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The Internet-of-Things (IoTs) is believed to address social issues and challenges
of the modern world to a large extent. For instance, the issues faced by big cities, such as
increasing rate of unemployment, economic downfall, the air pollution due to emissions from
vehicles or industries, useless energy consumption in homes, or the diculties experienced
by handicapped and elderly people, and so on. The IoTs provides opportunities to develop
applications and platforms that can be used to regulate and reduce the factors that cause
such problems. In addition, the IoTs posses a great potential of providing opportunities to
developing new applications that improve the quality of life of citizens.
The concept of IoTs is to interconnect the electronic devices (referred as things
in the terminology of IoTs) through a cloud network to perform actions without human
interaction. These devices include sensors at one end of the network to sense and collect
information about the environment. A communication medium with specic protocols is
used to transfer this data from sensing nodes to the central units. These units process thecollected data to extract useful information and execute a command or action accordingly.
This idea of interconnected things with ability to act independently is approaching toward
reality with the increase in number of nodes connected to the internet.
The ubiquitous sensing nodes, or simply sensors, deployed in IoTs network are
mobile or installed at xed positions spread over a large area. These nodes rely on wireless
communication technologies to transfer the collected data to the central unit through cloud
platform. Moreover, these nodes are constrained in terms of computational resources, memory
and battery to store energy making it impossible to implement the existing computationally
expensive cybersecurity techniques used in traditional Wireless Sensor Networks (WSNs).
Therefore, the nodes of IoTs are more prone to cyber attacks as compared to the constraintfree
immobile nodes of WSNs. This situation is posing a serious challenge of security and
data privacy while implementing the IoTs network. Furthermore, the performance of IoTs
is highly dependent on the sensed data collected by sensors. A failure originated in these
nodes (also known as fault) may corrupt the collected data leading to serious consequences,
such as huge economic losses, long delays in the network function, or human safety in some
cases. Therefore, it is necessary to develop algorithms for detection or prediction of the fault
occurrence in IoTs nodes. In short, the IoTs network should be capable of detecting faults
and intrusions in order to implement a reliable network free of disturbances. However, the
resource limitation problem of the IoTs nodes and the fact that these nodes are deployed
in the remote areas where the environmental conditions may vary rapidly, put limitations
on the development of faults and intrusion detection mechanisms. Firstly, the algorithms
designed for such sensors should perform well with the given amount of resources in the
node. Secondly, these algorithms should be capable of distinguishing the faults or attacks
from the behaviors occured due to variations in the environment. This dissertation is related
to developing the fault and intrusion detection algorithms for IoTs nodes while consideringthese challenges.
Firstly, a fault detection and diagnosis framework is proposed to identify the
presence and type of fault originated in sensors. A well-known data-driven supervised
machine learning-based classication algorithm, called Support Vector Machine (SVM),
is utilized to design this framework. The classier is given input in terms of statistical
time-domain features extracted from the raw signal of sensor. The dataset used to analyze
the performance of the classier is acquired as follows: normal data signals are obtained from
a temperature-to-voltage converter TC1047/1047A by using an arduino Uno microcontroller,
and a personal computer with MATLAB. Then, ve types of faults, including drift, gain,
precision degradation, spike and stuck fault, are injected in the normal data signals. The
performance of the proposed algorithm is presented under dierent scenarios. Furthermore, a
comparison with neural network-based algorithm show that the proposed algorithm perform
better. Secondly, a novel feature selection scheme is proposed to select most discriminating
subset of features among the whole set of features. The features having high mutual
information with target class and minimum mutual information with non-target class are
selected. This method is veried using the similar dataset. Thirdly, a distributed fault
detection and diagnosis approach is proposed using stacked auto-encoder, SVM, and fuzzy
deep neural network. The fault detection can be implemented in the resource-constrained
sensing nodes to avoid the delays occured due to data transmission between the sensor and
the central unit. Nevertheless, the fault diagnosis can be carried out in the central unit to
obtain more information about the detected fault. Again, this technique is veried using the
similar data with the same types of faults.
Moreover, a lightweight intrusion deteciton system is proposed using SVM-based
classier with three non-complex features, namely mean, maximum, and median. The denialof-
service (DoS) attacks are targetted which have a concomitant increasing or decreasingeect on the trac intensity. The anomaly-based intrusion detection system continuously
monitors the trac intensity attribute of the node to detect intrusion. The performance of
the proposed system is anayzed using a dataset obtained using simulations with dierent
types of DoS attacks, including packets
ooding, vulenerability, blackhole, jamming, selective
forwarding, sybil, sinkhole, clone, wormhole, and hello
ood attacks.
Finally, an SVM-based spectrum sensing technique is proposed to detect the
presence of primary users in cognitive radio networks. The secondary users start transmission
if the channel is sensed idle or free from primary user with a transmission power calculated
based on the quantized sensing result and the residual energy in node battery. The objective
is to improve the throughput performance of the system. The simulation results shows
that the proposed algorithms successfully achieve the objective of improved throughput as
compared to the conventional energy detection technique.
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일반대학원 전기전자컴퓨터공학과
In-Soo Koo
울산대학교 일반대학원 전기전자컴퓨터공학과
울산대학교 논문은 저작권에 의해 보호받습니다.
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Computer Engineering & Information Technology > 2. Theses (Ph.D)
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