37 research outputs found
Advances in Data Mining Knowledge Discovery and Applications
Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications
Timely Classification of Encrypted or ProtocolObfuscated Internet Traffic Using Statistical Methods
Internet traffic classification aims to identify the type of application or protocol that generated
a particular packet or stream of packets on the network. Through traffic classification,
Internet Service Providers (ISPs), governments, and network administrators can
access basic functions and several solutions, including network management, advanced
network monitoring, network auditing, and anomaly detection. Traffic classification is
essential as it ensures the Quality of Service (QoS) of the network, as well as allowing
efficient resource planning.
With the increase of encrypted or obfuscated protocol traffic on the Internet and multilayer
data encapsulation, some classical classification methods have lost interest from the
scientific community. The limitations of traditional classification methods based on port
numbers and payload inspection to classify encrypted or obfuscated Internet traffic have
led to significant research efforts focused on Machine Learning (ML) based classification
approaches using statistical features from the transport layer. In an attempt to increase
classification performance, Machine Learning strategies have gained interest from the scientific
community and have shown promise in the future of traffic classification, specially
to recognize encrypted traffic.
However, ML approach also has its own limitations, as some of these methods have a
high computational resource consumption, which limits their application when classifying
large traffic or realtime
flows. Limitations of ML application have led to the investigation
of alternative approaches, including featurebased
procedures and statistical methods. In
this sense, statistical analysis methods, such as distances and divergences, have been used
to classify traffic in large flows and in realtime.
The main objective of statistical distance is to differentiate flows and find a pattern in
traffic characteristics through statistical properties, which enable classification. Divergences
are functional expressions often related to information theory, which measure the
degree of discrepancy between any two distributions.
This thesis focuses on proposing a new methodological approach to classify encrypted
or obfuscated Internet traffic based on statistical methods that enable the evaluation of
network traffic classification performance, including the use of computational resources
in terms of CPU and memory. A set of traffic classifiers based on KullbackLeibler
and
JensenShannon
divergences, and Euclidean, Hellinger, Bhattacharyya, and Wootters distances
were proposed. The following are the four main contributions to the advancement
of scientific knowledge reported in this thesis.
First, an extensive literature review on the classification of encrypted and obfuscated Internet traffic was conducted. The results suggest that portbased
and payloadbased
methods are becoming obsolete due to the increasing use of traffic encryption and multilayer
data encapsulation. MLbased
methods are also becoming limited due to their computational
complexity. As an alternative, Support Vector Machine (SVM), which is also
an ML method, and the KolmogorovSmirnov
and Chisquared
tests can be used as reference
for statistical classification. In parallel, the possibility of using statistical methods
for Internet traffic classification has emerged in the literature, with the potential of good
results in classification without the need of large computational resources. The potential
statistical methods are Euclidean Distance, Hellinger Distance, Bhattacharyya Distance,
Wootters Distance, as well as KullbackLeibler
(KL) and JensenShannon
divergences.
Second, we present a proposal and implementation of a classifier based on SVM for P2P
multimedia traffic, comparing the results with KolmogorovSmirnov
(KS) and Chisquare
tests. The results suggest that SVM classification with Linear kernel leads to a better classification
performance than KS and Chisquare
tests, depending on the value assigned to
the Self C parameter. The SVM method with Linear kernel and suitable values for the Self
C parameter may be a good choice to identify encrypted P2P multimedia traffic on the
Internet.
Third, we present a proposal and implementation of two classifiers based on KL Divergence
and Euclidean Distance, which are compared to SVM with Linear kernel, configured
with the standard Self C parameter, showing a reduced ability to classify flows based
solely on packet sizes compared to KL and Euclidean Distance methods. KL and Euclidean
methods were able to classify all tested applications, particularly streaming and P2P,
where for almost all cases they efficiently identified them with high accuracy, with reduced
consumption of computational resources. Based on the obtained results, it can be
concluded that KL and Euclidean Distance methods are an alternative to SVM, as these
statistical approaches can operate in realtime
and do not require retraining every time a
new type of traffic emerges.
Fourth, we present a proposal and implementation of a set of classifiers for encrypted
Internet traffic, based on JensenShannon
Divergence and Hellinger, Bhattacharyya, and
Wootters Distances, with their respective results compared to those obtained with methods
based on Euclidean Distance, KL, KS, and ChiSquare.
Additionally, we present a comparative
qualitative analysis of the tested methods based on Kappa values and Receiver
Operating Characteristic (ROC) curves. The results suggest average accuracy values above
90% for all statistical methods, classified as ”almost perfect reliability” in terms of Kappa
values, with the exception of KS. This result indicates that these methods are viable options
to classify encrypted Internet traffic, especially Hellinger Distance, which showed
the best Kappa values compared to other classifiers. We conclude that the considered
statistical methods can be accurate and costeffective
in terms of computational resource
consumption to classify network traffic. Our approach was based on the classification of Internet network traffic, focusing on statistical
distances and divergences. We have shown that it is possible to classify and obtain
good results with statistical methods, balancing classification performance and the
use of computational resources in terms of CPU and memory. The validation of the proposal
supports the argument of this thesis, which proposes the implementation of statistical
methods as a viable alternative to Internet traffic classification compared to methods
based on port numbers, payload inspection, and ML.A classificação de tráfego Internet visa identificar o tipo de aplicação ou protocolo que
gerou um determinado pacote ou fluxo de pacotes na rede. Através da classificação de
tráfego, Fornecedores de Serviços de Internet (ISP), governos e administradores de rede
podem ter acesso às funções básicas e várias soluções, incluindo gestão da rede, monitoramento
avançado de rede, auditoria de rede e deteção de anomalias. Classificar o tráfego é
essencial, pois assegura a Qualidade de Serviço (QoS) da rede, além de permitir planear
com eficiência o uso de recursos.
Com o aumento de tráfego cifrado ou protocolo ofuscado na Internet e do encapsulamento
de dados multicamadas, alguns métodos clássicos da classificação perderam interesse de
investigação da comunidade científica. As limitações dos métodos tradicionais da classificação
com base no número da porta e na inspeção de carga útil payload para classificar
o tráfego de Internet cifrado ou ofuscado levaram a esforços significativos de investigação
com foco em abordagens da classificação baseadas em técnicas de Aprendizagem
Automática (ML) usando recursos estatísticos da camada de transporte. Na tentativa
de aumentar o desempenho da classificação, as estratégias de Aprendizagem Automática
ganharam o interesse da comunidade científica e se mostraram promissoras no futuro da
classificação de tráfego, principalmente no reconhecimento de tráfego cifrado.
No entanto, a abordagem em ML também têm as suas próprias limitações,
pois alguns
desses métodos possuem um elevado consumo de recursos computacionais, o que limita
a sua aplicação para classificação de grandes fluxos de tráfego ou em tempo real. As limitações
no âmbito da aplicação de ML levaram à investigação de abordagens alternativas,
incluindo procedimentos baseados em características e métodos estatísticos. Neste sentido,
os métodos de análise estatística, tais como distâncias e divergências, têm sido utilizados
para classificar tráfego em grandes fluxos e em tempo real.
A distância estatística possui como objetivo principal diferenciar os fluxos e permite encontrar
um padrão nas características de tráfego através de propriedades estatísticas, que
possibilitam a classificação. As divergências são expressões funcionais frequentemente
relacionadas com a teoria da informação, que mede o grau de discrepância entre duas
distribuições quaisquer.
Esta tese focase
na proposta de uma nova abordagem metodológica para classificação de
tráfego cifrado ou ofuscado da Internet com base em métodos estatísticos que possibilite
avaliar o desempenho da classificação de tráfego de rede, incluindo a utilização de recursos
computacionais, em termos de CPU e memória. Foi proposto um conjunto de classificadores
de tráfego baseados nas Divergências de KullbackLeibler
e JensenShannon
e Distâncias Euclidiana, Hellinger, Bhattacharyya e Wootters. A seguir resumemse
os tese.
Primeiro, realizámos uma ampla revisão de literatura sobre classificação de tráfego cifrado
e ofuscado de Internet. Os resultados sugerem que os métodos baseados em porta e
baseados em carga útil estão se tornando obsoletos em função do crescimento da utilização
de cifragem de tráfego e encapsulamento de dados multicamada. O tipo de métodos
baseados em ML também está se tornando limitado em função da complexidade computacional.
Como alternativa, podese
utilizar a Máquina de Vetor de Suporte (SVM),
que também é um método de ML, e os testes de KolmogorovSmirnov
e Quiquadrado
como referência de comparação da classificação estatística. Em paralelo, surgiu na literatura
a possibilidade de utilização de métodos estatísticos para classificação de tráfego
de Internet, com potencial de bons resultados na classificação sem aporte de grandes recursos
computacionais. Os métodos estatísticos potenciais são as Distâncias Euclidiana,
Hellinger, Bhattacharyya e Wootters, além das Divergências de Kullback–Leibler (KL) e
JensenShannon.
Segundo, apresentamos uma proposta e implementação de um classificador baseado na
Máquina de Vetor de Suporte (SVM) para o tráfego multimédia P2P (PeertoPeer),
comparando
os resultados com os testes de KolmogorovSmirnov
(KS) e Quiquadrado.
Os
resultados sugerem que a classificação da SVM com kernel Linear conduz a um melhor
desempenho da classificação do que os testes KS e Quiquadrado,
dependente do valor
atribuído ao parâmetro Self C. O método SVM com kernel Linear e com valores adequados
para o parâmetro Self C pode ser uma boa escolha para identificar o tráfego Par a Par
(P2P) multimédia cifrado na Internet.
Terceiro, apresentamos uma proposta e implementação de dois classificadores baseados
na Divergência de KullbackLeibler (KL) e na Distância Euclidiana, sendo comparados
com a SVM com kernel Linear, configurado para o parâmestro Self C padrão, apresenta
reduzida
capacidade de classificar fluxos com base apenas nos tamanhos dos pacotes
em relação aos métodos KL e Distância Euclidiana. Os métodos KL e Euclidiano foram
capazes de classificar todas as aplicações testadas, destacandose
streaming e P2P, onde
para quase todos os casos foi eficiente identificálas
com alta precisão, com reduzido consumo
de recursos computacionais.Com base nos resultados obtidos, podese
concluir que
os métodos KL e Distância Euclidiana são uma alternativa à SVM, porque essas abordagens
estatísticas podem operar em tempo real e não precisam de retreinamento cada vez
que surge um novo tipo de tráfego.
Quarto, apresentamos uma proposta e implementação de um conjunto de classificadores
para o tráfego de Internet cifrado, baseados na Divergência de JensenShannon
e nas Distâncias
de Hellinger, Bhattacharyya e Wootters, sendo os respetivos resultados comparados
com os resultados obtidos com os métodos baseados na Distância Euclidiana, KL, KS e Quiquadrado.
Além disso, apresentamos uma análise qualitativa comparativa dos
métodos testados com base nos valores de Kappa e Curvas Característica de Operação do
Receptor (ROC). Os resultados sugerem valores médios de precisão acima de 90% para todos
os métodos estatísticos, classificados como “confiabilidade quase perfeita” em valores
de Kappa, com exceçãode KS. Esse resultado indica que esses métodos são opções viáveis
para a classificação de tráfego cifrado da Internet, em especial a Distância de Hellinger,
que apresentou os melhores resultados do valor de Kappa em comparaçãocom os demais
classificadores. Concluise
que os métodos estatísticos considerados podem ser precisos e
económicos em termos de consumo de recursos computacionais para classificar o tráfego
da rede.
A nossa abordagem baseouse
na classificação de tráfego de rede Internet, focando em
distâncias e divergências estatísticas. Nós mostramos que é possível classificar e obter
bons resultados com métodos estatísticos, equilibrando desempenho de classificação e
uso de recursos computacionais em termos de CPU e memória. A validação da proposta
sustenta o argumento desta tese, que propõe a implementação de métodos estatísticos
como alternativa viável à classificação de tráfego da Internet em relação aos métodos com
base no número da porta, na inspeção de carga útil e de ML.Thesis prepared at Instituto de Telecomunicações Delegação
da Covilhã and at the Department
of Computer Science of the University of Beira Interior, and submitted to the
University of Beira Interior for discussion in public session to obtain the Ph.D. Degree in
Computer Science and Engineering.
This work has been funded by Portuguese FCT/MCTES through national funds and, when
applicable, cofunded
by EU funds under the project UIDB/50008/2020, and by operation
Centro010145FEDER000019
C4
Centro
de Competências em Cloud Computing,
cofunded
by the European Regional Development Fund (ERDF/FEDER) through
the Programa Operacional Regional do Centro (Centro 2020). This work has also been
funded by CAPES (Brazilian Federal Agency for Support and Evaluation of Graduate Education)
within the Ministry of Education of Brazil under a scholarship supported by the
International Cooperation Program CAPES/COFECUB Project
9090134/
2013 at the
University of Beira Interior
Computer Science & Technology Series : XVIII Argentine Congress of Computer Science. Selected papers
CACIC’12 was the eighteenth Congress in the CACIC series. It was organized by the School of Computer Science and Engineering at the Universidad Nacional del Sur.
The Congress included 13 Workshops with 178 accepted papers, 5 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses.
CACIC 2012 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research.
Each topic was supervised by a committee of 3-5 chairs of different Universities.
The call for papers attracted a total of 302 submissions. An average of 2.5 review reports were collected for each paper, for a grand total of 752 review reports that involved about 410 different reviewers.
A total of 178 full papers, involving 496 authors and 83 Universities, were accepted and 27 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Data Hiding and Its Applications
Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others
Low-rate attack detection with intelligent fine-grained network analysis
Low-rate attacks are a type of attacks that silently infiltrate the victim network, control computers, and steal sensitive data. As the effect of this attack type is devastating, it is essential to be able to detect such attacks. A detection system allows system administrators to react accordingly. More importantly, when the detection system is to analyse the network traffic, it may identify the malicious activity before the attack reaches the system. And by incorporating machine learning into the detection approach, the Network-based Intrusion Detection System (NIDS) will be able to adapt to evolving attacks and minimise human intervention, unlike signature-based NIDS.
Several works have tried to address the problem of low-rate attack detection. However, there are several issues with these previous works. Some of them are dated; therefore their performance drops on contemporary low-rate attacks. Some of them only focus on detecting attacks in one protocol, while low-rate attacks exist on various protocols. To tackle this problem, we proposed two Deep Learning (DL) models which analyse network payload and were trained with the unsupervised approach. Our best performing model surpasses the state-of-the-arts and provides an improvement in detection rate of at least 12.04%. The experiments also show that payload-based NIDSs are superior to header-based ones for identifying low-rate attacks.
A common approach in payload-based NIDSs is to read the full-length application layer messages, while in some protocols such as HTTP or SMTP, it is usual to have lengthy messages. Processing the full-length of such messages would be time-consuming. The damage from the attack may have been done by the time the decision for the particular message comes out. Therefore, we proposed an approach that can early predict the occurrence of low-rate attacks from as little information as possible. Based on our experiments, the proposed method can detect 97.57% of attacks by merely reading, on average, 35.21% of the application layer messages. It improves the detection speed by
three-fold
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
The ongoing deployment of the fifth generation (5G) wireless networks
constantly reveals limitations concerning its original concept as a key driver
of Internet of Everything (IoE) applications. These 5G challenges are behind
worldwide efforts to enable future networks, such as sixth generation (6G)
networks, to efficiently support sophisticated applications ranging from
autonomous driving capabilities to the Metaverse. Edge learning is a new and
powerful approach to training models across distributed clients while
protecting the privacy of their data. This approach is expected to be embedded
within future network infrastructures, including 6G, to solve challenging
problems such as resource management and behavior prediction. This survey
article provides a holistic review of the most recent research focused on edge
learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the
existing surveys on machine learning for 6G IoT security and machine
learning-associated threats in three different learning modes: centralized,
federated, and distributed. Then, we provide an overview of enabling emerging
technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of
existing research on attacks against machine learning and classify threat
models into eight categories, including backdoor attacks, adversarial examples,
combined attacks, poisoning attacks, Sybil attacks, byzantine attacks,
inference attacks, and dropping attacks. In addition, we provide a
comprehensive and detailed taxonomy and a side-by-side comparison of the
state-of-the-art defense methods against edge learning vulnerabilities.
Finally, as new attacks and defense technologies are realized, new research and
future overall prospects for 6G-enabled IoT are discussed
Applied Methuerstic computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC