ORCID Profile
0000-0001-7769-3384
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Artificial Intelligence and Image Processing not elsewhere classified | Computer System Security | Networking and Communications | Distributed Computing | Artificial Intelligence and Image Processing
ANZSRC Socio-Economic Objective (SEO)
Application tools and system utilities | Communication services not elsewhere classified | Application packages | Computer Software and Services not elsewhere classified | Communication Networks and Services not elsewhere classified | Application Tools and System Utilities |
Related Links
Publications
New Multi-dimensional Sorting Based K-Anonymity Microaggregation for Statistical Disclosure Control
Publisher: Springer Berlin Heidelberg
Date: 2013
Swarm intelligence based multi-phase OPF for peak power loss reduction in a smart grid
Publisher: IEEE
Date: 07-2014
Seamless integration of dependability and security concepts in SOA: A feedback control system based framework and taxonomy
Publisher: Elsevier BV
Date: 07-2011
CF-PSO based loss sensitivity clustering technique to identify optimal DG allocation nodes for energy efficient smart grid operation
Publisher: IEEE
Date: 06-2014
Clustering based semantic data summarization technique: A new approach
Publisher: IEEE
Date: 06-2014
A hybrid approach to improving clustering accuracy using SVM
Publisher: IEEE
Date: 06-2013
Enhanced estimation of Autoregressive wind power prediction model using Constriction Factor Particle Swarm Optimization
Publisher: IEEE
Date: 06-2014
K−Means Clustering Microaggregation for Statistical Disclosure Control
Publisher: Springer India
Date: 2013
Network traffic analysis based on collective anomaly detection
Publisher: IEEE
Date: 06-2014
Software clustering using automated feature subset selection
Publisher: Springer Berlin Heidelberg
Date: 2013
Hierarchical summarization techniques for network traffic
Publisher: IEEE
Date: 06-2011
HiCoOB: Hierarchical Concurrent Optimistic Blockchain Consensus Protocol for Peer-to-Peer Energy Trading Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
A Technique for Efficient Query Estimation over Distributed Data Streams
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Spam filtering for network traffic security on a multi‐core environment
Publisher: Wiley
Date: 24-04-2009
DOI: 10.1002/CPE.1435
A clustering based system for instant detection of cardiac abnormalities from compressed ECG
Publisher: Elsevier BV
Date: 05-2011
False Data Injection Attack Targeting the LTC Transformers to Disrupt Smart Grid Operation
Publisher: Springer International Publishing
Date: 2015
A survey of network anomaly detection techniques
Publisher: Elsevier BV
Date: 2016
Compressed ECG Biometric: A Fast, Secured and Efficient Method for Identification of CVD Patient
Publisher: Springer Science and Business Media LLC
Date: 06-01-2011
DOI: 10.1007/S10916-009-9412-4
Abstract: Adoption of compression technology is often required for wireless cardiovascular monitoring, due to the enormous size of Electrocardiography (ECG) signal and limited bandwidth of Internet. However, compressed ECG must be decompressed before performing human identification using present research on ECG based biometric techniques. This additional step of decompression creates a significant processing delay for identification task. This becomes an obvious burden on a system, if this needs to be done for a trillion of compressed ECG per hour by the hospital. Even though the hospital might be able to come up with an expensive infrastructure to tame the exuberant processing, for small intermediate nodes in a multihop network identification preceded by decompression is confronting. In this paper, we report a technique by which a person can be identified directly from his / her compressed ECG. This technique completely obviates the step of decompression and therefore upholds biometric identification less intimidating for the smaller nodes in a multihop network. The biometric template created by this new technique is lower in size compared to the existing ECG based biometrics as well as other forms of biometrics like face, finger, retina etc. (up to 8302 times lower than face template and 9 times lower than existing ECG based biometric template). Lower size of the template substantially reduces the one-to-many matching time for biometric recognition, resulting in a faster biometric authentication mechanism.
Anomaly detection in electric network database of smart grid: Graph matching approach
Publisher: Elsevier BV
Date: 04-2016
A Probabilistic Model to Predict the Survivability of SCADA Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2013
Critical infrastructure protection: Resource efficient sampling to improve detection of less frequent patterns in network traffic
Publisher: Elsevier BV
Date: 07-2010
A survey of anomaly detection techniques in financial domain
Publisher: Elsevier BV
Date: 02-2016
A Secure Federated Learning Framework for Residential Short Term Load Forecasting
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Stealthy and blind false injection attacks on SCADA EMS in the presence of gross errors
Publisher: IEEE
Date: 07-2016
Two-Tier Cache-Aided Full-Duplex Hybrid Satellite–Terrestrial Communication Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Can We Predict Dependencies Using Domain information?
Publisher: IEEE
Date: 10-2011
DOI: 10.1109/WCRE.2011.17
Identification of vulnerable node clusters against false data injection attack in an AMI based Smart Grid
Publisher: Elsevier BV
Date: 10-2015
Physical Layer Security in IRS-Assisted Cache-Enabled Satellite Communication Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
An Efficient Approach for Complex Data Summarization Using Multiview Clustering
Publisher: Springer International Publishing
Date: 2015
A Novel Approach for Network Traffic Summarization
Publisher: Springer International Publishing
Date: 2015
Heart Disease Diagnosis Using Co-clustering
Publisher: Springer International Publishing
Date: 2015
CHPSO-A new collaborative hybrid particle swarm optimization algorithm
Publisher: IEEE
Date: 06-2014
PPFSCADA: Privacy preserving framework for SCADA data publishing
Publisher: Elsevier BV
Date: 07-2014
Echidna: Efficient Clustering of Hierarchical Data for Network Traffic Analysis
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11753810_92
A data-driven approach to distinguish cyber-attacks from physical faults in a smart grid
Publisher: ACM
Date: 17-10-2015
Computing Hierarchical Summary from Two-Dimensional Big Data Streams
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
An Analytical Framework for Evaluating Survivability of SCADA Systems
Publisher: IEEE
Date: 06-2010
DOI: 10.1109/CIT.2010.164
Building Resilience against COVID-19 Pandemic Using Artificial Intelligence, Machine Learning, and IoT: A Survey of Recent Progress
Publisher: MDPI AG
Date: 06-12-2020
DOI: 10.3390/IOT1020028
Abstract: Coronavirus disease 2019 (COVID-19) has significantly impacted the entire world today and stalled off regular human activities in such an unprecedented way that it will have an unforgettable footprint on the history of mankind. Different countries have adopted numerous measures to build resilience against this life-threatening disease. However, the highly contagious nature of this pandemic has challenged the traditional healthcare and treatment practices. Thus, artificial intelligence (AI) and machine learning (ML) open up new mechanisms for effective healthcare during this pandemic. AI and ML can be useful for medicine development, designing efficient diagnosis strategies and producing predictions of the disease spread. These applications are highly dependent on real-time monitoring of the patients and effective coordination of the information, where the Internet of Things (IoT) plays a key role. IoT can also help with applications such as automated drug delivery, responding to patient queries, and tracking the causes of disease spread. This paper represents a comprehensive analysis of the potential AI, ML, and IoT technologies for defending against the COVID-19 pandemic. The existing and potential applications of AI, ML, and IoT, along with a detailed analysis of the enabling tools and techniques are outlined. A critical discussion on the risks and limitations of the aforementioned technologies are also included.
USMD: UnSupervised Misbehaviour Detection for Multi-Sensor Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Data-Driven Stealthy Injection Attacks on Smart Grid with Incomplete Measurements
Publisher: Springer International Publishing
Date: 2016
Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm
Publisher: MDPI AG
Date: 28-11-2019
DOI: 10.3390/S19235236
Abstract: With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an in idual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment.
A summarization paradigm for big data
Publisher: IEEE
Date: 10-2015
Survivable SCADA Systems: An Analytical Framework Using Performance Modelling
Publisher: IEEE
Date: 12-2010
Subset selection classifier (SSC): A training set reduction method
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/CSE.2013.130
Computing hierarchical summary of the data streams
Publisher: Springer International Publishing
Date: 2016
SCADASim—A Framework for Building SCADA Simulations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2011
Diagnosis of cardiovascular abnormalities from compressed ECG: A data mining based approach
Publisher: IEEE
Date: 11-2009
Uncertainty and evolutionary optimization: A novel approach
Publisher: IEEE
Date: 06-2014
False data injection threats in active distribution systems: A comprehensive survey
Publisher: Elsevier BV
Date: 03-2023
A Taxonomy of Cyber Defence Strategies Against False Data Attacks in Smart Grids
Publisher: Association for Computing Machinery (ACM)
Date: 17-07-2023
DOI: 10.1145/3592797
Abstract: The modern electric power grid, known as the Smart Grid , has fast transformed the isolated and centrally controlled power system to a fast and massively connected cyber-physical system that benefits from the revolutions happening in communications (such as 5G/6G) and the fast adoption of Internet of Things devices (such as intelligent electronic devices and smart meters). While the synergy of a vast number of cyber-physical entities has allowed the Smart Grid to be much more effective and sustainable in meeting the growing global energy challenges, it has also brought with it a large number of vulnerabilities resulting in breaches of data integrity, confidentiality, and availability. False data injection (FDI) appears to be among the most critical cyberattacks and has been a focal point of interest for both research and industry. To this end, this article presents a comprehensive review of the recent advances in defence countermeasures of FDI attacks on the Smart Grid. Relevant existing works of literature are evaluated and compared in terms of their theoretical and practical significance to Smart Grid cybersecurity. In conclusion, a range of technical limitations of existing false data attack detection research is identified, and a number of future research directions are recommended.
FedDiSC: A computation-efficient federated learning framework for power systems disturbance and cyber attack discrimination
Publisher: Elsevier BV
Date: 10-2023
Comprehensive survey and taxonomies of false data injection attacks in smart grids: attack models, targets, and impacts
Publisher: Elsevier BV
Date: 07-2022
Telemedicine Pre and Post Covid-19: Lessons for Commercialisation Based on Previous Use Cases
Publisher: University of KwaZulu-Natal
Date: 14-12-2020
Abstract: Telemedicine used to be slow, difficult, expensive and widely neglected by doctors and patients. COVID-19 changed everything telemedicine is entering a period of rapid economic and business growth. This paper discusses the reasons for change in telemedicine over the last 20 years, through real-life medical technology projects, telemetry, ehealth and health IT. Our methods are based on the analysis of telemedicine projects we have implemented and characteristic historical data. The results of our investigation demonstrate a clear increase of significance in telemedicine in the present and near future. We envision the evolution of mobile phones to personal telehealth monitors. Prior to COVID-19, market penetration and economic factors of telemedicine evolved slowly and in an uneven manner on a global scale. Many of the projects remained active only as long as the grant or corporate or national support was provided. The age of novel globally spreading infectious diseases, exemplified by COVID-19, has created an unusual, different setting. Recent pandemics and epidemics have changed global economics significantly and generated a new motivation and a new market with a projected trillion-dollar market value. Post COVID-19, regular and periodic epidemics and pandemics are expected to continue to occur. This will generate an enormous global market for isolated high-tech services, including telemedicine and telemetry.
Computing discounted multidimensional hierarchical aggregates using modified Misra Gries algorithm
Publisher: Elsevier BV
Date: 09-2015
Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements
Publisher: Elsevier BV
Date: 02-2017
TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Novel Iterative Min-Max Clustering to Minimize Information Loss in Statistical Disclosure Control
Publisher: Springer International Publishing
Date: 2015
Microaggregation Sorting Framework for K-Anonymity Statistical Disclosure Control in Cloud Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
An Architecture and Performance Evaluation of Blockchain-Based Peer-to-Peer Energy Trading
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Estimation of smart grid topology using SCADA measurements
Publisher: IEEE
Date: 11-2016
Forensic potentials of solid state drives
Publisher: Springer International Publishing
Date: 2015
Machine Learning to Ensure Data Integrity in Power System Topological Network Database
Publisher: MDPI AG
Date: 24-04-2020
DOI: 10.3390/ELECTRONICS9040693
Abstract: Operational and planning modules of energy systems heavily depend on the information of the underlying topological and electric parameters, which are often kept in database within the operation centre. Therefore, these operational and planning modules are vulnerable to cyber anomalies due to accidental or deliberate changes in the power system database model. To validate, we have demonstrated the impact of cyber-anomalies on the database model used for operation of energy systems. To counter these cyber-anomalies, we have proposed a defence mechanism based on widely accepted classification techniques to identify the abnormal class of anomalies. In this study, we find that our proposed method based on multilayer perceptron (MLP), which is a special class of feedforward artificial neural network (ANN), outperforms other exiting techniques. The proposed method is validated using IEEE 33-bus and 24-bus reliability test system and analysed using ten different datasets to show the effectiveness of the proposed method in securing the Optimal Power Flow (OPF) module against data integrity anomalies. This paper highlights that the proposed machine learning-based anomaly detection technique successfully identifies the energy database manipulation at a high detection rate allowing only few false alarms.
Measurement-driven blind topology estimation for sparse data injection attack in energy system
Publisher: Elsevier BV
Date: 2022
A Spatiotemporal Data Summarization Approach for Real-Time Operation of Smart Grid
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
An Efficient Clustering Scheme to Exploit Hierarchical Data in Network Traffic Analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2008
A novel approach for outlier detection and clustering improvement
Publisher: IEEE
Date: 06-2013
Predicting dependences using domain‐based coupling
Publisher: Wiley
Date: 29-05-2013
DOI: 10.1002/SMR.1598
Network Traffic Pattern Analysis Using Improved Information Theoretic Co-clustering Based Collective Anomaly Detection
Publisher: Springer International Publishing
Date: 2015
Related Organisations
No related organisations have been discovered for Abdun Mahmood.
Related Funding Activities
Linkage Projects - Grant ID: LP100100404
Start Date: 02-2010
End Date: 12-2013
Amount: $250,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP110100602
Start Date: 05-2011
End Date: 12-2015
Amount: $270,000.00
Funder: Australian Research Council
View Funded Activity