Organization of Information Security in Industrial Internet of Things Systems

Saksonov, Evgeny A.; Leokhin, Yury L.; Azarov, Vladimir N.

doi:10.1109/itqmis.2019.8928442

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…Samsonov et al [19] presented various approaches to provide security in IIoT environments. They employed edge and fog computing technologies, various data transmission technologies, cryptographic techniques, IIoT device protection, and blockchain technology to secure IIoT systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network

et al. 2023

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The Industrial Internet of Things (IIoT) refers to the employment of the Internet of Things in industrial management, where a substantial number of machines and devices are linked and synchronized with the help of software programs and third platforms to improve the overall productivity. The acquisition of the industrial IoT provides benefits that range from automation and optimization to eliminating manual processes and improving overall efficiencies, but security remains to be forethought. The absence of reliable security mechanisms and the magnitude of security features are significant obstacles to enhancing IIoT security. Over the last few years, alarming attacks have been witnessed utilizing the vulnerabilities of the IIoT network devices. Moreover, the attackers can also sink deep into the network by using the relationships amidst the vulnerabilities. Such network security threats cause industries and businesses to suffer financial losses, reputational damage, and theft of important information. This paper proposes an SDN-based framework using machine learning techniques for intrusion detection in an industrial IoT environment. SDN is an approach that enables the network to be centrally and intelligently controlled through software applications. In our framework, the SDN controller employs a machine-learning algorithm to monitor the behavior of industrial IoT devices and networks by analyzing traffic flow data and ultimately determining the flow rules for SDN switches. We use SVM and Decision Tree classification models to analyze our framework’s network intrusion and attack detection performance. The results indicate that the proposed framework can detect attacks in industrial IoT networks and devices with an accuracy of 99.7%.

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Section: Literature Reviewmentioning

confidence: 99%

Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network

et al. 2023

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“…IDSisusedformonitoringthemaliciousattackintheinterconnectednetworkornode.Itactsasthe maliciousactivityisdefinedastheintrusionthatisdestructivetosensornodes.TheIDSsystemcan behardwareorsoftwaretools.IDScanexaminetheuseractionsthatdeterminetheknownattacks andidentifiesmaliciousnetworkbehaviour.Itanalysestheactivityofnodesandnetworkswiththe determinationofvariousintrusionsandalertstheusersafterdetectingtheintrusions.Anintrusion detectionsystem(IDS)isoftenusedtodetectsuspiciousassaultsinanetworkconnectornode.It protectsthenodeornetworksbyactingasalinedefenseagainstintruders (Isbell,et al,2019;Yu& Guo,2019;Yeasmin&Baig,2020).Aninvasionthatleadssensornodestomisbehaveisclassifiedas illicitbehavior.BothhardwareandsoftwarecomponentscanindeedbefoundinanIntrusiondetection systemcanmonitoruseractivitytoidentifyknownrisksandanticipatepotentiallydangerousnetwork connections.Itmonitorsnodeaswellasnetflowtodetectdifferentbreachesandalertscustomers whenbreacheshavebeendiscovered.Asaresult,it'sknownasanalertornetworkingobservation.It preventsthenetworksfrombeingharmedthroughunauthorisedassaultsviageneratingalerts.IAas wellasEAcouldbothbedetectedbytheIntrusiondetectionsystem.Attackernodeinsomekindof alinkednetworkcreateIAs.ThirdpartycandidatescreateEAs,whichhavebeenobtainedthroughan exteriorframework.TheIDStechnologymonitorsnetworkdatatodeterminewhetherthey'refrom illegalorlegitimateindividuals.Observation,inquiry,andalarmarethe3partsofIDS.Thenetworking behaviors,traffic,andassetsareallmonitoredbythemonitoringcomponent.Theresearchisindeedthe majorelementthatusesthedefinedmethodologytoidentifyinvasions.Wheneverintrudershavebeen detected,theWarningcomponenttriggersanalarm.Thereare3typesofIDSs,whicharediscussed beneath (Niedermaier,et al,2019))Host-basedIDSSystem(HIDS):HIDScalculatesdatafroma singleormorehost,suchasarchitecture,systemsoftware,andprogramdocuments.Suchsystem detectsuseractionsandsystemprogrammeoperationsbycollectinginformationfromindividual sourcematerialsandtransferringitovertoaprocessorjustatoperatingnetworklevel.Enhanced recuperation,informativelogs,reducedfalsepositiverate,andunfamiliarattackforecastingwere allbenefitsofthesystem.Indecipherabledata,totalexposure,obliquedata,strangers,andhosting influencearesomeofthedownsides.2)Anomaly-basedIntrusionDetectionSystem(AIDS):Such structureisalsoknownasanomaly-basedvulnerabilityscanning.Thisanalysestheoccurrenceto discovermaliciousactivity.Thisbeginsbydescribingtheattack'stypicalactions.Whenevertheactions differfromwhathasbeenexpected,itisreferredtoasanincursion.3)NIDS(Network-basedIntrusion DetectionSystem):Thismethodrecognizesbreachesviaroutingtrafficvianetworkinterfacecards, gateways,andswitches.Informationisretrievedmostlybystandardnetworkstreamssuchasweb packet.NIDScouldrecognizevariousassaultsintheLocalareanetworkaswellasidentifyintrusions thathost-basedIDScouldn'tidentify.Theeaseofhandling,affordability,detectionlimit,forensic consistency,aswellasidentificationofalleffortsallseemtobebenefitsofNIDS.Degradationat wirespeed,directassaultvulnerability,unintelligiblepackets,andthedifficultyoffullcoveragehave allbeennegatives (Saksonov,et al,2019;AlAbdullatif,et al,2019;Puri,et al,2020).…”

Section: Intrusion Detection System (Ids)mentioning

confidence: 99%

Deep Ensemble Model for Detecting Attacks in Industrial IoT

Behera¹,

Pattanayak²,

Mohanty

2022

International Journal of Information Security and Privacy

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In this research work, a novel IIoT attack detection framework is designed by following four major phases: pre-processing, imbalance processing, feature extraction, and attack detection. The attack detection is carried out using the projected ensemble classification framework. The projected ensemble classification framework encapsulates the recurrent neural network, CNN, and optimized bi-directional long short-term memory (BI-LSTM). The RNN and CNN in the ensemble classification framework is trained with the extracted features. The outcome acquired from RNN and CNN is utilized for training the optimized BI-LSTM model. The final outcome regarding the presence/absence of attacks in the industrial IoT is portrayed by the optimized BI-LSTM model. Therefore, the weight of BI-LSTM model is fine-tuned using the newly projected hybrid optimization model referred as cat mouse updated slime mould algorithm (CMUSMA). The projected hybrids the concepts of both the standard slime mould algorithm (SMA) and cat and mouse-based optimizer(CMBO), respectively.

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“…Digital technologies affect business development, and also act as the main condition for survival in the fierce competition in the cargo transportation market. Thanks to them, it is possible to offer a more competitive product, not only making it more convenient for the consumer, but also reducing its cost [8].…”

Section: Fig 1 Digital Technologies At Russian Railwaysmentioning

confidence: 99%

Digital technologies in quality and efficiency management of transport service

Vasilenko

Kuzina

Bespalov³

et al. 2021

E3S Web Conf.

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The digital transformation of transport systems is one of the priority areas of the country’s strategic development, and requires joint participation and interaction of the state, transport industry enterprises, innovative companies - developers of new technologies and equipment. Constant price pressures and growing customer demands for product and service quality complicate the challenges faced by rail organizations. All these organizations, and first of all JSC Russian Railways, within the framework of the ongoing structural reform, cannot increase the efficiency of their activities using the traditional set of technological, organizational and management solutions to reduce costs for all business processes and divisions of the company. The current state of the Russian economy already allows us to note positive shifts in the field of digitalization. The digitalization of transport as one of the social sectors, the use of modern technical means and programs, makes it possible to increase the efficiency of the transport complex, to ensure high quality services. The authors considered digital technologies in JSC “Russian Railways” used to increase flexibility, efficiency and quality of service provision. The key electronic platforms used by Russian Railways in its work are presented. Disclosed telematic systems used in railway transport to improve safety. Methods are proposed for improving the quality of passenger service at railway stations in the Russian Federation.

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Organization of Information Security in Industrial Internet of Things Systems

Cited by 9 publications

References 6 publications

Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network

Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network

Deep Ensemble Model for Detecting Attacks in Industrial IoT

Digital technologies in quality and efficiency management of transport service

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