4,178 research outputs found

    Incremental learning for large-scale stream data and its application to cybersecurity

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    As many human currently depend on technologies to assist with daily tasks, there are more and more applications which have been developed to be fit in one small gadget such as smart phone and tablet. Thus, by carrying this small gadget alone, most of our tasks are able to be settled efficiently and fast. Until the end of 20th century, mobile phones are only used to call and to send short message service (sms). However, in early 21st century, a rapid revolution of communi�cation technology from mobile phone into smart phone has been seen in which the smart phone is equipped by 4G Internet line along with the telephone service provider line. Thus, the users are able to make a phone call, send messages using variety of application such as Whatsapp and Line, send email, serving websites, accessing maps and handling some daily tasks via online using online banking, online shopping and online meetings via video conferences. In previous years, if there are cases of missing children or missing cars, the victims would rely on the police investigation. But now, as easy as uploading a notification about the loss on Facebook and spread the news among Facebook users, there are more people are able to help in the search. Despite the advantages that can be obtained using these technologies, there are a group of irresponsible people who take advan�tage of current technologies for their own self-interest. Among the applications that are usually being used by almost Internet users and also are often misused by cyber criminals are email and websites. Therefore, we take this initiative to make enhancement in cyber security application to avoid the Internet users from being trapped and deceived by the trick of cyber criminals by developing detec�tion system of malicious spam email and Distributed Denial of Services (DDoS) 3773(53867 3(53867.1781.8781$0,1$+ iii backscatter. Imagine that a notice with a logo of Mobile Phone company is received by an email informing that the customer had recently run up a large mobile phone bill. A link regarding the bill is attached for him/her to find out the details. Since, the customer thinks that the billing might be wrong, thus the link is clicked. However, the link is directed to a webpage which displays a status that currently the webpage is under construction. Then the customer closes the page and thinking of to visit the website again at other time. Unfortunately, after a single click actually a malicious file is downloaded and installed without the customer aware of it. That malicious file most probably is a Trojan that capable to steal confidential information from victim’s computer. On the next day, when the same person is using the same computer to log in the online banking, all of a sudden find out that his/her money is lost totally. This is one of a worst case scenario of malicious spam email which is usually handled by cybersecurity field. Another different case of cybersecurity is the Distributed Denial of Services (DDoS) attack. Let say, Company X is selling flowers via online in which the market is from the local and international customer. The online business of Company X is running normally as usual, until a day before mother’s day, the webpage of Company X is totally down and the prospective customers could not open the webpage to make order to be sent specially for their beloved mother. Thus, the customers would search another company that sells the same item. The Company X server is down, most probably because of the DDoS attack where a junk traffic is sent to that company server which makes that server could not serve the request by the legitimate customers. This attack effect not only the profit of the company, but also reputation damage, regular customer turnover and productivity decline. Unfortunately, it is difficult for a normal user like us to detect malicious spam 377$ 3(53867$.1781.87810,10,1+ email or DDoS attack with naked eyes. It is because recently the spammers and attacker had improved their strategy so that the malicious email and the DDoS packets are hardly able to be differentiated with the normal email and data packets. Once the Social Engineering is used by the spammers to create relevant email content in the malicious spam email and when a new campaign of DDoS attack is launched by the attacker, no normal users are capable to distinguish the benign and malicious email or data packets. This is where my Ph.D project comes in handy. My Ph.d is focusing on constructing a detection system of malicious spam email and DDoS attack using a large number of dataset which are obtained by a server that collect double-bounce email and darknet for malicious spam email detection system and DDoS backscatter detection system, respectively. As many up-to-date data are used during the learning, the detection system would become more robust to the latest strategy of the cybercriminal. Therefore, the scenario mentioned above can be avoided by assisting the user with important information at the user-end such as malicious spam email filter or at the server firewall. First of all, the method to learn large-scale stream data must be solved before implementing it in the detection system. Therefore, in Chapter 2, the general learning strategy of large-scale data is introduced to be used in the cybersecurity applications which are discussed in Chapter 3 and Chapter 4, respectively. One of a critical criterion of the detection system is capable to learn fast because after the learning, the updated information needs to be passed to user to avoid the user from being deceived by the cybercriminal. To process large-scale data sequences, it is important to choose a suitable learning algorithm that is capable to learn in real time. Incremental learning has an ability to process large data in chunk and update the parameters after learning each chunk. Such type of learning keep and update only the minimum information on a classifier model. 3773(53867 3(53867.1781.8781$0,1$+ Therefore, it requires relatively small memory and short learning time. On the other hand, batch learning is not suitable because it needs to store all training data, which consume a large memory capacity. Due to the limited memory, it is certainly impossible to process online large-scale data sequences using the batch learning. Therefore, the learning of large-scale stream data should be conducted incrementally. This dissertation contains of five chapters. In Chapter 1, the concept of in�cremental learning is briefly described and basic theories on Resource Allocating Network (RAN) and conventional data selection method are discussed in this chapter. Besides that, the overview of this dissertation is also elaborated in this chapter. In Chapter 2, we propose a new algorithm based on incremental Radial Basis Function Network (RBFN) to accelerate the learning in stream data. The data sequences are represented as a large chunk size of data given continuously within a short time. In order to learn such data, the learning should be carried out incrementally. Since it is certainly impossible to learn all data in a short pe�riod, selecting essential data from a given chunk can shorten the learning time. In our method, we select data that are located in untrained or “not well-learned” region and discard data at trained or “well-learned” region. These regions are represented by margin flag. Each region is consisted of similar data which are near to each other. To search the similar data, the well-known LSH method pro�posed by Andoni et al. is used. The LSH method indeed has proven be able to quickly find similar objects in a large database. Moreover, we utilize the LSH ʼs properties; hash value and Hash Table to further reduced the processing time. A flag as a criterion to decide whether to choose or not the training data is added in the Hash Table and is updated in each chunk sequence. Whereas, the hash value of RBF bases that is identical with the hash value of the training data is used to select the RBF bases that is near to the training data. The performance results of 377$ 3(53867$.1781.87810,10,1+ vi the numerical simulation on nine UC Irvine (UCI) Machine Learning Repository datasets indicate that the proposed method can reduce the learning time, while keeping the similar accuracy rate to the conventional method. These results indi�cate that the proposed method can improve the RAN learning algorithm towards the large-scale stream data processing. In Chapter 3, we propose a new online system to detect malicious spam emails and to adapt to the changes of malicious URLs in the body of spam emails by updating the system daily. For this purpose, we develop an autonomous system that learns from double-bounce emails collected at a mail server. To adapt to new malicious campaigns, only new types of spam emails are learned by introducing an active learning scheme into a classifier model. Here, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with data selection. In this data selection, the same or similar spam emails that have already been learned are quickly searched for a hash table using Locally Sensitive Hashing, and such spam emails are discarded without learning. On the other hand, malicious spam emails are sometimes drastically changed along with a new arrival of malicious campaign. In this case, it is not appropriate to classify such spam emails into malicious or benign by a classifier. It should be analyzed by using a more reliable method such as a malware analyzer. In order to find new types of spam emails, an outlier detection mechanism is implemented in RAN-LSH. To analyze email contents, we adopt the Bag-of-Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency. To evaluate the developed system, we use a dataset of double-bounce spam emails which are collected from March 1, 2013 to May 10, 2013. In the experiment, we study the effect of introducing the outlier detection in RAN-LSH. As a result, by introducing the outlier detection, we confirm that the detection accuracy is enhanced on 3773(53867 3(53867.1781.87810,10,1+ average over the testing period. In Chapter 4, we propose a fast Distributed Denial of Service (DDoS) backscat�ter detection system to detect DDoS backscatter from a combination of protocols and ports other than the following two labeled packets: Transmission Control Protocol (TCP) Port 80 (80/TCP) and User datagram Protocol (UDP) Port 53 (53/UDP). Usually, it is hard to detect DDoS backscatter from the unlabeled packets, where an expert is needed to analyze every packet manually. Since it is a costly approach, we propose a detection system using Resource Allocating Network (RAN) with data selection to select essential data. Using this method, the learning time is shorten, and thus, the DDoS backscatter can be detected fast. This detection system consists of two modules which are pre-processing and classifier. With the former module, the packets information are transformed into 17 feature-vectors. With the latter module, the RAN-LSH classifier is used, where only data located at untrained region are selected. The performance of the proposed detection system is evaluated using 9,968 training data from 80/TCP and 53/UDP, whereas 5,933 test data are from unlabeled packets which are col�lected from January 1st, 2013 until January 20th, 2014 at National Institute of Information and Communications Technology (NICT), Japan. The results indi�cate that detection system can detect the DDoS backscatter from both labeled and unlabeled packets with high recall and precision rate within a short time. Finally, in Chapter 5, we discussed the conclusions and the future work of our study: RAN-LSH classifier, malicious spam email detection system and DDoS backscatter detection system

    Active control of DC fault currents in DC solid-state transformers during ride-through operation of multi-terminal HVDC systems

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    When a pole-to-pole dc fault occurs in a multi-terminal HVDC system, it is desirable that the stations and dc solid-state transformers on healthy cables continue contributing to power transfer, rather than blocking. To reduce the fault current of a modular multilevel converter based dc solid-state transformer, active fault current control is proposed, where the dc and ac components of fault arm currents are regulated independently. By dynamically regulating the dc offset of the arm voltage rather than being set at half the rated dc voltage, the dc component in the fault current is reduced significantly. Additionally, reduced ac voltage operation of the dc solid-state transformer during the fault is proposed, where the ac voltage of transformer is actively limited in the controllable range of both converters in the transformer to effectively suppress the ac component of the fault current. The fault arm current peak and the energy absorbed by the surge arrester in the dc circuit breakers are reduced by 31.8% and 4.9% respectively, thereby lowering the capacities of switching devices and circuit breakers. Alternatively, with the same fault current level, the dc-link node inductance can be halved by using the proposed control, yielding lowered cost and volume. The novel active fault current control mechanism and the necessary control strategy are presented and simulation results confirm its feasibility

    Improving skills in rounding off the whole number

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    This study was conducted to address teaching and learning skills in rounding off a whole number. This study consisted of 15 years 4 students from the Kong Nan Chinese Primary School, Parit Raja, Johor, Malaysia. Initial survey to identify this problem was carried out by analyzing the exercise books and exercises in pre-test. Based on these analyses, a large number of students were not proficient in relevant skills. A ‘q’ technique was introduced as an approach in teaching and learning to help students master the skills of rounding whole numbers. In summary, this technique helps students to remember the sequence of processes and process in rounding numbers. A total of four sessions of teaching and learning activities that take less than an hour have been implemented specifically to help students to master this technique. Results of the implementation of these activities have shown very positive results among the students. Two post tests were carried out to see the effectiveness of techniques and the results shows that 100% of students were able to answer correctly at least three questions correctly. The t-test analysis was clearly showed the effectiveness of ‘q’ technique. This technique also indirectly helps to maintain and increase student interest in learning Mathematics. This is shown with the active involvement of students in answering questions given by the teacher

    Real time parameter estimation for power quality control and intelligent protection of grid-connected power electronic converters

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    This paper presents a method to identify power system impedance in real-time using signals obtained from grid- connected power electronic converters. The proposed impedance estimation has potential applications in renewable/distributed energy systems, STATCOM, and solid state substations. The method uses wavelets to analyze transients associated with small disturbances imposed by power converters and determine the net impedance back to the source. A data capture period of 5ms is applied to an accurate impedance estimation which provides the possibility of ultra fast fault detection (i.e. within a half cycle). The paper describes how the proposed method would enhance the distributed generation operation during faults

    Voltage quality enhancement in distribution system using artificial neural network (ANN) based dynamic voltage restorer

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    Voltage quality issue and invariably power quality has become an important issue in distribution power system operation due to presence of increased use of nonlinear loads (computers, microcontrollers and power electronics systems). Voltage sags and swells as well as harmonics are problems for industrial, commercial and residential customers with sensitive loads, which need urgent attention for their compensation. In this paper, the modeling and simulations of a dynamic voltage restorer (DVR) was achieved u sing MATLAB/Simulink. The aim was to employ artificial intelligence to provide smart triggering pulses for the DVR to mitigate and to provide compensation against voltage sags and swells. The Artificial Neural Network (ANN) was trained online by data gener ated via a 3 - phase programmable voltage generator and these were used as inputs to the ANN, fault conditions were simulated to create voltage sags and swells in the source supply, while faultless condition of the system was simulated and the data obtained from it was used as targets of the ANN. A net fitting, feed forward back propagation, Lavenberg - Marquardt training algorithm and mean square error performance were used. ANN Simulink block was used as control for the gate of the full wave 3 - phase Insulated Gate Bipolar Transistor (IGBT) inverter employed in constructing the DVR. Three single phase injection transformers were employed to regulate the output amplitude voltage from the DVR, while filters were used to reduce the harmonics from 11.09% to 3.5%. A t the end, voltage sags and swells were effectively mitigated and harmonics in the system reduced to 3.5%, which is within the maximum acceptable IEEE standard 519 of 1992 for harmonic distortion. Key words : Voltage, Distribution System, ANN, Dynamic Volt age Restorer, Voltage quality enhancement, non - linear load

    Decoupling Control of Cascaded Power Electronic Transformer based on Feedback Exact Linearization

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    A Hybrid Modular DC Solid State Transformer Combining High Efficiency and Control Flexibility

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    Rural Facility Electric Power Quality Enhancement

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    Electric power disturbances are known to be more prevalent in small, isolated power systems than in larger interconnected grids which service most of the United States. This fact has given rise to a growing concern about the relative merits of different types of power conditioning equipment and their effectiveness in protecting sensitive electronics and essential loads in rural Alaska. A study has been conducted which compares isolation transformers, voltage regulators, power conditioners, uninterruptible power supplies and indoor computer surge suppressors in their ability to suppress the various disturbances which have been measured in several Alaskan communities. These include voltage sags and surges, impulses, blackouts, frequency variations and long-term voltage abnormalities. In addition, the devices were also subjected to fast, high-magnitude impulses such as might be expected in the event of a lightning strike to or near utility distribution equipment. The solutions for power line problems will vary for different load applications and for different rural electrical environments. The information presented in this report should prove to be valuable in making the analysis.List of Figures - viii List of Tables - xiv Acknowledgements - xv Chapter 1: Electric Disturbances in Power Systems Introduction - 16 Categorizing Electrical Disturbances - 17 Voltage Disturbances and Transients - 19 Frequency Disturbances - 22 Sources of Transients - 22 Lightning and EMP - 23 Switching - 24 Power System Noise - 25 Common Mode and Normal Mode Noise Signals - 26 Chapter 2: Power Quality in Rural Alaska Characterizing the Village Power System - 28 The Village Electric Load - 29 Power Quality Site Surveys - 30 Rural Power Quality in Alaska - 31 Power Conditioning Requirements for Village Loads - 37 Chapter 3: Isolation, Voltage Regulation and Power Conditioning Introduction - 39 Slow Voltage Fluctuations - 39 Voltage Regulation and Power Conditioning - 40 Ferroresonant Transformers - 40 Electronic Tap-Changing Regulators - 44 Isolation Transformers - 47 Dedicated Lines - 51 Chapter 4: Impulse Suppression Introduction - 52 Surge Suppressors - 52 Surge Suppressor Components - 55 Component Configuration - 58 EMI/RFI Filters - 58 Standard Tests for Evaluating Surge Suppressor Performance - 60 Scope of Impulse Testing for Rural Alaska - 60 Impulse Test Equipment - 62 Test Procedure - 62 Impulse Testing Measurements - 63 Test Results - 64 Chapter 5: Uninterruptible Power Supplies The True UPS - 68 Standby Power Systems and a New Generation of UPS - 69 UPS Backup Time - 74 UPS Testing - 74 Chapter 6: Computers and Power Problems Introduction - 78 The Computer Tolerance Envelope - 78 Ridethrough - 80 Component Degradation and Equipment Failure - 82 Computer Power Supplies - 82 Linear Power Supplies - 83 Switching Power Supplies - 84 PC Tolerance of Powerline Disturbances - 84 Chapter 7: Comparing Power Conditioning Alternatives Voltage Regulation - 89 Isolation - 93 Uninterruptible Power Systems - 94 Computer Surge Suppressors - 98 Summary - 98 Appendices Appendix A: Voltage Clamping Levels of Surge Suppressors - 101 Appendix B: Voltage Clamping Levels of Power Conditioners and Uninterruptible Power Systems - 115 Appendix C: Noise Suppression of Surge Suppressors and Power Conditioners - 129 Appendix D: Waveforms and Regulating Characteristics of Power Conditioners and Uninterruptible Power Systems - 135 Appendix E: Comparison of Voltage Clamping Levels of Surge Suppressors Power Conditioners, Isolation Transformers and Uninterruptible Power Systems to High-Magnitude Impulse Voltages - 151 References - 16

    Hybrid Smart Transformer for Enhanced Power System Protection Against DC With Advanced Grid Support

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    The traditional grid is rapidly transforming into smart substations and grid assets incorporating advanced control equipment with enhanced functionalities and rapid self-healing features. The most important and strategic equipment in the substation is the transformer and is expected to perform a variety of functions beyond mere voltage conversion and isolation. While the concept of smart solid-state transformers (SSTs) is being widely recognized, their respective lifetime and reliability raise concerns, thus hampering the complete replacement of traditional transformers with SSTs. Under this scenario, introducing smart features in conventional transformers utilizing simple, cost-effective, and easy to install modules is a highly desired and logical solution. This dissertation is focused on the design and evaluation of a power electronics-based module integrated between the neutral of power transformers and substation ground. The proposed module transforms conventional transformers into hybrid smart transformers (HST). The HST enhances power system protection against DC flow in grid that could result from solar storms, high-elevation nuclear explosions, monopolar or ground return mode (GRM) operation of high-voltage direct current (HVDC) transmission and non-ideal switching in inverter-based resources (IBRs). The module also introduces a variety of advanced grid-support features in conventional transformers. These include voltage regulation, voltage and impedance balancing, harmonics isolation, power flow control and voltage ride through (VRT) capability for distributed energy resources (DERs) or grid connected IBRs. The dissertation also proposes and evaluates a hybrid bypass switch for HST module and associated transformer protection during high-voltage events at the module output, such as, ground faults, inrush currents, lightning and switching transients. The proposed strategy is evaluated on a scaled hardware prototype utilizing controller hardware-in-the-loop (C-HIL) and power hardware-in-the-loop (P-HIL) techniques. The dissertation also provides guidelines for field implementation and deployment of the proposed HST scheme. The device is proposed as an all-inclusive solution to multiple grid problems as it performs a variety of functions that are currently being performed through separate devices increasing efficiency and justifying its installation
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