oral health of children from refugee and migrant backgrounds. Development of the community-based intervention described here extends the partnership approach to cogeneration of contemporary evidence with continued and meaningful involvement of investigators, community, cultural and government partners. This trial aims to establish a model for child oral health promotion for culturally diverse communities in Australia. Methods and analysis: This is an exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds. Families from an Iraqi, Lebanese or Pakistani background with children aged 1-4 years, residing in metropolitan Melbourne, were invited to participate in the trial by peer educators from their respective communities using snowball and purposive sampling techniques. Target sample size was 600. Moreland, a culturally diverse, inner-urban metropolitan area of Melbourne, was chosen as the intervention site. The intervention comprised peer educator led community oral health education sessions and reorienting of dental health and family services through cultural Competency Organisational Review (CORe). Ethics and dissemination: Ethics approval for this trial was granted by the University of Melbourne Human Research Ethics Committee and the Department of Education and Early Childhood Development Research Committee. Study progress and output will be disseminated via periodic newsletters, peer-reviewed research papers, reports, community seminars and at National and International conferences. Trial registration number: Australian New Zealand Clinical Trials Registry (ACTRN12611000532909).
- Authors: Gibbs, Lisa , Waters, Elizabeth , De Silva, Andrea , Riggs, Elisha , Moore, Laurence , Armit, Christine , Johnson, Britt , Morris, Michal , Calache, Hanny , Gussy, Mark , Young, Dana , Tadic, Maryanne , Christian, Bradley , Gondal, Iqbal , Watt, Richard , Pradel, Veronika , Truong, Mandy , Gold, Lisa
- Date: 2014
- Type: Text , Journal article
- Relation: BMJ Open Vol. 4, no. 3 (2014), p. 1-14
- Full Text:
- Reviewed:
- Description: Introduction: Inequalities are evident in early childhood caries rates with the socially disadvantaged experiencing greater burden of disease. This study builds on formative qualitative research, conducted in the Moreland/Hume local government areas of Melbourne, Victoria 2006-2009, in response to community concerns for oral health of children from refugee and migrant backgrounds. Development of the community-based intervention described here extends the partnership approach to cogeneration of contemporary evidence with continued and meaningful involvement of investigators, community, cultural and government partners. This trial aims to establish a model for child oral health promotion for culturally diverse communities in Australia. Methods and analysis: This is an exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds. Families from an Iraqi, Lebanese or Pakistani background with children aged 1-4 years, residing in metropolitan Melbourne, were invited to participate in the trial by peer educators from their respective communities using snowball and purposive sampling techniques. Target sample size was 600. Moreland, a culturally diverse, inner-urban metropolitan area of Melbourne, was chosen as the intervention site. The intervention comprised peer educator led community oral health education sessions and reorienting of dental health and family services through cultural Competency Organisational Review (CORe). Ethics and dissemination: Ethics approval for this trial was granted by the University of Melbourne Human Research Ethics Committee and the Department of Education and Early Childhood Development Research Committee. Study progress and output will be disseminated via periodic newsletters, peer-reviewed research papers, reports, community seminars and at National and International conferences. Trial registration number: Australian New Zealand Clinical Trials Registry (ACTRN12611000532909).
Blockchain leveraged decentralized IoT eHealth framework
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Internet of Things Vol. 9, no. March 2020 p. 100159
- Full Text:
- Reviewed:
- Description: Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data.
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Internet of Things Vol. 9, no. March 2020 p. 100159
- Full Text:
- Reviewed:
- Description: Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data.
- Gibbs, Lisa, De Silva, Andrea, Christian, Bradley, Gold, Lisa, Gussy, Mark, Moore, Laurence, Calache, Hanny, Young, Dana, Riggs, Elisha, Tadic, Maryanne, Watt, Richard, Gondal, Iqbal, Waters, Elizabeth
- Authors: Gibbs, Lisa , De Silva, Andrea , Christian, Bradley , Gold, Lisa , Gussy, Mark , Moore, Laurence , Calache, Hanny , Young, Dana , Riggs, Elisha , Tadic, Maryanne , Watt, Richard , Gondal, Iqbal , Waters, Elizabeth
- Date: 2016
- Type: Text , Journal article
- Relation: Community Dental Health Vol. 33, no. 2 (2016), p. 100-106
- Full Text: false
- Reviewed:
- Description: Early Childhood Caries (ECC) is the most common, preventable disease of childhood. It can affect children’s health and wellbeing and children from migrant families may be at greater risk of developing ECC. Objective: To describe ECC in children from migrant families, and explore possible influences. Basic research design: Cross-sectional analysis of caries data collected as baseline data for an oral health promotion study. Participants: The analysis sample included 630 1-4 year-old children clustered within 481 Iraqi, Lebanese and Pakistani families in Melbourne, Australia. Method: Child participants received a community-based visual dental examination. Parents completed a self-administered questionnaire on demographics, ethnicity, and oral health knowledge, behaviour and attitudes. Main outcome measure: Child caries experience. Bivariate associations between oral health behaviours and ethnicity were tested for significance using chi-square. Multivariate logistic regression analyses were performed to identify associations with ECC, adjusting for demographic variables and accounting for clustering by family. Results: Overall, 34% of children in the sample experienced caries (both non-cavitated and cavitated). For all caries lesions, parent’ length of residence in Australia, consumption of sweet drinks and parental education remained as independent predictors of child caries experience. Adding sugar to drinks was an additional risk factor for cavitation. Ethnicity was associated with some individual oral health behaviours suggesting cultural influences on health, however the relationship was not independent of other predictors. Conclusion: Culturally competent oral health promotion interventions should aim to support migrant families with young children, and focus on reducing sweet drink consumption. © BASCD 2016.
Continuous patient monitoring with a patient centric agent : A block architecture
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 32700-32726
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 32700-32726
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.
Cross-compiler bipartite vulnerability search
- Authors: Black, Paul , Gondal, Iqbal
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 11 (2021), p.
- Full Text:
- Reviewed:
- Description: Open-source libraries are widely used in software development, and the functions from these libraries may contain security vulnerabilities that can provide gateways for attackers. This paper provides a function similarity technique to identify vulnerable functions in compiled programs and proposes a new technique called Cross-Compiler Bipartite Vulnerability Search (CCBVS). CCBVS uses a novel training process, and bipartite matching to filter SVM model false positives to improve the quality of similar function identification. This research uses debug symbols in programs compiled from open-source software products to generate the ground truth. This automatic extraction of ground truth allows experimentation with a wide range of programs. The results presented in the paper show that an SVM model trained on a wide variety of programs compiled for Windows and Linux, x86 and Intel 64 architectures can be used to predict function similarity and that the use of bipartite matching substantially improves the function similarity matching performance. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Black, Paul , Gondal, Iqbal
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 11 (2021), p.
- Full Text:
- Reviewed:
- Description: Open-source libraries are widely used in software development, and the functions from these libraries may contain security vulnerabilities that can provide gateways for attackers. This paper provides a function similarity technique to identify vulnerable functions in compiled programs and proposes a new technique called Cross-Compiler Bipartite Vulnerability Search (CCBVS). CCBVS uses a novel training process, and bipartite matching to filter SVM model false positives to improve the quality of similar function identification. This research uses debug symbols in programs compiled from open-source software products to generate the ground truth. This automatic extraction of ground truth allows experimentation with a wide range of programs. The results presented in the paper show that an SVM model trained on a wide variety of programs compiled for Windows and Linux, x86 and Intel 64 architectures can be used to predict function similarity and that the use of bipartite matching substantially improves the function similarity matching performance. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Cyberattack triage using incremental clustering for intrusion detection systems
- Taheri, Sona, Bagirov, Adil, Gondal, Iqbal, Brown, Simon
- Authors: Taheri, Sona , Bagirov, Adil , Gondal, Iqbal , Brown, Simon
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Information Security Vol. 19, no. 5 (2020), p. 597-607
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
- Reviewed:
- Description: Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
- Description: This research was conducted in Internet Commerce Security Laboratory (ICSL) funded by Westpac Banking Corporation Australia. In addition, the research by Dr. Sona Taheri and A/Prof. Adil Bagirov was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP190100580).
- Authors: Taheri, Sona , Bagirov, Adil , Gondal, Iqbal , Brown, Simon
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Information Security Vol. 19, no. 5 (2020), p. 597-607
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
- Reviewed:
- Description: Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
- Description: This research was conducted in Internet Commerce Security Laboratory (ICSL) funded by Westpac Banking Corporation Australia. In addition, the research by Dr. Sona Taheri and A/Prof. Adil Bagirov was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP190100580).
Decentralized content sharing among tourists in visiting hotspots
- Kaisar, Shahriar, Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal
- Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 79, no. (2017), p. 25-40
- Full Text:
- Reviewed:
- Description: Content sharing with smart mobile devices using decentralized approach enables users to share contents without the use of any fixed infrastructure, and thereby offers a free-of-cost platform that does not add to Internet traffic which, in its current state, is approaching bottleneck in its capacity. Most of the existing decentralized approaches in the literature consider spatio-temporal regularity in human movement patterns and pre-existing social relationship for the sharing scheme to work. However, such predictable movement patterns and social relationship information are not available in places like tourist spots where people visit only for a short period of time and usually meet strangers. No works exist in literature that deals with content sharing in such environment. In this work, we propose a content sharing approach for such environments. The group formation mechanism is based on users' interest score and stay probability in the individual region of interest (ROI) as well as on the availability and delivery probabilities of contents in the group. The administrator of each group is selected by taking into account its probability of stay in the ROI, connectivity with other nodes, its trustworthiness and computing and energy resources to serve the group. We have also adopted an incentive mechanism as encouragement that awards nodes for sharing and forwarding contents. We have used network simulator NS3 to perform extensive simulation on a popular tourist spot in Australia which facilitates a number of activities. The proposed approach shows promising results in sharing contents among tourists, measured in terms of content hit, delivery success rate and latency.
- Description: Content sharing with smart mobile devices using decentralized approach enables users to share contents without the use of any fixed infrastructure, and thereby offers a free-of-cost platform that does not add to Internet traffic which, in its current state, is approaching bottleneck in its capacity. Most of the existing decentralized approaches in the literature consider spatio-temporal regularity in human movement patterns and pre-existing social relationship for the sharing scheme to work. However, such predictable movement patterns and social relationship information are not available in places like tourist spots where people visit only for a short period of time and usually meet strangers. No works exist in literature that deals with content sharing in such environment. In this work, we propose a content sharing approach for such environments. The group formation mechanism is based on users' interest score and stay probability in the individual region of interest (ROI) as well as on the availability and delivery probabilities of contents in the group. The administrator of each group is selected by taking into account its probability of stay in the ROI, connectivity with other nodes, its trustworthiness and computing and energy resources to serve the group. We have also adopted an incentive mechanism as encouragement that awards nodes for sharing and forwarding contents. We have used network simulator NS3 to perform extensive simulation on a popular tourist spot in Australia which facilitates a number of activities. The proposed approach shows promising results in sharing contents among tourists, measured in terms of content hit, delivery success rate and latency. © 2016
- Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 79, no. (2017), p. 25-40
- Full Text:
- Reviewed:
- Description: Content sharing with smart mobile devices using decentralized approach enables users to share contents without the use of any fixed infrastructure, and thereby offers a free-of-cost platform that does not add to Internet traffic which, in its current state, is approaching bottleneck in its capacity. Most of the existing decentralized approaches in the literature consider spatio-temporal regularity in human movement patterns and pre-existing social relationship for the sharing scheme to work. However, such predictable movement patterns and social relationship information are not available in places like tourist spots where people visit only for a short period of time and usually meet strangers. No works exist in literature that deals with content sharing in such environment. In this work, we propose a content sharing approach for such environments. The group formation mechanism is based on users' interest score and stay probability in the individual region of interest (ROI) as well as on the availability and delivery probabilities of contents in the group. The administrator of each group is selected by taking into account its probability of stay in the ROI, connectivity with other nodes, its trustworthiness and computing and energy resources to serve the group. We have also adopted an incentive mechanism as encouragement that awards nodes for sharing and forwarding contents. We have used network simulator NS3 to perform extensive simulation on a popular tourist spot in Australia which facilitates a number of activities. The proposed approach shows promising results in sharing contents among tourists, measured in terms of content hit, delivery success rate and latency.
- Description: Content sharing with smart mobile devices using decentralized approach enables users to share contents without the use of any fixed infrastructure, and thereby offers a free-of-cost platform that does not add to Internet traffic which, in its current state, is approaching bottleneck in its capacity. Most of the existing decentralized approaches in the literature consider spatio-temporal regularity in human movement patterns and pre-existing social relationship for the sharing scheme to work. However, such predictable movement patterns and social relationship information are not available in places like tourist spots where people visit only for a short period of time and usually meet strangers. No works exist in literature that deals with content sharing in such environment. In this work, we propose a content sharing approach for such environments. The group formation mechanism is based on users' interest score and stay probability in the individual region of interest (ROI) as well as on the availability and delivery probabilities of contents in the group. The administrator of each group is selected by taking into account its probability of stay in the ROI, connectivity with other nodes, its trustworthiness and computing and energy resources to serve the group. We have also adopted an incentive mechanism as encouragement that awards nodes for sharing and forwarding contents. We have used network simulator NS3 to perform extensive simulation on a popular tourist spot in Australia which facilitates a number of activities. The proposed approach shows promising results in sharing contents among tourists, measured in terms of content hit, delivery success rate and latency. © 2016
Dependable large scale behavioral patterns mining from sensor data using Hadoop platform
- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2017
- Type: Text , Journal article
- Relation: Information Sciences Vol. 379, no. (2017), p. 128-145
- Full Text: false
- Reviewed:
- Description: Wireless sensor networks (WSNs) will be an integral part of the future Internet of Things (loT) environment and generate large volumes of data. However, these data would only be of benefit if useful knowledge can be mined from them. A data mining framework for WSNs includes data extraction, storage and mining techniques, and must be efficient and dependable. In this paper, we propose a new type of behavioral pattern mining technique from sensor data called regularly frequent sensor patterns (RFSPs). RFSPs can identify a set of temporally correlated sensors which can reveal significant knowledge from the monitored data. A distributed data extraction model to prepare the data required for mining RFSPs is proposed, as the distributed scheme ensures higher availability through greater redundancy. The tree structure for RFSP is compact requires less memory and can be constructed using only a single scan through the dataset, and the mining technique is efficient with low runtime. Current mining techniques in the literature on sensor data employ a single memory-based sequential approach and hence are not efficient. Moreover, usage of the. MapReduce model for the distributed solution has not been explored extensively. Since MapReduce is becoming the de facto model for computation on large data, we also propose a parallel implementation of the RFSP mining algorithm, called RFSP on Hadoop (RFSP-H), which uses a MapReduce-based framework to gain further efficiency. Experiments conducted to evaluate the compactness and performance of the data extraction model, RFSP-tree and RFSP-H mining show improved results. (C) 2016 Elsevier Inc. All rights reserved.
Dynamic resource allocation for improved QoS in WiMAX/WiFi integration
- Rabbani, Md, Kamruzzaman, Joarder, Gondal, Iqbal, Ahmad, Iftekhar, Hassan, Md Rafiul
- Authors: Rabbani, Md , Kamruzzaman, Joarder , Gondal, Iqbal , Ahmad, Iftekhar , Hassan, Md Rafiul
- Date: 2011
- Type: Text , Journal article
- Relation: Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2011 (Studies in Computational Intelligence series) Vol. 368, no. 2011 (2011), p. 141-156
- Full Text: false
- Reviewed:
- Description: Wireless access technology has come a long way in its relatively short but remarkable lifetime, which has so far been led by WiFi technology. WiFi enjoys a high penetration in the market.Most of the electronic gadgets such as laptop, notepad, mobile set, etc., boast the provision ofWiFi. Currently most WiFi hotspots are connected to the Internet via wired connections (e.g., Ethernet), and the deployment cost of wired connection is high. On the other hand, since WiMAX can provide a high coverage area and transmission bandwidth, it is very suitable for the backbone networks of WiFi. WiMAX can also provide the better QoS needed for many 4G applications. WiMAX devices, however, are not as common as WiFi devices and it is also expensive to deploy aWiMAX-only infrastructure. An integrated WiMAX/WiFi architecture (using WiMAX as backhaul connection for WiFi) can support 4G applications with QoS assurance and mobility, and provide high-speed broadband services in rural, regional and urban areas while reducing the backhaul cost. WiMAX and WiFi have different MAC mechanisms to handle QoS. WiMAX MAC architecture is connection-oriented providing the platform for strong QoS control. In contrast,WiFi MAC is not connection-oriented, hence can provide only best effort services. Delivering improved QoS in an integrated WiMAX/WiFi architecture poses a serious technological challenge. The paper depicts a converged architecture of WiMAX and WiFi, and then proposes an adaptive resource distribution model for the access points. The resource distribution model ultimately allocates more time slots to those connections that need more instantaneous resources to meet QoS requirements. A dynamic splitting technique is also presented that divides the total transmission period into downlink and uplink transmission by taking the minimum data rate requirements of the connections into account. This ultimately improves the utilization of the available resources, and the QoS of the connections. Simulation results show that the proposed schemes significantly outperform the other existing resource sharing schemes, in terms of maintaining QoS of different traffic classes in an integratedWiMAX/WiFi architecture
False data detection in a clustered smart grid using unscented Kalman filter
- Rashed, Muhammad, Kamruzzaman, Joarder, Gondal, Iqbal, Islam, Syed
- Authors: Rashed, Muhammad , Kamruzzaman, Joarder , Gondal, Iqbal , Islam, Syed
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 78548-78556
- Full Text:
- Reviewed:
- Description: The smart grid accessibility over the Internet of Things (IoT) is becoming attractive to electrical grid operators as it brings considerable operational and cost efficiencies. However, this in return creates significant cyber security challenges, such as fortification of state estimation data such as state variables against false data injection attacks (FDIAs). In this paper, a clustered partitioning state estimation (CPSE) technique is proposed to detect FDIA by using static state estimation, namely, weighted least square (WLS) method in conjunction with dynamic state estimation using minimum variance unscented Kalman filter (MV-UKF) which improves the accuracy of state estimation. The estimates acquired from the MV-UKF do not deviate like WLS as these are purely based on the previous iteration saved in the transition matrix. The deviation between the corresponding estimations of WLS and MV-UKF are utilised to partition the smart grid into smaller sub-systems to detect FDIA and then identify its location. To validate the proposed detection technique, FIDAs are injected into IEEE 14-bus, IEEE 30-bus, IEEE 118-bus, and IEEE 300-bus distribution feeder using MATPOWER simulation platform. Our results clearly demonstrate that the proposed technique can locate the attack area efficiently compared to other techniques such as chi square. © 2013 IEEE.
- Authors: Rashed, Muhammad , Kamruzzaman, Joarder , Gondal, Iqbal , Islam, Syed
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 78548-78556
- Full Text:
- Reviewed:
- Description: The smart grid accessibility over the Internet of Things (IoT) is becoming attractive to electrical grid operators as it brings considerable operational and cost efficiencies. However, this in return creates significant cyber security challenges, such as fortification of state estimation data such as state variables against false data injection attacks (FDIAs). In this paper, a clustered partitioning state estimation (CPSE) technique is proposed to detect FDIA by using static state estimation, namely, weighted least square (WLS) method in conjunction with dynamic state estimation using minimum variance unscented Kalman filter (MV-UKF) which improves the accuracy of state estimation. The estimates acquired from the MV-UKF do not deviate like WLS as these are purely based on the previous iteration saved in the transition matrix. The deviation between the corresponding estimations of WLS and MV-UKF are utilised to partition the smart grid into smaller sub-systems to detect FDIA and then identify its location. To validate the proposed detection technique, FIDAs are injected into IEEE 14-bus, IEEE 30-bus, IEEE 118-bus, and IEEE 300-bus distribution feeder using MATPOWER simulation platform. Our results clearly demonstrate that the proposed technique can locate the attack area efficiently compared to other techniques such as chi square. © 2013 IEEE.
Function similarity using family context
- Black, Paul, Gondal, Iqbal, Vamplew, Peter, Lakhotia, Arun
- Authors: Black, Paul , Gondal, Iqbal , Vamplew, Peter , Lakhotia, Arun
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics Vol. 9, no. 7 (Jul 2020), p. 20
- Full Text:
- Reviewed:
- Description: Finding changed and similar functions between a pair of binaries is an important problem in malware attribution and for the identification of new malware capabilities. This paper presents a new technique called Function Similarity using Family Context (FSFC) for this problem. FSFC trains a Support Vector Machine (SVM) model using pairs of similar functions from two program variants. This method improves upon previous research called Cross Version Contextual Function Similarity (CVCFS) e epresenting a function using features extracted not just from the function itself, but also, from other functions with which it has a caller and callee relationship. We present the results of an initial experiment that shows that the use of additional features from the context of a function significantly decreases the false positive rate, obviating the need for a separate pass for cleaning false positives. The more surprising and unexpected finding is that the SVM model produced by FSFC can abstract function similarity features from one pair of program variants to find similar functions in an unrelated pair of program variants. If validated by a larger study, this new property leads to the possibility of creating generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra.
- Description: This research was performed in the Internet Commerce Security Lab (ICSL), which is a joint venture with research partners Westpac, IBM, and Federation University Australia.
- Authors: Black, Paul , Gondal, Iqbal , Vamplew, Peter , Lakhotia, Arun
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics Vol. 9, no. 7 (Jul 2020), p. 20
- Full Text:
- Reviewed:
- Description: Finding changed and similar functions between a pair of binaries is an important problem in malware attribution and for the identification of new malware capabilities. This paper presents a new technique called Function Similarity using Family Context (FSFC) for this problem. FSFC trains a Support Vector Machine (SVM) model using pairs of similar functions from two program variants. This method improves upon previous research called Cross Version Contextual Function Similarity (CVCFS) e epresenting a function using features extracted not just from the function itself, but also, from other functions with which it has a caller and callee relationship. We present the results of an initial experiment that shows that the use of additional features from the context of a function significantly decreases the false positive rate, obviating the need for a separate pass for cleaning false positives. The more surprising and unexpected finding is that the SVM model produced by FSFC can abstract function similarity features from one pair of program variants to find similar functions in an unrelated pair of program variants. If validated by a larger study, this new property leads to the possibility of creating generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra.
- Description: This research was performed in the Internet Commerce Security Lab (ICSL), which is a joint venture with research partners Westpac, IBM, and Federation University Australia.
How to improve postgenomic knowledge discovery using imputation
- Sehgal, Muhammad Shoaib B, Gondal, Iqbal, Dooley, Laurence, Coppel, Ross
- Authors: Sehgal, Muhammad Shoaib B , Gondal, Iqbal , Dooley, Laurence , Coppel, Ross
- Date: 2009
- Type: Text , Journal article
- Relation: Eurasip Journal on Bioinformatics and Systems Biology Vol. 2009, no. 1 (2009), p. 1-14
- Full Text:
- Reviewed:
- Description: While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputation, which exploit the biological transactional behaviour of functionally correlated genes to afford accurate missing value estimation. This paper examines the influence of missing value imputation techniques upon postgenomic knowledge inference methods with results for various algorithms consistently corroborating that instead of ignoring missing values, recycling microarray data by flexible and robust imputation can provide substantial performance benefits for subsequent downstream procedures
- Authors: Sehgal, Muhammad Shoaib B , Gondal, Iqbal , Dooley, Laurence , Coppel, Ross
- Date: 2009
- Type: Text , Journal article
- Relation: Eurasip Journal on Bioinformatics and Systems Biology Vol. 2009, no. 1 (2009), p. 1-14
- Full Text:
- Reviewed:
- Description: While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputation, which exploit the biological transactional behaviour of functionally correlated genes to afford accurate missing value estimation. This paper examines the influence of missing value imputation techniques upon postgenomic knowledge inference methods with results for various algorithms consistently corroborating that instead of ignoring missing values, recycling microarray data by flexible and robust imputation can provide substantial performance benefits for subsequent downstream procedures