ORCID Profile
0000-0003-3916-1381
Current Organisation
Deakin University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: IEEE
Date: 07-2014
Publisher: Springer Singapore
Date: 2014
Publisher: Springer International Publishing
Date: 2021
Publisher: MDPI AG
Date: 24-01-2022
DOI: 10.3390/ELECTRONICS11030355
Abstract: Energy consumption data is being used for improving the energy efficiency and minimizing the cost. However, obtaining energy consumption data has two major challenges: (i) data collection is very expensive, time-consuming, and (ii) security and privacy concern of the users which can be revealed from the actual data. In this research, we have addressed these challenges by using generative adversarial networks for generating energy consumption profile. We have successfully generated synthetic data which is similar to the real energy consumption data. On the basis of the recent research conducted on TimeGAN, we have implemented a framework for synthetic energy consumption data generation that could be useful in research, data analysis and create business solutions. The framework is implemented using the real-world energy dataset, consisting of energy consumption data of the year 2020 for the Australian states of Victoria, New South Wales, South Australia, Queensland and Tasmania. The results of implementation is evaluated using various performance measures and the results are showcased using visualizations along with Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (TSNE) plots. Overall, experimental results show that Synthetic data generated using the proposed implementation possess very similar characteristics to the real dataset with high comparison accuracy.
Publisher: IEEE
Date: 06-2014
Publisher: IEEE
Date: 06-2014
Publisher: Elsevier BV
Date: 04-2023
Publisher: MDPI AG
Date: 29-07-2020
DOI: 10.3390/ELECTRONICS9081218
Abstract: The smart grid system is one of the key infrastructures required to sustain our future society. It is a complex system that comprises two independent parts: power grids and communication networks. There have been several cyber attacks on smart grid systems in recent years that have caused significant consequences. Therefore, cybersecurity training specific to the smart grid system is essential in order to handle these security issues adequately. Unfortunately, concepts related to automation, ICT, smart grids, and other physical sectors are typically not covered by conventional training and education methods. These cybersecurity experiences can be achieved by conducting training using a smart grid co-simulation, which is the integration of at least two simulation models. However, there has been little effort to research attack simulation tools for smart grids. In this research, we first review the existing research in the field, and then propose a smart grid attack co-simulation framework called GridAttackSim based on the combination of GridLAB-D, ns-3, and FNCS. The proposed architecture allows us to simulate smart grid infrastructure features with various cybersecurity attacks and then visualize their consequences automatically. Furthermore, the simulator not only features a set of built-in attack profiles but also enables scientists and electric utilities interested in improving smart grid security to design new ones. Case studies were conducted to validate the key functionalities of the proposed framework. The simulation results are supported by relevant works in the field, and the system can potentially be deployed for cybersecurity training and research.
Publisher: IEEE
Date: 07-2012
Publisher: Elsevier BV
Date: 10-2023
Publisher: Springer Science and Business Media LLC
Date: 03-05-2022
DOI: 10.1007/S12667-022-00511-W
Abstract: This paper has developed an approach to optimise energy sell and price bids at the sellers along with optimising energy purchase decisions at the buyers in a peer-to-peer (P2P) energy trading market. The optimum price and energy sell bids are designed to maximise the profit at the sellers, while buyers make energy purchase decisions to minimise their energy deficit. The proposed approach relies on a day-ahead optimisation mechanism that can utilise the daily generation and demand patterns as well as a rolling horizon based real-time update strategy when there are variations in generation or demand forecasts. The aforementioned approach is evaluated for a real-life generation and demand dataset under different scenarios. The numerical results demonstrate that when the forecasting error is not very high, the proposed optimisation approach can allow sellers to obtain some profit in most of the time intervals during the day.
Publisher: IEEE
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2017
Publisher: IEEE
Date: 20-11-2022
Publisher: MDPI AG
Date: 24-06-2022
DOI: 10.3390/S22134795
Abstract: The smart grid is one of the core technologies that enable sustainable economic and social developments. In recent years, various cyber attacks have targeted smart grid systems, which have led to severe, harmful consequences. It would be challenging to build a real smart grid system for cybersecurity experimentation and validation purposes. Hence, analytical techniques, with simulations, can be considered as a practical solution to make smart grid cybersecurity experimentation possible. This paper first provides a literature review on the current state-of-the-art in smart grid attack analysis. We then apply graphical security modeling techniques to design and implement a Cyber Attack Analysis Framework for Smart Grids, named GridAttackAnalyzer. A case study with various attack scenarios involving Internet of Things (IoT) devices is conducted to validate the proposed framework and demonstrate its use. The functionality and user evaluations of GridAttackAnalyzer are also carried out, and the evaluation results show that users have a satisfying experience with the usability of GridAttackAnalyzer. Our modular and extensible framework can serve multiple purposes for research, cybersecurity training, and security evaluation in smart grids.
Publisher: IEEE
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 10-2023
Publisher: ACM
Date: 10-07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2024
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: IEEE
Date: 20-11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 04-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 08-2023
Publisher: Elsevier BV
Date: 2023
DOI: 10.2139/SSRN.4376558
Publisher: Elsevier BV
Date: 03-2022
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 07-2016
Publisher: MDPI AG
Date: 23-02-2021
DOI: 10.3390/S21041554
Abstract: IEC 61850 is one of the most prominent communication standards adopted by the smart grid community due to its high scalability, multi-vendor interoperability, and support for several input/output devices. Generic Object-Oriented Substation Events (GOOSE), which is a widely used communication protocol defined in IEC 61850, provides reliable and fast transmission of events for the electrical substation system. This paper investigates the security vulnerabilities of this protocol and analyzes the potential impact on the smart grid by rigorously analyzing the security of the GOOSE protocol using an automated process and identifying vulnerabilities in the context of smart grid communication. The vulnerabilities are tested using a real-time simulation and industry standard hardware-in-the-loop emulation. An in-depth experimental analysis is performed to demonstrate and verify the security weakness of the GOOSE publish-subscribe protocol towards the substation protection within the smart grid setup. It is observed that an adversary who might have familiarity with the substation network architecture can create falsified attack scenarios that can affect the physical operation of the power system. Extensive experiments using the real-time testbed validate the theoretical analysis, and the obtained experimental results prove that the GOOSE-based IEC 61850 compliant substation system is vulnerable to attacks from malicious intruders.
Publisher: Springer International Publishing
Date: 2022
Publisher: Elsevier BV
Date: 10-2015
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 11-05-2020
Publisher: European Alliance for Innovation n.o.
Date: 08-05-2015
DOI: 10.4108/INIS.2.3.E5
Publisher: IEEE
Date: 12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: IEEE
Date: 12-2020
Publisher: IEEE
Date: 12-2020
Publisher: Wiley
Date: 03-12-2021
Publisher: IEEE
Date: 06-2014
Publisher: IEEE
Date: 18-07-2021
Publisher: MDPI AG
Date: 11-02-2021
DOI: 10.3390/S21041273
Abstract: The efficiency of cooperative communication protocols to increase the reliability and range of transmission for Vehicular Ad hoc Network (VANET) is proven, but identity verification and communication security are required to be ensured. Though it is difficult to maintain strong network connections between vehicles because of there high mobility, with the help of cooperative communication, it is possible to increase the communication efficiency, minimise delay, packet loss, and Packet Dropping Rate (PDR). However, cooperating with unknown or unauthorized vehicles could result in information theft, privacy leakage, vulnerable to different security attacks, etc. In this paper, a blockchain based secure and privacy preserving authentication protocol is proposed for the Internet of Vehicles (IoV). Blockchain is utilized to store and manage the authentication information in a distributed and decentralized environment and developed on the Ethereum platform that uses a digital signature algorithm to ensure confidentiality, non-repudiation, integrity, and preserving the privacy of the IoVs. For optimized communication, transmitted services are categorized into emergency and optional services. Similarly, to optimize the performance of the authentication process, IoVs are categorized as emergency and general IoVs. The proposed cooperative protocol is validated by numerical analyses which show that the protocol successfully increases the system throughput and decreases PDR and delay. On the other hand, the authentication protocol requires minimum storage as well as generates low computational overhead that is suitable for the IoVs with limited computer resources.
Publisher: Springer Science and Business Media LLC
Date: 29-07-2022
Publisher: IEEE
Date: 06-2012
Publisher: PeerJ
Date: 15-07-2021
DOI: 10.7717/PEERJ-CS.643
Abstract: Smart meters have ensured effective end-user energy consumption data management and helping the power companies towards network operation efficiency. However, recent studies highlighted that cyber adversaries may launch attacks on smart meters that can cause data availability, integrity, and confidentiality issues both at the consumer side or at a network operator’s end. Therefore, research on smart meter data security has been attributed as one of the top priorities to ensure the safety and reliability of the critical energy system infrastructure. Authentication is one of the basic building blocks of any secure system. Numerous authentication schemes have been proposed for the smart grid, but most of these methods are applicable for two party communication. In this article, we propose a distributed, dynamic multistage authenticated key agreement scheme for smart meter communication. The proposed scheme provides secure authentication between smart meter, NAN gateway, and SCADA energy center in a distributed manner. Through rigorous cryptanalysis we have proved that the proposed scheme resist replay attack, insider attack, impersonation attack and man-in-the-middle attack. Also, it provides perfect forward secrecy, device anonymity and data confidentiality. The proposed scheme security is formally proved in the CK—model and, using BAN logic, it is proved that the scheme creates a secure session between the communication participants. The proposed scheme is simulated using the AVISPA tool and verified the safety against all active attacks. Further, efficiency analysis of the scheme has been made by considering its computation, communication, and functional costs. The computed results are compared with other related schemes. From these analysis results, it is proved that the proposed scheme is robust and secure when compared to other schemes.
Publisher: IEEE
Date: 06-2012
Publisher: MDPI AG
Date: 11-03-2021
DOI: 10.3390/ELECTRONICS10060650
Abstract: Intelligent electronic devices (IEDs) along with advanced information and communication technology (ICT)-based networks are emerging in the legacy power grid to obtain real-time system states and provide the energy management system (EMS) with wide-area monitoring and advanced control capabilities. Cyber attackers can inject malicious data into the EMS to mislead the state estimation process and disrupt operations or initiate blackouts. A machine learning algorithm (MLA)-based approach is presented in this paper to detect false data injection attacks (FDIAs) in an IED-based EMS. In addition, stealthy construction of FDIAs and their impact on the detection rate of MLAs are analyzed. Furthermore, the impacts of natural disturbances such as faults on the system are considered, and the research work is extended to distinguish between cyber attacks and faults by using state-of-the-art MLAs. In this paper, state-of-the-art MLAs such as Random Forest, OneR, Naive Bayes, SVM, and AdaBoost are used as detection classifiers, and performance parameters such as detection rate, false positive rate, precision, recall, and f-measure are analyzed for different case scenarios on the IEEE benchmark 14-bus system. The experimental results are validated using real-time load flow data from the New York Independent System Operator (NYISO).
Publisher: ACM
Date: 17-10-2015
Publisher: MDPI AG
Date: 30-03-2022
DOI: 10.3390/ELECTRONICS11071083
Abstract: The Internet of Things (IoT) has brought new ways for humans and machines to communicate with each other over the internet. Though sensor-driven devices have largely eased our everyday lives, most IoT infrastructures have been suffering from security challenges. Since the emergence of IoT, lightweight block ciphers have been a better option for intelligent and sensor-based applications. When public-key infrastructure dominates worldwide, the symmetric key encipherment such as Advanced Encryption Standard (AES) shows immense prospects to sit with the smart home IoT appliances. As investigated, chaos motivated logistic map shows enormous potential to secure IoT aligned real-time data communication. The unpredictability and randomness features of the logistic map in sync with chaos-based scheduling techniques can pave the way to build a particular dynamic key propagation technique for data confidentiality, availability and integrity. After being motivated by the security prospects of AES and chaos cryptography, the paper illustrates a key scheduling technique using a 3-dimensional S-box (substitution-box). The logistic map algorithm has been incorporated to enhance security. The proposed approach has applicability for lightweight IoT devices such as smart home appliances. The work determines how seeming chaos accelerates the desired key-initiation before message transmission. The proposed model is evaluated based on the key generation delay required for the smart-home sensor devices.
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.
Publisher: IEEE
Date: 18-07-2021
Publisher: IEEE
Date: 25-10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 11-2021
Publisher: MDPI AG
Date: 20-09-2022
DOI: 10.3390/S22197102
Abstract: This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case.
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: MDPI AG
Date: 04-01-2021
DOI: 10.3390/SU13010400
Abstract: Existing research shows that Cluster-based Medium Access Control (CB-MAC) protocols perform well in controlling and managing Vehicular Ad hoc Network (VANET), but requires ensuring improved security and privacy preserving authentication mechanism. To this end, we propose a multi-level blockchain-based privacy-preserving authentication protocol. The paper thoroughly explains the formation of the authentication centers, vehicles registration, and key generation processes. In the proposed architecture, a global authentication center (GAC) is responsible for storing all vehicle information, while Local Authentication Center (LAC) maintains a blockchain to enable quick handover between internal clusters of vehicle. We also propose a modified control packet format of IEEE 802.11 standards to remove the shortcomings of the traditional MAC protocols. Moreover, cluster formation, membership and cluster-head selection, and merging and leaving processes are implemented while considering the safety and non-safety message transmission to increase the performance. All blockchain communication is performed using high speed 5G internet while encrypted information is transmitted while using the RSA-1024 digital signature algorithm for improved security, integrity, and confidentiality. Our proof-of-concept implements the authentication schema while considering multiple virtual machines. With detailed experiments, we show that the proposed method is more efficient in terms of time and storage when compared to the existing methods. Besides, numerical analysis shows that the proposed transmission protocols outperform traditional MAC and benchmark methods in terms of throughput, delay, and packet dropping rate.
Publisher: IEEE
Date: 23-12-2022
Publisher: MDPI AG
Date: 02-07-2022
DOI: 10.3390/EN15134877
Abstract: Supervisory Control and Data Acquisition (SCADA) systems are essential for reliable communication and control of smart grids. However, in the cyber-physical realm, it becomes highly vulnerable to cyber-attacks like False Data Injection (FDI) into the measurement signal which can circumvent the conventional detection methods and interfere with the normal operation of grids, which in turn could potentially lead to huge financial losses and can have a large impact on public safety. It is imperative to have an accurate state estimation of power consumption for further operational decision-making.This work presents novel forecasting-aided anomaly detection using an CNN-LSTM based auto-encoder sequence to sequence architecture to combat against false data injection attacks. We further present an adaptive optimal threshold based on the consumption patterns to identify abnormal behaviour. Evaluation is performed on real-time energy demand consumption data collected from the Australian Energy Market Operator. An extensive experiment shows that the proposed model outperforms other benchmark algorithms in not only improving the data injection attack (95.43%) but also significantly reducing the false positive rate.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 06-2012
Publisher: Elsevier BV
Date: 03-2023
Publisher: Inderscience Publishers
Date: 2022
Publisher: Journal of Modern Power Systems and Clean Energy
Date: 2023
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.
Publisher: Springer Science and Business Media LLC
Date: 06-04-2021
Publisher: Elsevier BV
Date: 10-2023
Publisher: Elsevier BV
Date: 07-2022
Publisher: IEEE
Date: 20-11-2022
Publisher: Elsevier BV
Date: 02-2017
Publisher: IEEE
Date: 08-2010
Publisher: MDPI AG
Date: 16-06-2022
DOI: 10.3390/SU14127362
Abstract: Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature. This modality is related to changes in human behaviour over time. The developments in these methods aim to lify continuous authentication such as biometrics to protect their privacy on user devices. Various Continuous Authentication (CA) systems have been proposed in the literature. They represent a new generation of security mechanisms that continuously monitor user behaviour and use this as the basis to re-authenticate them periodically throughout a login session. However, these methods usually constitute a single classification model which is used to identify or verify a user. This work proposes an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) by introducing a two-step user verification algorithm that verifies the user’s identity using motion-based biometrics and complements the multi-factor authentication, thus making it more secure and flexible. This two-step user verification algorithm is also immune to adversarial attacks, based on our experimental results that show how the rate of misclassification drops while using this model with adversarial data.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-07-2023
DOI: 10.36227/TECHRXIV.23584530
Abstract: Space anomaly detection is of paramount importance in ensuring the safety and reliability of space systems, particularly in the face of increasing threats. This comprehensive survey article focuses on the unique challenges encountered in space security and provides a thorough analysis and synthesis of state-of-the-art anomaly detection systems. The survey identifies key challenges such as scalability, real-time detection, limited labeled data, concept drift, and adversarial attacks, setting the stage for future research in the field. By extensively reviewing existing approaches and methods, the article evaluates their strengths, limitations, and potential applications in space networks. It goes beyond a mere summary by introducing an innovative integration of stream-based and graph-based methods for dynamic space anomaly detection. This integration not only opens up new avenues for research but also enhances detection accuracy by capturing the complex temporal and structural dependencies within space networks. By pioneering research in space security, this survey article offers valuable insights, lessons, and guidance for researchers, engineers, and practitioners in the field of space anomaly detection. It acknowledges the increasing number and sophistication of space threats and addresses the urgent need for innovative approaches to detect anomalies within space networks. With the knowledge and recommendations provided, the space industry can enhance its anomaly detection capabilities, mitigate risks, and safeguard the integrity and security of space systems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-07-2023
DOI: 10.36227/TECHRXIV.23584530.V1
Abstract: Space anomaly detection is of paramount importance in ensuring the safety and reliability of space systems, particularly in the face of increasing threats. This comprehensive survey article focuses on the unique challenges encountered in space security and provides a thorough analysis and synthesis of state-of-the-art anomaly detection systems. The survey identifies key challenges such as scalability, real-time detection, limited labeled data, concept drift, and adversarial attacks, setting the stage for future research in the field. By extensively reviewing existing approaches and methods, the article evaluates their strengths, limitations, and potential applications in space networks. It goes beyond a mere summary by introducing an innovative integration of stream-based and graph-based methods for dynamic space anomaly detection. This integration not only opens up new avenues for research but also enhances detection accuracy by capturing the complex temporal and structural dependencies within space networks. By pioneering research in space security, this survey article offers valuable insights, lessons, and guidance for researchers, engineers, and practitioners in the field of space anomaly detection. It acknowledges the increasing number and sophistication of space threats and addresses the urgent need for innovative approaches to detect anomalies within space networks. With the knowledge and recommendations provided, the space industry can enhance its anomaly detection capabilities, mitigate risks, and safeguard the integrity and security of space systems.
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.
Publisher: Elsevier BV
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: MDPI AG
Date: 28-06-2021
DOI: 10.3390/EN14133887
Abstract: Accurate building energy prediction is useful in various applications starting from building energy automation and management to optimal storage control. However, vulnerabilities should be considered when designing building energy prediction models, as intelligent attackers can deliberately influence the model performance using sophisticated attack models. These may consequently degrade the prediction accuracy, which may affect the efficiency and performance of the building energy management systems. In this paper, we investigate the impact of bi-level poisoning attacks on regression models of energy usage obtained from household appliances. Furthermore, an effective countermeasure against the poisoning attacks on the prediction model is proposed in this paper. Attacks and defenses are evaluated on a benchmark dataset. Experimental results show that an intelligent cyber-attacker can poison the prediction model to manipulate the decision. However, our proposed solution successfully ensures defense against such poisoning attacks effectively compared to other benchmark techniques.
Publisher: IEEE
Date: 06-2012
Publisher: ACM
Date: 10-07-2023
Publisher: Elsevier BV
Date: 02-2021
Publisher: MDPI AG
Date: 11-07-2021
DOI: 10.3390/S21144736
Abstract: The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.
No related grants have been discovered for Adnan Anwar.