Load Tap Changing (LTC) Transformers are widely used in a Power Distribution System to regulate t... more Load Tap Changing (LTC) Transformers are widely used in a Power Distribution System to regulate the voltage level within standard operational limit. In a SCADA connected network, the performance of LTC transformers can be improved by utilizing a closed loop monitoring and control mechanism. The widely used SCADA communication protocols, including Modbus and DNP3, have been proven vulnerable under cyber attack. In this paper, we conduct a vulnerability analysis of LTC transformers under malicious modification of measurement data. Here, we define two different attack strategies, (i)attack targeting energy system efficiency, and (ii) attack targeting energy system stability. With theoretical background and simulation results, we demonstrate that the attack strategies can significantly affect the power distribution system operations in terms of energy efficiency and stability. The experiments are performed considering IEEE benchmark 123 node test distribution system.
In today’s Smart Grid, the power Distribution System Operator (DSO) uses real-time measurement
d... more In today’s Smart Grid, the power Distribution System Operator (DSO) uses real-time measurement
data from the Advanced Metering Infrastructure (AMI) for efficient, accurate and advanced
monitoring and control. Smart Grids are vulnerable to sophisticated data integrity attacks
like the False Data Injection (FDI) attack on the AMI sensors that produce misleading operational
decision of the power system [1]. Presently, there is a lack of research in the area of
power system analysis that relates the FDI attacks with system stability that is important for both
analysis of the effect of cyber-attack as well as for taking preventive measures of protection.
In this paper, we study the physical characteristics of the power system, and draw a relationship
between the system stability indices and the FDI attacks. We identify the level of
vulnerabilities of each AMI node in terms of different degrees of FDI attacks. In order to obtain
the interdependent relationship of different nodes, we implement an improved Constriction
Factor Particle Swarm Optimization (CF-PSO) based hybrid clustering technique to group the
nodes into the most, the moderate and the least vulnerable clusters. With extensive experiments
and analysis using two benchmark test systems, we show that the nodes in the most vulnerable
cluster exhibit higher likelihood of de-stabilizing system operation compared to other nodes.
Complementing research is the construction of FDI attacks and their countermeasures, this paper
focuses on the understanding of characteristics and practical effect of FDI attacks on the
operation of the Smart Grid by analyzing the interdependent nature of its physical properties.
Accurate forecasting is important for cost-effective and efficient monitoring and control of the ... more Accurate forecasting is important for cost-effective and efficient monitoring and control of the renewable energy based power generation. Wind based power is one of the most difficult energy to predict accurately, due to the widely varying and unpredictable nature of wind energy. Although Autoregressive (AR) techniques have been widely used to create wind power models, they have shown limited accuracy in forecasting, as well as difficulty in determining the correct parameters for an optimized AR model. In this paper, Constriction Factor Particle Swarm Optimization (CF-PSO) is employed to optimally determine the parameters of an Autoregressive (AR) model for accurate prediction of the wind power output behaviour. Appropriate lag order of the proposed model is selected based on Akaike information criterion. The performance of the proposed PSO based AR model is compared with four well-established approaches; Forward-backward approach, Geometric lattice approach, Least-squares approach and Yule-Walker approach, that are widely used for error minimization of the AR model. To validate the proposed approach, real-life wind power data of Capital Wind Farm was obtained from Australian Energy Market Operator. Experimental evaluation based on a number of different datasets demonstrate that the performance of the AR model is significantly improved compared with benchmark methods.
Recently there has been increasing interest in improving smart grids efficiency using computation... more Recently there has been increasing interest in improving smart grids efficiency using computational intelligence.
Microaggregation Sorting Framework for K-Anonymity Statistical Disclosure Control in Cloud Computing
IEEE Transactions on Cloud Computing, 2015
Anomaly detection in electric network database of smart grid: Graph matching approach
Electric Power Systems Research, 2016
Recent studies have shown that the operational modules of an Energy Management System (EMS) are v... more Recent studies have shown that the operational modules of an Energy Management System (EMS) are vulnerable to the anomalies that exist in an electric topological and configuration database (DB). In this paper, we focus on the security of EMS modules by detecting anomalies in an electric network DB. Firstly, we explain how an EMS's Optimal Power Flow (OPF) module can be exploited by accidental or deliberate changes in a power system model. As a defense mechanism, for the first time, we propose a graph comparison-based approach for identifying anomalies in an electric network DB. In this study, we formulate the problem as a Quadratic Assignment Problem (QAP) and use the Graduated Assignment algorithm to perform graph matching. To evaluate the effectiveness of the proposed method, we consider different test scenarios considering the IEEE benchmark 24-bus, 30-bus and 118-bus test systems. The results obtained from this analysis show that the proposed method successfully captures DB anomalies at very high detection rates with a smaller time complexity than those obtained from studies published in relevant literature.
A Data-Driven Approach to Distinguish Cyber-Attacks from Physical Faults in a Smart Grid
Recently, there has been significant increase in interest on Smart Grid security. Researchers hav... more Recently, there has been significant increase in interest on Smart Grid security. Researchers have proposed various techniques to detect cyber-attacks using sensor data. However, there has been little work to distinguish a cyber-attack from a power system physical fault. A serious operational failure in physical power grid may occur from the mitigation strategies if fault is wrongly classified as a cyber-attack or vice-versa. In this paper, we utilize a data-driven approach to accurately differentiate the physical faults from cyber-attacks. First, we create a realistic dataset by generating different types of faults and cyber-attacks on the IEEE 30 bus benchmark test system. With extensive experiments, we observe that most of the established supervised methods perform poorly for the classification of faults and cyber-attacks specially for the practical datasets. Hence, we provide a data-driven approach where labelled data are projected in a new low-dimensional subspace using Princip...
ICST Transactions on Scalable Information Systems)</, 2015
There is significant interest in the data mining and network management communities to efficientl... more There is significant interest in the data mining and network management communities to efficiently analyse huge amounts of network traffic, given the amount of network traffic generated even in small networks. Summarization is a primary data mining task for generating a concise yet informative summary of the given data and it is a research challenge to create summary from network traffic data. Existing clustering based summarization techniques lack the ability to create a suitable summary for further data mining tasks such as anomaly detection and require the summary size as an external input. Additionally, for complex and high dimensional network traffic datasets, there is often no single clustering solution that explains the structure of the given data. In this paper, we investigate the use of multiview clustering to create a meaningful summary using original data instances from network traffic data in an efficient manner. We develop a mathematically sound approach to select the summary size using a sampling technique. We compare our proposed approach with regular clustering based summarization incorporating the summary size calculation method and random approach. We validate our proposed approach using the benchmark network traffic dataset and state-of-theart summary evaluation metrics.
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 2015
Anomaly detection is an important aspect of data mining, where the main objective is to identify ... more Anomaly detection is an important aspect of data mining, where the main objective is to identify anomalous or unusual data from a given dataset. However, there is no formal categorization of application-specific anomaly detection techniques for big data and this ignites a confusion for the data miners. In this paper, we categorise anomaly detection techniques based on nearest neighbours, clustering and statistical approaches and investigate the performance analysis of these techniques in critical infrastructure applications such as SCADA systems. Extensive experimental analysis is conducted to compare representative algorithms from each of the categories using seven benchmark datasets (both real and simulated) in SCADA systems. The effectiveness of the representative algorithms is measured through a number of metrics. We highlighted the set of algorithms that are the best performing for SCADA systems.
Smart grid security is crucial to maintain stable and reliable power system operation during the ... more Smart grid security is crucial to maintain stable and reliable power system operation during the contingency situation due to the failure of any critical power system component. Ensuring a 'secured smart grid' involves with a less possibility of power grid collapse or equipment malfunction. Due to lack of the proper 'security measures', a major blackout may occur which can even lead to a cascading failure. Therefore, to protect this critical power system infrastructure and to ensure a reliable and an uninterrupted power supply to the end users, smart grid security issues must be addressed with high priority.
Particle Swarm optimization (PSO) is a powerful optimization tool which is widely used to solve a... more Particle Swarm optimization (PSO) is a powerful optimization tool which is widely used to solve a wide range of real-life optimization problems. Some of the widely used PSO variants, including CF-PSO and AW-PSO, cannot guarantee to achieve globally optimal solutions during the period of stagnation when particle velocity variations decline considerably, leading to early convergence. In order to address this problem, this paper proposes an improved PSO algorithm, 'Collaborative Hybrid PSO (CHPSO)'. In the proposed algorithm, the initial swarm is divided into two sub-swarms, one following the Constriction Factor approach and other following Adaptive Weight approach. When the velocity of any of these two sub-swarms goes below the threshold, an information exchange mechanism is utilized and mutation is performed to improve the quality of the solutions. The proposed method is implemented on Matlab and evaluated using five well studied benchmark test functions. Results obtained in this analysis show that the proposed method finds better solutions compared with Constriction Factor PSO (CF-PSO) and Adaptive Weight PSO (AW-PSO), when they work individually. Statistical significance test also shows the robustness of the proposed method compared with CF-PSO and AW-PSO.
In this paper we present a random shuffling scheme to apply with adaptive sorting algorithms. Ada... more In this paper we present a random shuffling scheme to apply with adaptive sorting algorithms. Adaptive sorting algorithms utilize the presortedness present in a given sequence. We have probabilistically increased the amount of presortedness present in a sequence by using a random shuffling technique that requires little computation. Theoretical analysis suggests that the proposed scheme can improve the performance of
2014 IEEE PES General Meeting | Conference & Exposition, 2014
Recently there has been increasing interest in improving smart grids efficiency using computation... more Recently there has been increasing interest in improving smart grids efficiency using computational intelligence.
Extracting useful information from Solid State Drives (SSD) is a challenging but important forens... more Extracting useful information from Solid State Drives (SSD) is a challenging but important forensic task. However, there are opposing views [15][22] that 1) SSDs destroy the forensics evidences automatically and 2) even after sanitization of SSDs, data can be recovered. This paper investigates this issue and reports experimental findings that identify the reason why certain SSDs seem to destroy forensic evidences while other SSDs do not. The experiments provide insight and analyses of the behaviour of SSDs when certain software components, such as Background Garbage Collector (BGC) and Operating System functions, such as TRIM, are executed on the SSD.
This paper proposes a feature selection technique for software clustering which can be used in th... more This paper proposes a feature selection technique for software clustering which can be used in the architecture recovery of software systems. The recovered architecture can then be used in the subsequent phases of software maintenance, reuse and re-engineering. A number of diverse features could be extracted from the source code of software systems, however, some of the extracted features may have less information to use for calculating the entities, which result in dropping the quality of software clusters. Therefore, further research is required to select those features which have high relevancy in finding associations between entities. In this article first we propose a supervised feature selection technique for unlabeled data, and then we apply this technique for software clustering. A number of feature subset selection techniques in software architecture recovery have been proposed. However none of them focus on automated feature selection in this domain. Experimental results on three software test systems reveal that our proposed approach produces results which are closer to the decompositions prepared by human experts, as compared to those discovered by the well-known K-Means algorithm.
Instance-based learning algorithms are often required to choose which instances to store for use ... more Instance-based learning algorithms are often required to choose which instances to store for use during classification. Keeping too many instances usually results in more storage and processing time requirements during classification. Many attempts have been made to reduce the size of the training set. The major drawback of majority of these attempts is their expensive learning process that limits their application in practical domains. In this paper, we propose a new training set reduction algorithm called Subset Selection Classifier (SSC), which chooses a minimal subset by performing an incremental search in the training set. SSC extends the nearest neighbor concept by constructing several circular regions in the training sample and building a model by collecting the central instance of each circular region along its radius. A test instance is classified by the selected instances if it falls within the radius of any selected instance. Experimental evaluation against 12 existing techniques on 11 benchmark datasets show that SSC has the best accuracy as well as the best reduction of the size of the training set in the average case.
We have developed an efficient summarization paradigm for data drawn from hierarchical domain to ... more We have developed an efficient summarization paradigm for data drawn from hierarchical domain to construct a succinct view of important large-valued regions ("heavy hitters"). It requires one pass over the data with moderate number of updates per element of the data and requires lesser amount of memory space as compared to existing approaches for approximating hierarchically discounted frequency counts of heavy hitters with provable guarantees. The proposed technique is generic that can make use of existing state-of-the-art sketch-based or count-based frequency estimation approaches. Any algorithm from both of these families can be coupled as a subroutine in the proposed framework without any substantial modifications. Experimental as well as theoretical justifications have been provided for its significance.
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Papers by Abdun Mahmood
data from the Advanced Metering Infrastructure (AMI) for efficient, accurate and advanced
monitoring and control. Smart Grids are vulnerable to sophisticated data integrity attacks
like the False Data Injection (FDI) attack on the AMI sensors that produce misleading operational
decision of the power system [1]. Presently, there is a lack of research in the area of
power system analysis that relates the FDI attacks with system stability that is important for both
analysis of the effect of cyber-attack as well as for taking preventive measures of protection.
In this paper, we study the physical characteristics of the power system, and draw a relationship
between the system stability indices and the FDI attacks. We identify the level of
vulnerabilities of each AMI node in terms of different degrees of FDI attacks. In order to obtain
the interdependent relationship of different nodes, we implement an improved Constriction
Factor Particle Swarm Optimization (CF-PSO) based hybrid clustering technique to group the
nodes into the most, the moderate and the least vulnerable clusters. With extensive experiments
and analysis using two benchmark test systems, we show that the nodes in the most vulnerable
cluster exhibit higher likelihood of de-stabilizing system operation compared to other nodes.
Complementing research is the construction of FDI attacks and their countermeasures, this paper
focuses on the understanding of characteristics and practical effect of FDI attacks on the
operation of the Smart Grid by analyzing the interdependent nature of its physical properties.