Performance evaluation of multi-tier ensemble classifiers for phishing websites
- Authors: Abawajy, Jemal , Beliakov, Gleb , Kelarev, Andrei , Yearwood, John
- Date: 2012
- Type: Text , Conference proceedings
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- Description: This article is devoted to large multi-tier ensemble classifiers generated as ensembles of ensembles and applied to phishing websites. Our new ensemble construction is a special case of the general and productive multi-tier approach well known in information security. Many efficient multi-tier classifiers have been considered in the literature. Our new contribution is in generating new large systems as ensembles of ensembles by linking a top-tier ensemble to another middletier ensemble instead of a base classifier so that the toptier ensemble can generate the whole system. This automatic generation capability includes many large ensemble classifiers in two tiers simultaneously and automatically combines them into one hierarchical unified system so that one ensemble is an integral part of another one. This new construction makes it easy to set up and run such large systems. The present article concentrates on the investigation of performance of these new multi-tier ensembles for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of multi-level ensembles with three tiers. The results presented here demonstrate that new three-tier ensemble classifiers performed better than the base classifiers and standard ensembles included in the system. This example of application to the classification of phishing websites shows that the new method of combining diverse ensemble techniques into a unified hierarchical three-tier ensemble can be applied to increase the performance of classifiers in situations where data can be processed on a large computer.
Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing features
- Authors: Abawajy, Jemal , Kelarev, Andrei , Stranieri, Andrew , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference proceedings
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- Description: This article is devoted to an empirical investigation of performance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using sub-sets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.
Investigating the relationship between neonatal mortality rate and Mother's characteristics
- Authors: Abdollahian, Mali , Ahmad, Shafiq , Huda, Shamsul , Anggraini, D
- Date: 2012
- Type: Text , Conference proceedings
- Relation: WORLDCOMP'12, USA, 16th-19th July published in Proceedings of the 2012 World Congress in Computer Science - Computer Engineering and Applied Computing pg 1-6
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- Description: Neonatal mortality rate (NMR) is an increasingly important public health issues in many developing countries. Neonatal death now accounts for about two-thirds of the eight million infant deaths that occur globally each year. It is welldocumented that low birth weight (LBW) is the most significant factor influencing NMR. This paper deploys regression analysis to explore the relationship between weight of low birth weight babies and various characteristics of mother. The results indicate that there is a significant relationship between weight of low birth weight babies and mother's weight, age, gestation age and hemoglobin level.
Multivariate control charts for surgical procedures
- Authors: Abdollahian, Malie , Ahmad, S. , Huda, Shamsul
- Date: 2011
- Type: Text , Conference proceedings
- Full Text: false
- Description: Patient's progress in the Intensive Care Unit is characterised by more than one quality characteristics. This paper employs univariate and multivariate control charts to monitor patient progress in the Intensive Care Unit. A definitive comparison is made, between the performance of univariate and multivariate control chart methods, in the monitoring of the patient recovery process. © 2011 ACM.
LiDAR segmentation using suitable seed points for 3D building extraction
- Authors: Abdullah, S.M. , Awrangjeb, Mohammad , Lu, Guojun
- Date: 2014
- Type: Text , Conference proceedings
- Full Text: false
- Description: Effective building detection and roof reconstruction has an influential demand over the remote sensing research community. In this paper, we present a new automatic LiDAR point cloud segmentation method using suitable seed points for building detection and roof plane extraction. Firstly, the LiDAR point cloud is separated into "ground" and "non-ground" points based on the analysis of DEM with a height threshold. Each of the non-ground point is marked as coplanar or non-coplanar based on a coplanarity analysis. Commencing from the maximum LiDAR point height towards the minimum, all the LiDAR points on each height level are extracted and separated into several groups based on 2D distance. From each group, lines are extracted and a coplanar point which is the nearest to the midpoint of each line is considered as a seed point. This seed point and its neighbouring points are utilised to generate the plane equation. The plane is grown in a region growing fashion until no new points can be added. A robust rule-based tree removal method is applied subsequently to remove planar segments on trees. Four different rules are applied in this method. Finally, the boundary of each object is extracted from the segmented LiDAR point cloud. The method is evaluated with six different data sets consisting hilly and densely vegetated areas. The experimental results indicate that the proposed method offers a high building detection and roof plane extraction rates while compared to a recently proposed method.
Automatic segmentation of LiDAR point cloud data at different height levels for 3D building extraction C3 - Proceedings - IEEE International Conference on Multimedia and Expo
- Authors: Abdullah, S.M. , Awrangjeb, Mohammad , Lu, Guojun
- Date: 2014
- Type: Text , Conference proceedings
- Full Text: false
- Description: This paper presents a new LiDAR segmentation technique for automatic building detection and roof plane extraction. First, it uses a height threshold, based on the digital elevation model it divides the LiDAR point cloud into 'ground' and 'non-ground' points. Then, starting from the maximum LiDAR height, and decreasing the height at each iteration, it looks for points to form planar roof segments. At each height level, it clusters the points based on the distance and finds straight lines using the points. The nearest coplanar point to the midpoint of each line is used as a seed point and the plane is grown in a region growing fashion. Finally, a rule-based procedure is followed to remove planar segments in trees. The experimental results show that the proposed technique offers a high building detection and roof plane extraction rates while compared to other recently proposed techniques.
A review on chemical diagnosis techniques for transformer paper insulation degradation
- Authors: Abu Bakar, Norazhar , Abu Siada, Ahmed , Islam, Syed
- Date: 2013
- Type: Text , Conference proceedings , Conference paper
- Relation: 2013 Australasian Universities Power Engineering Conference, AUPEC 2013; Hobart, Australia; 29th September-3rd October 2013 p. 1-6
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- Description: Energized parts within power transformer are isolated using paper insulation and are immersed in insulating oil. Hence, transformer oil and paper insulation are essential sources to detect incipient and fast developing power transformer faults. Several chemical diagnoses techniques are developed to examine the condition of paper insulation such as degree of polymerization, carbon oxides, furanic compounds and methanol. The principle and limitation of these diagnoses are discussed and compared in this paper.
Effects of transformer oil properties and contamination on its spectral response
- Authors: Abu Bakar, Norazhar , Abu-Siada, Ahmed , Islam, Syed , El-Naggar, M.
- Date: 2014
- Type: Text , Conference proceedings
- Relation: 2014 International Conference on Condition Monitoring and Diagnosis, CMD 2014; Jeju, Korea; 21st September 2014
- Full Text: false
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- Description: UV Spectrophotometry is a non-intrusive test that can be employed to determine power transformer’s integrity. Light transmitted through transformer oil sample containing various contaminations is decreased by that fraction being absorbed and is detected as a function of wavelength. This paper investigates the impact of various contaminations and oil properties such as water content, acidity, interfacial tension and average voltage breakdown on the spectral response of the transformer oil. In this regard, various transformer oil samples are collected from in-service transformers and the aforementioned parameters are measured. Same oil samples are scanned with UV-Vis spectroscopic and the spectral responses are obtained and analysed.
Image processing-based on-line technique to detect power transformer winding faults
- Authors: Abu-Siada, Ahmed , Islam, Syed
- Date: 2013
- Type: Text , Conference proceedings , Conference paper
- Relation: 39th Annual Conference of the IEEE Industrial Electronics Society, IECON 2013; Vienna, Austria; 10th-14th November 2013 p. 1-6
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- Description: Frequency Response Analysis (FRA) has been growing in popularity in recent times as a tool to detect mechanical deformation within power transformers. To conduct the test, the transformer has to be taken out of service which may cause interruption to the electricity grid. Moreover, because FRA relies on graphical analysis, it calls for an expert person to analyse the results as so far, there is no standard code for FRA interpretation worldwide. In this paper an online technique is introduced to detect the internal faults within a power transformer by constructing the voltage-current (V-I) locus diagram to provide a current state of the transformer health condition. The technique does not call for any special equipment as it uses the existing metering devices attached to any power transformer to monitor the input voltage, output voltage and the input current at the power frequency and hence online monitoring can be realised. Various types of faults have been simulated to assess its impact on the proposed locus. A Matlab code based on digital image processing is developed to calculate any deviation of the V-I locus with respect to the reference one and to identify the type of fault.
Investigation of microgrid instability caused by time delay
- Authors: Aghanoori, Navid , Masoum, Mohammad , Islam, Syed , Nethery, Steven
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 10th International Conference on Electrical and Electronics Engineering, ELECO 2017; Bursa, Turkey; 29th-2nd December 2017 Vol. 2018, p. 105-110
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- Description: This paper investigates the impact of time delay in the control of a grid-connected microgrid with renewable energy resources. The considered microgrid has a critical load that needs to be powered and protected in the event of grid voltage disturbance while the microgrid maintains connection to the grid. Three case studies are performed considering three different time delays to indicate the advantages of fast communication system in the performance of renewable microgrids. Detailed simulation results illustrate that the proposed communication system using IEC 61850 substation automation standard provides better voltage and current quality to the critical local load with larger phase and gain margins while keeping the microgid connected to main grid.
An intelligent model to control preemption rate of instantaneous request calls in networks with book- ahead reservation
- Authors: Ahmad, Iftekhar , Kamruzzaman, Joarder , Habibi, Daryoush , Islam, Farzana
- Date: 2008
- Type: Text , Conference proceedings
- Relation: 2008 Australasian Telecommunication Networks and Applications Conference, ATNAC, Adelaide 2008. Published in Proceedings of Australasian Telecommunication Networks and Applications conference , IEEE (pp.344-34)
- Full Text: false
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- Description: Resource sharing between Book-Ahead (BA) and Instantaneous Request (IR) reservation often results in high preemption rate of on-going IR calls. High IR call preemption rate causes interruption to service continuity which is considered as detrimental in a QoS-enabled network. A number of call admission control models have been proposed in literature to reduce the preemption rate of on-going IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an Artificial Neural Network (ANN) model to dynamically control the preemption rate of on-going calls in a QoS-enabled network. The model maps network traffic parameters and desired level of preemption rate into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired level of preemption rate. Simulation results show that the preemption rate attained by the model closely matches with the target rat
Secure passive keyless entry and start system using machine learning
- Authors: Ahmad, Usman , Song, Hong , Bilal, Awais , Alazab, Mamoun , Jolfaei, Alireza
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 11th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage, SpaCCS 2018; Melbourne, Australia; 11th-13th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11342 LNCS, p. 304-313
- Full Text: false
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- Description: Despite the benefits of the passive keyless entry and start (PKES) system in improving the locking and starting capabilities, it is vulnerable to relay attacks even though the communication is protected using strong cryptographic techniques. In this paper, we propose a data-intensive solution based on machine learning to mitigate relay attacks on PKES Systems. The main contribution of the paper, beyond the novelty of the solution in using machine learning, is in (1) the use of a set of security features that accurately profiles the PKES system, (2) identifying abnormalities in PKES regular behavior, and (3) proposing a countermeasure that guarantees a desired probability of detection with a fixed false alarm rate by trading off the training time and accuracy. We evaluated our method using the last three months log of a PKES system using the Decision Tree, SVM, KNN and ANN and provide the comparative analysis of the relay attack detection results. Our proposed framework leverages the accuracy of supervised learning on known classes with the adaptability of k-fold cross-validation technique for identifying malicious and suspicious activities. Our test results confirm the effectiveness of the proposed solution in distinguishing relayed messages from legitimate transactions.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Dynamic derivative-droop control for supercapacitor synthetic inertial support
- Authors: Akram, Umer , Mithulananthan, N. , Shah, Rakibuzzaman , Islam, Rabiul
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 2020 IEEE Industry Applications Society Annual Meeting, IAS 2020 Vol. 2020-January
- Full Text: false
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- Description: Energy storage is recognized as a potential solution to alleviate the impacts of inertia reduction and intermittency due to the integration of inverter based renewable energy sources (RES) in power systems. Out of various rapid responsive energy storage technologies, supercapacitor energy storage (SCES) is the most promising technology for synthetic inertia support. Because the SCES has high power density, very small response time, and large cycle life. In this paper, a dynamic derivative-droop control strategy is developed for SCES to provide the synthetic inertia in low inertia power system. The proposed strategy overcomes the limitations of separately applied derivative and droop controls. In addition, the use of time varying gains (referred as dynamic) instead of fixed gains improves the performance compared to derivative-droop coordinated control. Different types of events are created at different penetration levels of RES to test the robustness of the proposed control. A comparison, based on RoCoF and frequency nadir, between the derivative, droop, derivative-droop coordinated and the proposed controls is presented to show the effectiveness of the proposed control approach. © 2020 IEEE.
New approach to detection of incipient slip using inductive sensory system
- Authors: Al-Mamun, Abdullah , Ibrahim, Yousef
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false
On-road and wind tunnel aerodynamic study of human powered vehicles
- Authors: Alam, Firoz , Chowdhury, Harun , Guillaume, Erika , Yang, Jie , Zimmer, Gary
- Date: 2013
- Type: Text , Conference proceedings
- Full Text: false
- Description: The aim of the Royal Automotive Club of Victoria (RACV) Energy Breakthrough annual event is to provide an opportunity to school students to design and develop human powered vehicles (HPVs) and race a nonstop 24 hours event that requires energy conservation, endurance and reliability. The event involves primary and secondary school students, teachers, parents and local industry to work together on the design and use of energy efficient vehicles. The key areas with interest of HPVs are the significance of aerodynamic design and ways to improve overall aerodynamics as most HPVs are designed with minimal or no aerodynamic consideration. Therefore, the primary objective of this study is to examine the aerodynamic behaviour of two production HPVs of variable designs using on-road, wind tunnel experimentations and Computational Fluid Dynamics (CFD) modelling. The study shows that the aerodynamic efficiency of vehicle largely depends on external shape especially the extrusion, gaps and bumps. The useful data can be obtained and utilized using wind tunnel and on-road tests for HPVs if a close replica along is used. © 2013 The Authors.
Assessing transformer oil quality using deep convolutional networks
- Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
- Full Text:
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- Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
- Description: E1
A method to improve transparency of electronic election process without identification
- Authors: Alamuti, Roghayeh , Barjini, Hassan , Khandelwal, Manoj , Jafarabad, Mohammad
- Date: 2015
- Type: Text , Conference proceedings
- Full Text:
- Description: Transparency of bank accounts, nowadays, is an undeniable necessity, but no one denies that definite transparency throughout election process is not realized thus far in the world. This calls for fundamental changes in traditional electronic election methods. The new method must close the way for any complaints by the candidate as to the voting process as the public completely trusts in the voting mechanism. Synchronizing voting and votes counting improves the public's trust in the results of election. The proposed secure room-corridor of electronic voting employs election watchers and reports real time results of election along with observance of confidentiality of the votes. © 2015 The Authors.
An optimal transportation routing approach using GIS-based dynamic traffic flows
- Authors: Alazab, Ammar , Venkatraman, Sitalakshmi , Abawajy, Jemal , Alazab, Mamoun
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false
- Description: This paper examines the value of real-time traffic information gathered through Geographic Information Systems for achieving an optimal vehicle routing within a dynamically stochastic transportation network. We present a systematic approach in determining the dynamically varying parameters and implementation attributes that were used for the development of a Web-based transportation routing application integrated with real-time GIS services. We propose and implement an optimal routing algorithm by modifying Dijkstra’s algorithm in order to incorporate stochastically changing traffic flows. We describe the significant features of our Web application in making use of the real-time dynamic traffic flow information from GIS services towards achieving total costs savings and vehicle usage reduction. These features help users and vehicle drivers in improving their service levels and productivity as the Web application enables them to interactively find the optimal path and in identifying destinations effectively.
Maximising competitive advantage on e-Business websites : A data mining approach
- Authors: Alazab, Ammar , Bevinakoppa, Savitri , Khraisat, Ansam
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018; Langkawi, Malaysia; 21st-22nd November 2018 p. 111-116
- Full Text: false
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- Description: Many organizations are interested in analyzing and evaluating the web data for their websites because websites are a very important platform to carry out their business. However, website evaluations face many challenges in using analytics, especially with the huge amount of data that the websites are collecting from various sources. This explosive growth in data requires a complex tool for analyzing and automatically convert the data into valuable information. However, without using a proper analysis tool, it is very difficult to understand the user's behaviour, user's interaction patterns on the website and how users involve in the site. This paper explains methods to examine, understand and visualize the huge amounts of stored data collected from the websites. In this paper, a framework is developed for identifying user's behaviours on websites. Firstly, the attributes are extracted from different websites using Google Analytics and other API tools. Secondly, data mining techniques such as clustering, classification and information gain are applied to build this framework. The findings of these study can be used to evaluate the website and provide some guidelines for the web team to increase user engagement on the website and understand the influence of user behaviour. In addition, this framework is able to identify which behaviour features influence user decisions. Our proposed framework for identifying user's behaviours on websites is tested on a large dataset that contains a variety of individual users from different websites. © 2018 IEEE.
Towards understanding malware behaviour by the extraction of API calls
- Authors: Alazab, Mamoun , Venkatraman, Sitalakshmi , Watters, Paul
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: One of the recent trends adopted by malware authors is to use packers or software tools that instigate code obfuscation in order to evade detection by antivirus scanners. With evasion techniques such as polymorphism and metamorphism malware is able to fool current detection techniques. Thus, security researchers and the anti-virus industry are facing a herculean task in extracting payloads hidden within packed executables. It is a common practice to use manual unpacking or static unpacking using some software tools and analyse the application programming interface (API) calls for malware detection. However, extracting these features from the unpacked executables for reverse obfuscation is labour intensive and requires deep knowledge of low-level programming that includes kernel and assembly language. This paper presents an automated method of extracting API call features and analysing them in order to understand their use for malicious purpose. While some research has been conducted in arriving at file birthmarks using API call features and the like, there is a scarcity of work that relates to features in malcodes. To address this gap, we attempt to automatically analyse and classify the behavior of API function calls based on the malicious intent hidden within any packed program. This paper uses four-step methodology for developing a fully automated system to arrive at six main categories of suspicious behavior of API call features. © 2010 IEEE.