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99.referencias.bib
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@manual{NBR6028:2003,
address = {Rio de Janeiro},
date-added = {2012-12-15 21:02:12 +0000},
date-modified = {2012-12-15 21:02:50 +0000},
month = nov,
org-short = {ABNT},
organization = {Associa{\c c}\~ao Brasileira de Normas T\'ecnicas},
pages = 2,
subtitle = {Resumo - Apresenta{\c c}{\~a}o},
title = {{NBR} 6028},
year = 2003
}
@inproceedings{Andy2017,
title={{Attack scenarios and security analysis of mqtt communication protocol in iot system}},
author={Andy,Syaiful and Rahardjo,Budi and Hanindhito,Bagus},
abstract={Various communication protocols are currently used in the Internet
of Things (IoT) devices. One of the protocols that are already standardized by
ISO is MQTT protocol (ISO / IEC 20922: 2016). Many IoT developers use this
protocol because of its minimal bandwidth requirement and low memory
consumption. Sometimes,IoT device sends confidential data that should only be
accessed by authorized people or devices. Unfortunately,the MQTT protocol only
provides authentication for the security mechanism which,by default,does not
encrypt the data in transit thus data privacy,authentication,and data
integrity become problems in MQTT implementation. This paper discusses several
reasons on why there are many IoT system that does not implement adequate
security mechanism. Next,it also demonstrates and analyzes how we can attack
this protocol easily using several attack scenarios. Finally,after the
vulnerabilities of this protocol have been examined,we can improve our
security awareness especially in MQTT protocol and then implement security
mechanism in our MQTT system to prevent such attack. {\textcopyright}
2018,Institute of Advanced Engineering and Science. All rights reserved.},
keywords={Attack,MQTT,Protocol,Scenario},
journal={International Conference on Electrical Engineering,Computer Science and Informatics (EECSI)},
doi={10.11591/eecsi.4.1064},
isbn={9781538605486},
issn={2407439X},
number={September},
pages={600--604},
volume={4},
year={2017}
}
@article{Choudhary2018,
abstract={Internet of things (IoT) is the smart network which connects smart objects over the Internet. The Internet is un- trusted and unreliable network and thus IoT network is vulner- able to different kind of attacks. Conventional encryption and authentication techniques sometimes fail on IoT based network and intrusion may succeed to destroy the network. So,it is necessary to design intrusion detection system for such network. In our paper,we detect routing attacks such as sinkhole and selective forwarding. We have also tried to prevent our net- work from these attacks. We designed detection and prevention algorithm,i.e.,KMA(Key Match Algorithm) and CBA(Cluster- Based Algorithm) in MatLab simulation environment. We gave two intrusion detection mechanisms and compared their results as well. True positive intrusion detection rate for our work is between 50{\%} to 80{\%} with KMA and 76{\%} to 96{\%} with CBA algorithm.},
author={Choudhary,Sarika and Kesswani,Nishtha},
doi={10.1109/TrustCom/BigDataSE.2018.00219},
isbn={978-1-5386-4388-4},
journal={2018 17th IEEE International Conference On Trust,Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)},
keywords={(IDS),6LoWPAN,Internet of Things,Intrusion Detection System,RPL,Routing Attack,Security,Selective Forwarding,Sinkhole},
mendeley-groups={UFSCar/trab-fichamento[/trab-final},
pages={1537--1540},
publisher={IEEE},
title={{Detection and Prevention of Routing Attacks in Internet of Things}},
url={https://ieeexplore.ieee.org/document/8456088/},
year={2018}
}
@article{DO2018,
title={Cyber-physical systems information gathering: A smart home case study},
abstract={With the growth in the use of Cyber-Physical Systems,such as Internet of Things (IoT) devices,there is a corresponding increase in the potential attack footprint of personal and corporate users. In this paper,we explore the potential for exploiting information retrieved from two IoT devices which,seemingly,are unlikely to store substantial amounts of data. We specifically focus on prominent smart home devices for the purpose of obtaining compromising information. We undertake a collection and analysis process,constrained by the limitations placed upon three types of adversaries,namely: forensic passive,forensic active and real-time active. The former two adversaries aim to comply with the requirements of forensic soundness,whereas the real-time active adversary does not have these constraints and therefore more closely models a malicious real-world attacker. The findings show that a variety of device data is available to even the passive adversary,and this data can be used to determine the actions and/or presence of an individual at a given time based on their interactions with the IoT device. These interactions can be both user initiated (e.g. powering on or off a switch or light) and device initiated (e.g. background polling).},
author={Do,Quang and Martini,Ben and Choo,Kim-Kwang Raymond},
doi={https://doi.org/10.1016/j.comnet.2018.03.024},
issn={1389-1286},
journal={Computer Networks},
keywords={Cyber-physical system forensics,Forensic adversary,Internet of Battlefield Things forensics,Internet of Things forensics,Smart home forensics},
mendeley-groups={UFSCar/trab-fichamento[,UFSCar/trab-fichamento[/trab-final},
pages={1--12},
url={http://www.sciencedirect.com/science/article/pii/S1389128618301440},
volume={138},
year={2018}
}
@article{Acar2018,
abstract={In this paper,we present two web-based attacks against local IoT de- vices that any malicious web page or third-party script can perform,even when the devices are behind NATs. In our attack scenario,a victim visits the attacker's website,which contains a malicious script that communicates with IoT devices on the local network that have open HTTP servers. We show how the malicious script can circumvent the same-origin policy by exploiting error messages on the HTML5 MediaError interface or by carrying out DNS rebinding attacks. We demonstrate that the attacker can gather sensitive infor- mation from the devices (e.g.,unique device identifiers and precise geolocation),track and profile the owners to serve ads,or control the devices by playing arbitrary videos and rebooting. We propose potential countermeasures to our attacks that users,browsers,DNS providers,and IoT vendors can implement.},
author={Acar,Gunes and Huang,Danny Yuxing and Li,Frank and Narayanan,Arvind and Feamster,Nick},
doi={10.1145/3229565.3229568},
isbn={9781450359054},
journal={Proceedings of the 2018 Workshop on IoT Security and Privacy - IoT S{\&}P '18},
keywords={DNS rebinding,Internet of Things,JavaScript,privacy},
mendeley-groups={UFSCar/trab-fichamento[/trab-final},
pages={29--35},
title={{Web-based Attacks to Discover and Control Local IoT Devices}},
url={http://dl.acm.org/citation.cfm?doid=3229565.3229568},
year={2018}
}
@article{dean2008mapreduce,
title={MapReduce: simplified data processing on large clusters},
author={Dean,Jeffrey and Ghemawat,Sanjay},
journal={Communications of the ACM},
volume={51},
number={1},
pages={107--113},
year={2008},
publisher={ACM}
}
@article{Dean2004,
title={{MapReduce: Simplified data processing on large clusters}},
author={Dean,Jeffrey and Ghemawat,Sanjay},
journal={OSDI 2004 - 6th Symposium on Operating Systems Design and Implementation},
doi={10.21276/ijre.2018.5.5.4},
issn={23487852},
pages={137--149},
year={2004}
}
@inproceedings{Pandey2014,
author={Pandey,Shweta and Tokekar,Vrinda},
title={Prominence of MapReduce in Big Data Processing},
year={2014},
isbn={9781479930708},
publisher={IEEE Computer Society},
address={USA},
url={https://doi.org/10.1109/CSNT.2014.117},
doi={10.1109/CSNT.2014.117},
booktitle={Proceedings of the 2014 Fourth International Conference on Communication Systems and Network Technologies},
pages={555–560},
numpages={6},
keywords={MapReduce,Hadoop Distributed File System,Hadoop,Google file System,Big Data},
series={CSNT ’14}
}
@misc{ApacheHadoop2020,
title={{The Apache™ Hadoop® project develops open-source software for reliable,scalable,distributed computing.}},
author= {{Apache Hadoop}},
url={https://hadoop.apache.org/},
urldate={2020-02-10},
year={2020}
}
@misc{ApacheSpark2020,
title={{Apache Spark™ - Unified Analytics Engine for Big Data}},
author= {{Apache Spark}},
url={https://spark.apache.org/},
urldate={2020-02-10},
year={2020}
}
@techreport{Zaharia,
title={{Spark: Cluster Computing with Working Sets}},
author={Zaharia,Matei and Chowdhury,Mosharaf and Franklin,Michael J and Shenker,Scott},
year= {2010},
journal={HotCloud},
volume={10},
number={10-10},
pages={95},
abstract={MapReduce and its variants have been highly successful in
implementing large-scale data-intensive applications on commodity clusters.
However,most of these systems are built around an acyclic data flow model
that is not suitable for other popular applications. This paper fo-cuses on
one such class of applications: those that reuse a working set of data across
multiple parallel operations. This includes many iterative machine learning
algorithms,as well as interactive data analysis tools. We propose a new
framework called Spark that supports these applications while retaining the
scalability and fault tolerance of MapReduce. To achieve these goals,Spark
introduces an abstraction called resilient distributed datasets (RDDs). An RDD
is a read-only collection of objects partitioned across a set of machines that
can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in
iterative machine learning jobs,and can be used to interactively query a 39
GB dataset with sub-second response time.}
}
@article{sparkStreaming2016,
title = {{Apache spark: A unified engine for big data processing}},
author = {Zaharia,Matei and Xin,Reynold S. and Wendell,Patrick and Das,Tathagata
and Armbrust,Michael and Dave,Ankur and Meng,Xiangrui and Rosen,Josh and
Venkataraman,Shivaram and Franklin,Michael J. and Ghodsi,Ali and
Gonzalez,Joseph and Shenker,Scott and Stoica,Ion},
doi = {10.1145/2934664},
issn = {15577317},
journal = {Communications of the ACM},
number = {11},
pages = {56--65},
volume = {59},
year = {2016}
}
% referencias da Quali Gui Cassales
% (PERNER,2007)
% PERNER2007,
% author="Perner,Petra",
% title="Concepts for Novelty Detection and Handling Based on a Case-Based Reasoning Process Scheme",
% booktitle="Advances in Data Mining. Theoretical Aspects and Applications",
% year="2007",
% publisher="Springer",
% pages="21--33",
% isbn="978-3-540-73435-2"
% }
% Perner2009,
% title="Concepts for novelty detection and handling based on a case-based reasoning process scheme",
% author="Petra Perner",
% (GAMA,2010).
% (MUKKAMALA; SUNG; ABRAHAM,2005)
@article{McHugh2000,
author={McHugh,John},
title={Testing Intrusion Detection Systems: A Critique of the 1998 and 1999 DARPA Intrusion Detection System Evaluations As Performed by Lincoln Laboratory},
journal={ACM Trans. Inf. Syst. Secur.},
issue_date={Nov. 2000},
volume={3},
number={4},
month=nov,
year={2000},
issn={1094-9224},
pages={262--294},
numpages={33},
url={http://doi.acm.org/10.1145/382912.382923},
doi={10.1145/382912.382923},
acmid={382923},
publisher={ACM},
address={New York,NY,USA},
keywords={computer security,intrusion detection,receiver operating curves (ROC),software evaluation},
}
@INPROCEEDINGS{Sommer2010,
author={R. {Sommer} and V. {Paxson}},
booktitle={2010 IEEE Symposium on Security and Privacy},
title={Outside the Closed World: On Using Machine Learning for Network Intrusion Detection},
year={2010},
volume={},
number={},
pages={305-316},
keywords={learning (artificial intelligence);security of data;machine learning;network intrusion detection;anomaly detection;Machine learning;Intrusion detection;Computer science;Telecommunication traffic;Guidelines;Computer security;National security;Privacy;Laboratories;Computerized monitoring;anomaly detection;machine learning;intrusion detection;network security},
doi={10.1109/SP.2010.25},
ISSN={2375-1207},
month={May}
}
@inproceedings{Striki2009,
author={Striki,Maria and Manousakis,Kyriakos and Kindred,Darrell and Sterne,Dan and Lawler,Geoff and Ivanic,Natalie and Tran,George},
title={Quantifying Resiliency and Detection Latency of Intrusion Detection Structures},
booktitle={Proceedings of the 28th IEEE Conference on Military Communications},
series={MILCOM'09},
year={2009},
isbn={978-1-4244-5238-5},
location={Boston,Massachusetts,USA},
pages={2205--2212},
numpages={8},
url={http://dl.acm.org/citation.cfm?id=1856821.1857149},
acmid={1857149},
publisher={IEEE Press},
address={Piscataway,NJ,USA},
}
@article{ModiIDSCloud,
author={Modi,Chirag and Patel,Dhiren and Borisaniya,Bhavesh and Patel,Hiren and Patel,Avi and Rajarajan,Muttukrishnan},
title={Review: A Survey of Intrusion Detection Techniques in Cloud},
journal={J. Netw. Comput. Appl.},
issue_date={January,2013},
volume={36},
number={1},
month=jan,
year={2013},
issn={1084-8045},
pages={42--57},
numpages={16},
url={http://dx.doi.org/10.1016/j.jnca.2012.05.003},
doi={10.1016/j.jnca.2012.05.003},
acmid={2406205},
publisher={Academic Press Ltd.},
address={London,UK,UK},
keywords={Cloud computing,Firewalls,Intrusion detection system,Intrusion prevention system},
}
% ====== inseridos por hermes
@article{dastjerdi2016,
title={Fog computing: Helping the Internet of Things realize its potential},
author={Dastjerdi,Amir Vahid and Buyya,Rajkumar},
abstract={The Internet of Things (IoT) could enable innovations that enhance
the quality of life,but it generates unprecedented amounts of data that are
difficult for traditional systems,the cloud,and even edge computing to
handle. Fog computing is designed to overcome these limitations.},
keywords={data handling;Internet of Things;fog computing;Internet of
Things;IoT;data handling;Internet of things;Data analysis;Big data;Cloud
computing;Internet of Things;IoT;cloud computing;Cloud Cover;fog computing},
journal={Computer},
volume={49},
number={8},
pages={112--116},
year={2016},
doi={10.1109/MC.2016.245},
ISSN={1558-0814},
month={Aug},
publisher={IEEE}
}
@misc{gartner_it_glossary_2018,
title={Gartner Says Worldwide IoT Security Spending Will Reach \$1.5 Billion in 2018},
author={Gartner},
url={https://www.gartner.com/newsroom/id/3869181},
journal={Gartner IT Glossary},
publisher={Gartner,Inc.},
year={2018},
month={Mar}}
@misc{gartner_forecast_2017,
title={Gartner Says 8.4 Billion Connected ``Things'' Will Be in Use in 2017,Up 31 Percent From 2016},
author={Gartner},
url={https://www.gartner.com/en/newsroom/press-releases/2017-02-07-gartner-says-8-billion-connected-things-will-be-in-use-in-2017-up-31-percent-from-2016},
journal={Gartner Press Release},
publisher={Gartner,Inc.},
year={2017}
}
@article{mitchell2014survey,
title={A survey of intrusion detection techniques for cyber-physical systems},
author={Mitchell,Robert and Chen,Ing-Ray},
journal={ACM Computing Surveys (CSUR)},
volume={46},
number={4},
pages={55},
year={2014},
publisher={ACM}
}
@inproceedings{roesch1999snort,
title={Snort: Lightweight intrusion detection for networks.},
author={Roesch,Martin and others},
booktitle={Lisa},
volume={99},
number={1},
pages={229--238},
year={1999}
}
@article{buczak2016survey,
title={A survey of data mining and machine learning methods for cyber security intrusion detection},
author={Buczak,Anna L and Guven,Erhan},
journal={IEEE Communications Surveys \& Tutorials},
volume={18},
number={2},
pages={1153--1176},
year={2016},
publisher={IEEE}
}
@article{da2014internet,
title={Internet of things in industries: A survey},
author={Da Xu,Li and He,Wu and Li,Shancang},
journal={IEEE Transactions on industrial informatics},
volume={10},
number={4},
pages={2233--2243},
year={2014},
publisher={IEEE}
}
@article{gubbi2013internet,
title={Internet of Things (IoT): A vision,architectural elements,and future directions},
author={Gubbi,Jayavardhana and Buyya,Rajkumar and Marusic,Slaven and Palaniswami,Marimuthu},
journal={Future generation computer systems},
volume={29},
number={7},
pages={1645--1660},
year={2013},
publisher={Elsevier}
}
% ============
@inproceedings{dos-6lowpan-iot,
author={P. Kasinathan and C. Pastrone and M. A. Spirito and M. Vinkovits},
booktitle={IEEE 9th Intl. Conf. on Wireless and Mobile Computing,Networking and Communications (WiMob)},
title={Denial-of-Service detection in 6LoWPAN based Internet of Things},
year={2013},
volume={},
number={},
pages={600-607},
doi={10.1109/WiMOB.2013.6673419},
ISSN={2160-4886},
month={Oct}
}
@INPROCEEDINGS{Hybrid-ids-arch-iot,
author={M. Sheikhan and H. Bostani},
booktitle={8th Intl. Symp. on Telecommunications (IST)},
title={A hybrid intrusion detection architecture for Internet of things},
year={2016},
volume={},
number={},
pages={601-606},
doi={10.1109/ISTEL.2016.7881893},
ISSN={},
month={Sept}
}
@INPROCEEDINGS{Kalis,
author={D. Midi and A. Rullo and A. Mudgerikar and E. Bertino},
booktitle={IEEE Intl. Conf. on Distributed Computing Systems (ICDCS)},
title={Kalis - A System for Knowledge-Driven Adaptable Intrusion Detection for the Internet of Things},
year={2017},
volume={},
number={},
pages={656-666},
doi={10.1109/ICDCS.2017.104},
ISSN={1063-6927},
month={June}}
@article{SVELTE,
title="SVELTE: Real-time intrusion detection in the Internet of Things",
journal="Ad Hoc Networks",
volume="11",
number="8",
pages="2661 - 2674",
year="2013",
issn="1570-8705",
doi="https://doi.org/10.1016/j.adhoc.2013.04.014",
author="Shahid Raza and Linus Wallgren and Thiemo Voigt",
keywords="Intrusion detection,Internet of Things,6LoWPAN,RPL,IPv6,Security,Sensor networks"
}
@article{anomalies-adaptive-detection,
author={Zhang,Ji and Li,Hongzhou and Gao,Qigang and Wang,Hai and Luo,Yonglong},
title={Detecting Anomalies from Big Network Traffic Data Using an Adaptive Detection Approach},
journal={Inf. Sci.},
issue_date={October 2015},
volume={318},
number={C},
month=oct,
year={2015},
issn={0020-0255},
pages={91--110},
numpages={20},
doi={10.1016/j.ins.2014.07.044},
acmid={2794100},
publisher={Elsevier Science Inc.},
address={New York,NY,USA},
keywords={Anomaly detection,Big data,Outlier detection},
}
@article{IoT-arch-smartmeter,
author={J. Lloret and J. Tomas and A. Canovas and L. Parra},
journal={IEEE Communications Magazine},
title={An Integrated IoT Architecture for Smart Metering},
year={2016},
volume={54},
number={12},
pages={50-57},
doi={10.1109/MCOM.2016.1600647CM},
ISSN={0163-6804}}
@article{scalable-anomaly-detection-smart-city,
author={D. E. Difallah and P. Cudré-Mauroux and S. A. McKenna},
journal={IEEE Internet Computing},
title={Scalable Anomaly Detection for Smart City Infrastructure Networks},
year={2013},
volume={17},
number={6},
pages={39-47},
doi={10.1109/MIC.2013.84},
ISSN={1089-7801},
month={Nov}}
@article{DS-based-IDS-SmartGrid,
author={M. A. Faisal and Z. Aung and J. R. Williams and A. Sanchez},
journal={IEEE Systems Journal},
title={Data-Stream-Based Intrusion Detection System for Advanced Metering Infrastructure in Smart Grid: A Feasibility Study},
year={2015},
volume={9},
number={1},
pages={31-44},
doi={10.1109/JSYST.2013.2294120},
ISSN={1932-8184},
month={March}}
@article{Fault-tolerance-disaster,
AUTHOR={Furquim,Gustavo and Filho,Geraldo P. R. and Jalali,Roozbeh and Pessin,Gustavo and Pazzi,Richard W. and Ueyama,Jó},
TITLE={How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT,ML and Real Data},
JOURNAL={Sensors},
VOLUME={18},
YEAR={2018},
NUMBER={3},
ARTICLENUMBER={907},
ISSN={1424-8220},
DOI={10.3390/s18030907}}
@article{Adat2017,
abstract={Internet technology is very pervasive today. The number of devices connected to the Internet,those with a digital identity,is increasing day by day. With the developments in the technology,Internet of Things (IoT) become important part of human life. However,it is not well defined and secure. Now,various security issues are considered as major problem for a full-fledged IoT environment. There exists a lot of security challenges with the proposed architectures and the technologies which make the backbone of the Internet of Things. Some efficient and promising security mechanisms have been developed to secure the IoT environment,however,there is a lot to do. The challenges are ever increasing and the solutions have to be ever improving. Therefore,aim of this paper is to discuss the history,background,statistics of IoT and security based analysis of IoT architecture. In addition,we will provide taxonomy of security challenges in IoT environment and taxonomy of various defense mechanisms. We conclude our paper discussing various research challenges that still exist in the literature,which provides better understanding of the problem,current solution space,and future research directions to defend IoT against different attacks.},
annote={ISSN=10184864},
author={Adat,Vipindev and Gupta,B. B.},
doi={10.1007/s11235-017-0345-9},
issn={15729451},
journal={Telecommunication Systems},
keywords={Computer architecture,Computer security,Denial of service attacks,Distributed denial of service,Internet of Things,Internet of things,Intrusion detection systems,Intrusion detection systems (Computer security),Security challenges},
number={3},
pages={423--441},
title={{Security in Internet of Things: issues,challenges,taxonomy,and architecture}},
volume={67},
year={2017}
}
@article{GT-BIS,
author="Campiolo,Rodrigo AND Batista,Daniel M.",
title="Uma Arquitetura para Detecção de Ameaças Cibernéticas Baseada na Análise de Grandes Volumes de Dados",
journal="WSCDC SBRC",
year="2018"
}
@book{Marz:lambda,
author={Marz,Nathan and Warren,James},
title={Big Data: Principles and Best Practices of Scalable Realtime Data Systems},
year={2015},
isbn={1617290343,9781617290343},
edition={1st},
publisher={Manning Publications Co.},
address={Greenwich,CT,USA},
}
@misc{Kappa,
author={Kreps,Jay},
title={Questioning the Lambda Architecture},
year={2014},
url={https://www.oreilly.com/ideas/questioning-the-lambda-architecture}
}
@inproceedings{Liquid,
title={Liquid: Unifying Nearline and Offline Big Data Integration},
author={Raul Castro Fernandez and Peter R. Pietzuch and Jay Kreps and Neha Narkhede and Jun Rao and Joel Koshy and Dong Lin and Chris Riccomini and Guozhang Wang},
booktitle={CIDR},
year={2015}
}
@article{Spinosa2009ollinda,
author={Spinosa,Eduardo J. and de Leon F. de Carvalho,Andr\'{e} Ponce and Gama,Jo\~{a}o},
title={Novelty Detection with Application to Data Streams},
year={2009},
issue_date={August 2009},
publisher={IOS Press},
address={NLD},
volume={13},
number={3},
issn={1088-467X},
journal={Intell. Data Anal.},
month=aug,
pages={405–422},
numpages={18},
keywords={unsupervised learning,k-means,clustering,Novelty detection}
}
@article{Masud2010ECSMiner,
author={M. Masud and J. Gao and L. Khan and J. Han and B. M. Thuraisingham},
journal={IEEE Trans. on Knowledge and Data Engineering},
title={Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints},
year={2011},
volume={23},
number={6},
pages={859-874},
doi={10.1109/TKDE.2010.61},
ISSN={1041-4347},
month={June},
publisher={IEEE},
abstract={Most existing data stream classification techniques ignore one
important aspect of stream data: arrival of a novel class. We address this issue
and propose a data stream classification technique that integrates a novel class
detection mechanism into traditional classifiers,enabling automatic detection
of novel classes before the true labels of the novel class instances arrive.
Novel class detection problem becomes more challenging in the presence of
concept-drift,when the underlying data distributions evolve in streams. In
order to determine whether an instance belongs to a novel class,the
classification model sometimes needs to wait for more test instances to discover
similarities among those instances. A maximum allowable wait time Tc is imposed
as a time constraint to classify a test instance. Furthermore,most existing
stream classification approaches assume that the true label of a data point can
be accessed immediately after the data point is classified. In reality,a time
delay Tl is involved in obtaining the true label of a data point since manual
labeling is time consuming. We show how to make fast and correct classification
decisions under these constraints and apply them to real benchmark data.
Comparison with state-of-the-art stream classification techniques prove the
superiority of our approach. {\textcopyright} 2011 IEEE.}
}
@InCollection{Gama2007,
author="Gama, Jo{\~a}o and Rodrigues, Pedro Pereira",
editor="Gama, Jo{\~a}o and Gaber, Mohamed Medhat",
title="Data Stream Processing",
bookTitle="Learning from Data Streams: Processing Techniques in Sensor Networks",
year="2007",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="25--39",
isbn="978-3-540-73679-0",
doi="10.1007/3-540-73679-4_3",
url="https://doi.org/10.1007/3-540-73679-4_3"
}
@book{Gama2010,
author="Gama,Jo{\~a}o and Rodrigues,Pedro Pereira",
title="Knowledge Discovery from Data Streams",
year="2010",
editor="Gama,Jo{\~a}o and Gaber,Mohamed Medhat",
publisher={Chapman and Hall/CRC},
ISBN="9781439826119"
}
@article{Mukkamala2005,
author={Mukkamala,Srinivas and Sung,Andrew and Abraham,Ajith},
title={Cyber Security Challenges: Designing Efficient Intrusion Detection Systems and Antivirus Tools},
year={2005},
month={01},
pages={}
}
@inproceedings{Faria2013Minas,
author = {Faria, Elaine R. and Gama, Jo\~{a}o and Carvalho, Andr\'{e} C. P. L. F.},
title = {Novelty Detection Algorithm for Data Streams Multi-Class Problems},
year = {2013},
isbn = {9781450316569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2480362.2480515},
doi = {10.1145/2480362.2480515},
abstract = {Novelty detection has been presented in the literature as
one-class problem. In this case, new examples are classified as either
belonging to the target class or not. The examples not explained by the model
are detected as belonging to a class named novelty. However, novelty detection
is much more general, especially in data streams scenarios, where the number
of classes might be unknown before learning and new classes can appear any
time. In this case, the novelty concept is composed by different classes. This
work presents a new algorithm to address novelty detection in data streams
multi-class problems, the MINAS algorithm. Moreover, we also present a new
experimental methodology to evaluate novelty detection methods in multi-class
problems. The data used in the experiments include artificial and real data
sets. Experimental results show that MINAS is able to discover novelties in
multi-class problems.},
booktitle = {Proceedings of the 28th Annual ACM Symposium on Applied Computing},
pages = {795–800},
numpages = {6},
keywords = {data stream, multi-class, novelty detection, clustering},
location = {Coimbra, Portugal},
series = {SAC '13}
}
@manual{Faria2013source,
author = {Faria, Elaine R},
title = {{Source codes of MINAS: MultI-class learNing Algorithm for data Streams}},
address = {Universidade Federal de Uberlândia, Faculdade de Computação, Uberlândia, Minas Gerais, Brasil},
month = {aug},
url = {http://www.facom.ufu.br/~elaine/DadosMINAS/MINAS-SourceCode.rar},
year = {2013}
}
@inproceedings{Faria2013evaluation,
author = {Faria, Elaine R. and Gonçalves, Isabel J.C.R. and Gama, João and Carvalho, Andre C.P.L.F.},
title = {Evaluation Methodology for Multiclass Novelty Detection Algorithms},
booktitle = {2013 Brazilian Conference on Intelligent Systems},
doi={10.1109/BRACIS.2013.12},
isbn={9780769550923},
journal={Proceedings - 2013 Brazilian Conference on Intelligent Systems,BRACIS 2013},
pages={19--25},
abstract={Novelty detection is a useful ability for learning systems,
especially in data stream scenarios,where new concepts can appear,known
concepts can disappear and concepts can evolve over time. There are several
studies in the literature investigating the use of machine learning
classification techniques for novelty detection in data streams. However,
there is no consensus regarding how to evaluate the performance of these
techniques,particular for multiclass problems. In this study,we propose a
new evaluation approach for multiclass data streams novelty detection
problems. This approach is able to deal with: i) multiclass problems,ii)
confusion matrix with a column representing the unknown examples,iii)
confusion matrix that increases over time,iv) unsupervised learning,that
generates novelties without an association with the problem classes and v)
representation of the evaluation measures over time. We evaluate the
performance of the proposed approach by known novelty detection algorithms
with artificial and real data sets. {\textcopyright} 2013 IEEE.},
year={2013}
}
@Article{Faria2016minas,
author={de Faria, Elaine Ribeiro
and Ponce de Leon Ferreira Carvalho, Andr{\'e} Carlos
and Gama, Jo{\~a}o},
title={MINAS: multiclass learning algorithm for novelty detection in data streams},
journal={Data Mining and Knowledge Discovery},
year={2016},
month={May},
day={01},
volume={30},
number={3},
pages={640-680},
abstract={Data stream mining is an emergent research area that aims at
extracting knowledge from large amounts of continuously generated data.
Novelty detection (ND) is a classification task that assesses if one or a set
of examples differ significantly from the previously seen examples. This is an
important task for data stream, as new concepts may appear, disappear or
evolve over time. Most of the works found in the ND literature presents it as
a binary classification task. In several data stream real life problems, ND
must be treated as a multiclass task, in which, the known concept is composed
by one or more classes and different new classes may appear. This work
proposes MINAS, an algorithm for ND in data streams. MINAS deals with ND as a
multiclass task. In the initial training phase, MINAS builds a decision model
based on a labeled data set. In the online phase, new examples are classified
using this model, or marked as unknown. Groups of unknown examples can be used
later to create valid novelty patterns (NP), which are added to the current
model. The decision model is updated as new data come over the stream in order
to reflect changes in the known classes and allow the addition of NP. This
work also presents a set of experiments carried out comparing MINAS and the
main novelty detection algorithms found in the literature, using artificial
and real data sets. The experimental results show the potential of the
proposed algorithm.},
issn={1573-756X},
doi={10.1007/s10618-015-0433-y},
url={https://doi.org/10.1007/s10618-015-0433-y}
}
@Article{Faria2016ndds,
author={Faria, Elaine R.
and Gon{\c{c}}alves, Isabel J. C. R.
and de Carvalho, Andr{\'e} C. P. L. F.
and Gama, Jo{\~a}o},
title={Novelty detection in data streams},
journal={Artificial Intelligence Review},
year={2016},
month={Feb},
day={01},
volume={45},
number={2},
pages={235-269},
issn={1573-7462},
doi={10.1007/s10462-015-9444-8},
url={https://doi.org/10.1007/s10462-015-9444-8}
}
@article{DeFaria2015evaluation,
title = {Evaluation of Multiclass Novelty Detection Algorithms for Data Streams},
author = {de Faria, Elaine Ribeiro and Gonçalves, Isabel Ribeiro and Gama, Joao
and Carvalho, Andre Carlos Ponce de Leon Ferreira},
abstract = {{\textcopyright} 2015 IEEE. Data stream mining is an emergent
research area that investigates knowledge extraction from large amounts of
continuously generated data, produced by non-stationary distribution. Novelty
detection, the ability to identify new or previously unknown situations, is a
useful ability for learning systems, especially when dealing with data
streams, where concepts may appear, disappear, or evolve over time. There are
several studies currently investigating the application of novelty detection
techniques in data streams. However, there is no consensus regarding how to
evaluate the performance of these techniques. In this study, we propose a new
evaluation methodology for multiclass novelty detection in data streams able
to deal with: i) unsupervised learning, which generates novelty patterns
without an association with the true classes, where one class may be composed
of a novelty set, ii) confusion matrix that increases over time, iii)
confusion matrix with a column representing unknown examples, i.e., those not
explained by the model, and iv) representation of the evaluation measures over
time. We propose a new methodology to associate the novelty patterns detected
by the algorithm, in an unsupervised fashion, with the true classes. Finally,
we evaluate the performance of the proposed methodology through the use of
known novelty detection algorithms with artificial and real data sets.},
journal = {IEEE Transactions on Knowledge and Data Engineering},
year = {2015},
volume = {27},
number = {11},
pages = {2961-2973},
doi = {10.1109/TKDE.2015.2441713},
issn = {1041-4347},
month = {nov},
url = {http://ieeexplore.ieee.org/document/7118190/}
}
@article{Silva2013,
author={Silva,Jonathan A. and Faria,Elaine R. and Barros,Rodrigo C. and Hruschka,Eduardo R. and Carvalho,Andr\'{e} C. P. L. F. de and Gama,Jo\~{a}o},
title={Data Stream Clustering: A Survey},
year={2013},
issue_date={October 2013},
publisher={Association for Computing Machinery},
address={New York,NY,USA},
volume={46},
number={1},
issn={0360-0300},
url={https://doi.org/10.1145/2522968.2522981},
doi={10.1145/2522968.2522981},
journal={ACM Comput. Surv.},
month=jul,
articleno={Article 13},
numpages={31},
keywords={online clustering,Data stream clustering}
}
@incollection{NIST2011,
author={Mell,Peter and Grance,Timothy},
booktitle={Public Cloud Computing: Security and Privacy Guidelines},
publisher={National Institute of Standards and Technology},
isbn={9781620819821},
mendeley-groups={Conceitos},
organization={National Institute of Standards and Technology},
pages={97--101},
title={{The NIST definition of cloud computing: Recommendations of the National Institute of Standards and Technology}},
url={http://faculty.winthrop.edu/domanm/csci411/Handouts/NIST.pdf},
year={2012}
}
@article{Shi2016,
title={Edge Computing: Vision and Challenges},
author={Shi,Weisong and Cao,Jie and Zhang,Quan and Li,Youhuizi and Xu,Lanyu},
doi={10.1109/JIOT.2016.2579198},
issn={23274662},
journal={IEEE Internet of Things Journal},
keywords={Edge computing,Internet of Things (IoT),smart home and city},
mendeley-groups={Conceitos/sbrc-refs},
month={oct},
number={5},
pages={637--646},
publisher={Institute of Electrical and Electronics Engineers Inc.},
url={https://ieeexplore.ieee.org/abstract/document/7488250},
volume={3},
abstract={The proliferation of Internet of Things (IoT) and the success of
rich cloud services have pushed the horizon of a new computing paradigm,edge
computing,which calls for processing the data at the edge of the network. Edge
computing has the potential to address the concerns of response time
requirement,battery life constraint,bandwidth cost saving,as well as data
safety and privacy. In this paper,we introduce the definition of edge
computing,followed by several case studies,ranging from cloud offloading to
smart home and city,as well as collaborative edge to materialize the concept of
edge computing. Finally,we present several challenges and opportunities in the
field of edge computing,and hope this paper will gain attention from the
community and inspire more research in this direction.},
year={2016}
}
@book{IEEECommunicationsSociety2018,
title={IEEE Std 1934-2018: IEEE Standard for Adoption of OpenFog Reference Architecture for Fog Computing.},
author={{IEEE Communications Society}},
abstract={OpenFog Consortium--OpenFog Reference Architecture for Fog Computing
is adopted by this standard. OpenFog Reference Architecture [OPFRA001.020817] is
a structural and functional prescription of an open,interoperable,horizontal
system architecture for distributing computing,storage,control and networking
functions closer to the users along a cloud-to-thing continuum of communicating,
computing,sensing and actuating entities. It encompasses various approaches to
disperse Information Technology (IT),Communication Technology (CT) and
Operational Technology (OT) Services through information messaging
infrastructure as well as legacy and emerging multi-access networking
technologies},
keywords={Communication technology,Computer architecture,Edge computing,IEEE
Standards,Information technology,OpenFog,adoption,communication technology
IEEE 1934™,information technology,operational technology},
booktitle={IEEE P1934/D2.0,April 2018},
isbn={9781504450171},
pages={176},
publisher={IEEE},
url={https://ieeexplore.ieee.org/document/8423800},
year={2018}
}
@inproceedings{Bonomi2012,
title={Fog computing and its role in the internet of things},
author={Bonomi,Flavio and Milito,Rodolfo and Zhu,Jiang and Addepalli,Sateesh},
booktitle={Proceedings of the first edition of the MCC workshop on Mobile cloud computing},
abstract={Fog Computing extends the Cloud Computing paradigm to the edge of
the network,thus enabling a new breed of applications and services. Defining
characteristics of the Fog are: a) Low latency and location awareness; b)
Widespread geographical distribution; c) Mobility; d) Very large number of
nodes,e) Predominant role of wireless access,f) Strong presence of streaming
and real time applications,g) Het-erogeneity. In this paper we argue that the
above characteristics make the Fog the appropriate platform for a number of
critical Internet of Things (IoT) services and applications ,namely,Connected
Vehicle,Smart Grid ,Smart Cities,and,in general,Wireless Sensors and Actuators
Networks (WSANs).},
isbn={9781450315197},
pages={13--16},
keywords={Analytics,C2 [Computer-Communication Networks]: C24 Com-pute,Cloud
Computing,IoT,Real Time Systems,Software Defined Networks,WSAN},
mendeley-groups={Conceitos/sbrc-refs},
url={http://www.lispmob.org},
year={2012}
}
@inproceedings{Babcock2002,
author={Babcock,Brian and Babu,Shivnath and Datar,Mayur and Motwani,Rajeev and Widom,Jennifer},
title={Models and Issues in Data Stream Systems},
year={2002},
isbn={1581135076},
publisher={Association for Computing Machinery},
address={New York,NY,USA},
url={https://doi.org/10.1145/543613.543615},
doi={10.1145/543613.543615},
booktitle={Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems},
pages={1–16},
numpages={16},
location={Madison,Wisconsin},
series={PODS ’02}
}
@article{Stonebraker2005,
title={The 8 requirements of real-time stream processing},
author={Stonebraker,Michael and çetintemel,Uǧur and Zdonik,Stan},
abstract={Applications that require real-time processing of high-volume data steams are pushing the limits of traditional data processing infrastructures. These stream-based applications include market feed processing and electronic trading on Wall Street,network and infrastructure monitoring,fraud detection,and command and control in military environments. Furthermore,as the "sea change" caused by cheap micro-sensor technology takes hold,we expect to see everything of material significance on the planet get "sensor- tagged" and report its state or location in real time. This sensorization of the real world will lead to a "green field" of novel monitoring and control applications with high-volume and low-latency processing requirements. Recently,several technologies have emerged - including off-the-shelf stream processing engines - specifically to address the challenges of processing high-volume,real-time data without requiring the use of custom code. At the same time,some existing software technologies,such as main memory DBMSs and rule engines,are also being "repurposed" by marketing departments to address these applications. In this paper,we outline eight requirements that a system software should meet to excel at a variety of real-time stream processing applications. Our goal is to provide high-level guidance to information technologists so that they will know what to look for when evaluation alternative stream processing solutions. As such,this paper serves a purpose comparable to the requirements papers in relational DBMSs and on-line analytical processing. We also briefly review alternative system software technologies in the context of our requirements. The paper attempts to be vendor neutral,so no specific commercial products are mentioned.},
journal={SIGMOD Record},
volume={34},
number={4},
pages={42--47},
doi={10.1145/1107499.1107504},
url={https://doi.org/10.1145/1107499.1107504},
issn={01635808},
mendeley-groups={minas/sbrc-2019-refs},
month={dec},
year={2005}
}
@book{marz2015big,
title={Big Data: Principles and best practices of scalable real-time data systems},
author={Marz,Nathan and Warren,James},
year={2015},
publisher={New York; Manning Publications Co.}
}
@article{Gaber2005,
title={Mining Data Streams: A Review},
author={Gaber,Mohamed Medhat and Zaslavsky,Arkady and Krishnaswamy,Shonali},
year={2005},
issue_date={June 2005},
publisher={Association for Computing Machinery},
address={New York,NY,USA},
volume={34},
number={2},
issn={0163-5808},
url={https://doi.org/10.1145/1083784.1083789},
doi={10.1145/1083784.1083789},
journal={SIGMOD Rec.},
month={jun},
pages={18–26},
numpages={9}
}
@Inbook{Almusallam2017,
title="Dimensionality Reduction for Intrusion Detection Systems in Multi-data Streams---A Review and Proposal of Unsupervised Feature Selection Scheme",
bookTitle="Emergent Computation : A Festschrift for Selim G. Akl",
author="Almusallam,Naif Y. and Tari,Zahir and Bertok,Peter and Zomaya,Albert Y.",
editor="Adamatzky,Andrew",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="467--487",
abstract="An Intrusion Detection System (IDS)Intrusion Detection Systemis a security mechanism that is intended to dynamically inspect traffic in order to detect any suspicious behaviour or launched attacks. However,it is a challenging task to apply IDS for large and high dimensional data streams. Data streams have characteristics that are quite distinct from those of statistical databases,which greatly impact on the performance of the anomaly-based ID algorithms used in the detection process. These characteristics include,but are not limited to,the processing of large data as they arrive (real-time),the dynamic nature of data streams,the curse of dimensionality,limited memory capacity and high complexity. Therefore,the main challenge in this area of research is to design efficient data-driven ID systems that are capable of efficiently dealing with data streams by considering these specific traffic characteristics. This chapter provides an overview of some of the relevant work carried out in three major fields related to the topic,namely feature selections (FS),intrusion detection systems (IDS) and anomaly detectionAnomaly detection in multi data streams. This overview is intended to provide the reader with a better understanding of the major recent works in the area. By critically investigating and combining those three fields,researchers and practitioners will be better able to develop efficient and robust IDS for data streams. At the end of this chapter,we provide two basic models: an Unsupervised Feature Selection to Improve Detection Accuracy for Anomaly Detection (UFSAD) and its extension (UFSAD-MS) for multi streams,that could reduce the volume and the dimensionality of the big data resulting from the streams. The reduction is based on the selection of only the relevant features and removing irrelevant and redundant ones. The last section of the chapter provides an example of the developed UFSAD model,followed by some experimental results. UFSAD-MS is provided as a conceptual model as it is in the implementation phase.",
isbn="978-3-319-46376-6",
doi="10.1007/978-3-319-46376-6_22",
url="https://doi.org/10.1007/978-3-319-46376-6_22"
}
@misc{Kreps2014,
title={{Questioning the Lambda Architecture – O'Reilly}},
author={Kreps,Jay},
keywords={AI {\&} ML,Data},
mendeley-groups={minas/cassales},
pages={10},
url={https://www.oreilly.com/radar/questioning-the-lambda-architecture/},
urldate={2020-02-10},
year={2014}
}
@article{lin2017lambda,
title={The lambda and the kappa},
author={Lin,Jimmy},
journal={IEEE Internet Computing},
number={5},
pages={60--66},
year={2017},
publisher={IEEE}
}
@inproceedings{Kambourakis2017,
title={{The Mirai botnet and the IoT Zombie Armies}},
author={Kambourakis,Georgios and Kolias,Constantinos and Stavrou,Angelos},
url={http://ieeexplore.ieee.org/document/8170867/},
booktitle={MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)},
abstract={{\textcopyright} 2017 IEEE. The rapidly growing presence of Internet of Things (IoT) devices is becoming a continuously alluring playground for malicious actors who try to harness their vast numbers and diverse locations. One of their primary goals is to assemble botnets that can serve their nefarious purposes,ranging from Denial of Service (DoS) to spam and advertisement fraud. The most recent example that highlights the severity of the problem is the Mirai family of malware,which is accountable for a plethora of massive DDoS attacks of unprecedented volume and diversity. The aim of this paper is to offer a comprehensive state-of-the-art review of the IoT botnet landscape and the underlying reasons of its success with a particular focus on Mirai and major similar worms. To this end,we provide extensive details on the internal workings of IoT malware,examine their interrelationships,and elaborate on the possible strategies for defending against them.},
doi={10.1109/MILCOM.2017.8170867},
isbn={978-1-5386-0595-0},
keywords={Botnet,DDoS,Hajime,IoT,Mirai,Network Security},
mendeley-groups={minas/generic},
month={oct},
pages={267--272},
publisher={IEEE},
volume={2017-Octob},
year={2017}
}
@misc{mawiSamplepointF,
title={{Index of /mawi/samplepoint-F}},
author={{MAWI Working Group Traffic Archive}},
year={2020},
url={http://mawi.wide.ad.jp/mawi/samplepoint-F/},
urldate={2020-02-11}
}
@inproceedings{Fontugne2010,
author={Fontugne,Romain and Borgnat,Pierre and Abry,Patrice and Fukuda,Kensuke},
title={MAWILab: Combining Diverse Anomaly Detectors for Automated Anomaly Labeling and Performance Benchmarking},
booktitle={ACM CoNEXT '10},
month={December},
year={2010},