15,735 research outputs found
Incremental learning for large-scale stream data and its application to cybersecurity
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 communiļæ½cation 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 advanļæ½tage 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 detecļæ½tion system of malicious spam email and Distributed Denial of Services (DDoS) 377.1781.8781$0,1$+
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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 victimās 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 motherās 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$
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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. 377.1781.8781$0,1$+
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 inļæ½cremental 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 peļæ½riod, selecting essential data from a given chunk can shorten the learning time. In
our method, we select data that are located in untrained or ānot well-learnedā
region and discard data at trained or āwell-learnedā 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 proļæ½posed 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 Ź¼s
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$
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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 indiļæ½cate 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 377.1781.8781+
average over the testing period.
In Chapter 4, we propose a fast Distributed Denial of Service (DDoS) backscatļæ½ter 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 colļæ½lected from January 1st, 2013 until January 20th, 2014 at National Institute of
Information and Communications Technology (NICT), Japan. The results indiļæ½cate 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
Multiphase induction motor drives - a technology status review
The area of multiphase variable-speed motor drives in general and multiphase induction motor drives in particular has experienced a substantial growth since the beginning of this century. Research has been conducted worldwide and numerous interesting developments have been reported in the literature. An attempt is made to provide a detailed overview of the current state-of-the-art in this area. The elaborated aspects include advantages of multiphase induction machines, modelling of multiphase induction machines, basic vector control and direct torque control schemes and PWM control of multiphase voltage source inverters. The authors also provide a detailed survey of the control strategies for five-phase and asymmetrical six-phase induction motor drives, as well as an overview of the approaches to the design of fault tolerant strategies for post-fault drive operation, and a discussion of multiphase multi-motor drives with single inverter supply. Experimental results, collected from various multiphase induction motor drive laboratory rigs, are also included to facilitate the understanding of the drive operatio
System configuration, fault detection, location, isolation and restoration: a review on LVDC Microgrid protections
Low voltage direct current (LVDC) distribution has gained the significant interest of research due to the advancements in power conversion technologies. However, the use of converters has given rise to several technical issues regarding their protections and controls of such devices under faulty conditions. Post-fault behaviour of converter-fed LVDC system involves both active converter control and passive circuit transient of similar time scale, which makes the protection for LVDC distribution significantly different and more challenging than low voltage AC. These protection and operational issues have handicapped the practical applications of DC distribution. This paper presents state-of-the-art protection schemes developed for DC Microgrids. With a close look at practical limitations such as the dependency on modelling accuracy, requirement on communications and so forth, a comprehensive evaluation is carried out on those system approaches in terms of system configurationsļ¼ fault detection, location, isolation and restoration
The Age of Multilevel Converters Arrives
This work is devoted to review and analyze the most relevant characteristics of multilevel converters, to motivate possible solutions, and to show that we are in a decisive instant in which energy companies have to bet on these converters as a good solution compared with classic two-level converters. This article presents a brief overview of the actual applications of multilevel converters and provides an introduction of the modeling techniques and the most common modulation strategies. It also addresses the operational and technological issues
To develop an efficient variable speed compressor motor system
This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque
is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce
dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy
optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization
control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained
clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment
Investigation of FACTS devices to improve power quality in distribution networks
Flexible AC transmission system (FACTS) technologies are power electronic solutions
that improve power transmission through enhanced power transfer volume and stability,
and resolve quality and reliability issues in distribution networks carrying sensitive
equipment and non-linear loads. The use of FACTS in distribution systems is still in
its infancy. Voltages and power ratings in distribution networks are at a level where
realistic FACTS devices can be deployed. Efficient power converters and therefore loss
minimisation are crucial prerequisites for deployment of FACTS devices.
This thesis investigates high power semiconductor device losses in detail. Analytical
closed form equations are developed for conduction loss in power devices as a function
of device ratings and operating conditions. These formulae have been shown to predict
losses very accurately, in line with manufacturer data. The developed formulae enable
circuit designers to quickly estimate circuit losses and determine the sensitivity of those
losses to device voltage and current ratings, and thus select the optimal semiconductor
device for a specific application.
It is shown that in the case of majority carrier devices (such as power MOSFETs), the
conduction power loss (at rated current) increases linearly in relation to the varying rated
current (at constant blocking voltage), but is a square root of the variable blocking voltage
when rated current is fixed. For minority carrier devices (such as a pin diode or IGBT),
a similar relationship is observed for varying current, however where the blocking voltage
is altered, power losses are derived as a square root with an offset (from the origin).
Finally, this thesis conducts a power loss-oriented evaluation of cascade type multilevel
converters suited to reactive power compensation in 11kV and 33kV systems. The cascade
cell converter is constructed from a series arrangement of cell modules. Two prospective
structures of cascade type converters were compared as a case study: the traditional type
which uses equal-sized cells in its chain, and a second with a ternary relationship between
its dc-link voltages. Modelling (at 81 and 27 levels) was carried out under steady state
conditions, with simplified models based on the switching function and using standard
circuit simulators. A detailed survey of non punch through (NPT) and punch through
(PT) IGBTs was completed for the purpose of designing the two cascaded converters.
Results show that conduction losses are dominant in both types of converters in NPT
and PT IGBTs for 11kV and 33kV systems. The equal-sized converter is only likely to
be useful in one case (27-levels in the 33kV system). The ternary-sequence converter
produces lower losses in all other cases, and this is especially noticeable for the 81-level
converter operating in an 11kV network
High-performance motor drives
This article reviews the present state and trends in the development of key parts of controlled induction motor drive systems: converter topologies, modulation methods, as well as control and estimation techniques. Two- and multilevel voltage-source converters, current-source converters, and direct converters are described. The main part of all the produced electric energy is used to feed electric motors, and the conversion of electrical power into mechanical power involves motors ranges from less than 1 W up to several dozen megawatts
A Review of DC Shipboard Microgrids:Part I: Power Architectures, Energy Storage and Power Converters
Hybrid Synchronized PWM Schemes for Closed-Loop Current Control of High-Power Motor Drives
Ā© 1982-2012 IEEE. For high-power drives, switching frequency is usually restricted to several hundred hertz to minimize the switching losses. To maintain the current distortions and torque ripples at a reasonable level, synchronized pulse patterns with half-wave and quarter-wave symmetries are employed. The analytic compensation is derived by Fourier analysis to ensure the proportionality between the voltage reference and the output voltage of an inverter for pulse width modulation (PWM) with low pulse ratio. A simple yet very effective method with varying sampling rate is proposed to maintain synchronization even for fast dynamic processes. The fast and smooth transition between different PWM patterns is achieved by compensating phase angle of the voltage reference through the analysis of stator flux trajectories. The effectiveness of the proposed method is validated on a down-scaled 2.2-kW induction motor drives
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