CONTENTS
1.
2.
3.
4.
5.
6.
7.
INTRODUCTION ……………………………………………………………………………………………….1-2
LITERATURE REVIEW ………………………………………………………………………………………….3
PROPOSED MODEL……………………………...…………………………………………………………….3-5
COMPARISON WITH OTHER EXISTIG MODELS…………………………………………………………….6
FUTURE SCOPE AND RECOMMENDATION………………………………………………………………….7
CONCLUSION……………………………………………………………………………….…………………….7
BIBLIOGRAPHY……………………………………………………………………………….………………….8
ABSTRACT
TheoInternetoofoThingso(IoT)ohasoemergedoasoaoforefrontotechnology,ocaptivatingotheoattenti
on ofotheoresearchocommunityoandoprovingoeconomicallyolucrative for businesses. It
revolves aroundoconnectingonumerousodevicesoandoestablishingolinksobetween devices and
humans. Toofacilitateotheoexchangeoandoprocessingoofodata in IoT, a cloud computing
environment is essential.oSimultaneously,oartificialointelligenceo(AI)oisorequiredoto analyse
theodataostoredoinotheocloud infrastructure swiftly and reliably, enabling intelligent decisionmaking.
The communication among
interconnectedoIoTodevicesoreliesoonouniqueoidentifiersoandoembeddedosensors,outilizing the
internet and cloud-based network infrastructure for information exchange [1]. In theocurrent
eraoof big data, where the rapid and accurate analysis of cloud-based data is imperative, the
application of AI and machine learning (ML) has become critical. Despite the significant role
of AI in enhancing traditional cybersecurity, challenges persist in the form of vulnerabilities in
the cloud and theonetworkingoof IoT devices. Additionally, AI is increasingly utilized by
hackers, posing an ongoing threat to cybersecurity worldwide.
Furthermore, wirelessly accessed IoT devices deployed on public networks face constant
cyber threats. This research paperoproposesoa solution in the form of a hybrid detection
model that leveragesoartificial intelligenceoand machineolearningo(AI/ML). The aim is to
effectively address and mitigate IoT cyber threats in cloud computing environments,
addressing both host-based and network-level vulnerabilities.
1. INTRODUCTION
The concept of theo"Internet of Things" (IoT) was formally introduced by the International
TelecommunicationoUnion during the World Summitoon the Information Society (WSIS) in
2005 [1]. IoT encompasses a distributed networkothat integrates various sensor devices and
systems, includingosensor networks,oRFID devices, barcodeoandoQR code devices, and
global positioning systems,owithotheoInternetothrough both wired and wireless
communicationotechnologies.oThisoenablesoembeddedosystems to communicate
andointerconnect.
Theoevolution of IoT has followed three primary technical routes:
•
Development of Sensing,oIdentification, and Authentication Technologies: These
technologies serve as the foundation of IoT, with sensors being crucial components.
Sensors, acting as theonerveoendingsoofoIoT,oformotheolargest and fundamental part
of the IoT chain. General-purposeosensorodevicesohave become widespread, and
high-endosensorodevicesoinospecificofieldsohaveoalso seen significant progress.
•
DevelopmentoofoTransmissionoand Communication Technologies: These
technologiesoact as the backbone of IoT, ensuring theotransmission and aggregation
of theovastoamountoofoinformationocollectedobyoIoT devices. The progress in wired
and wireless networks, cellular networks, and other transmission and communication
technologies facilitates large-scale data transmission in IoT.
•
Developmentoof DataoComputingoandoProcessing Technologies: These technologies
areoessential for providing applicationsoandoservicesousing IoT data. Real-time
perceptionoandointelligentofeedbackofor a multitude of information nodes in IoT
applications require advancements in data computing and processing technologies.
Technologies like artificial intelligence and cloud computing contribute to enhancing
processing intelligence and effectiveness in IoT.
As technologies advance and applications expand, IoT has transformed into aoset of
solutions tailoredoforospecific applications. It has become a novel approach, tool, and
method for social governance by integrating andoinnovatingosolutions, connecting the
Internet with theophysicaloworld,oandoenabling intelligent interactions. IoT
applicationsospanovariousofields such as manufacturing, energyomanagemento(e.g.,osmart
grids),ourbanolifeo(e.g., smart cities),oandopersonal healthcare.
FIGURE 1.
o
Three-tier architecture of IoT.
o
o
Following traditional architecture and ISO/IEC 30141:2018 "IoT Reference Architecture," IoT t
ypically adopts an entity based architecture with three layers: the terminal perception layer,
network transport layer, and application service layer. The terminal perception layer collects
IoT data, the network transport layer facilitates communication, and the application service
layer processes and provides various applications and services. This architecture illustrates
the interactive relationships among the entities involved in IoT.
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
2. LITERATURE REVIEW
The realm of computer science encounters significant challenges with the advent of the
Internet of Things (IoT), presenting a novel concept for interconnected devices and smart
data. Key challenges for researchers in the IoT domain revolve around the preparation
and processing of data [9]. In this context, a comprehensive study proposed four data
mining models to address these challenges. The first model adopts a multi-layer
approach, incorporating layers for data collection, management, event processing, and
data mining services. The second model focuses on distributed data mining, particularly
for data deposition across different sites. The third model introduces a grid-based
approach, aiming tooimplementoheterogeneous,olarge-scale,oand high-performance
applications. Theofourthomodeloprovidesoaomulti-technology integration perspective,
outliningoaoframeworkofor the future Internet [9].
Another pertinent study by reference [10] delves into the warehousing of Radio
Frequency Identification (RFID) data, emphasizing the management andomining of RFID
streamodata.oReferenceo[11]ocontributes a systematic methodofor reviewing data mining
knowledgeoandotechniquesoacrossovarious applications. The study explores data mining
functionsosuchoasoclassification,oclustering, association analysis, time seriesoanalysis,
andooutlierodetection.oTheoresearchers find similarities between data generatedoby data
miningoapplications,oincludingoe-commerce,oindustry,ohealthcare, city governance, and
IoT data. They then align popular data mining functionalities with specific applications,
determining the most appropriate techniques for processing data in each context [11].
In response to the challenges encountered in preparing and processing IoT data through
data mining techniques, reference [12] conducted a comprehensive survey. The authors
structure their research into three major sections. The first and second sections provide
insights into IoT, the nature of the data, and challenges in the field, including the
establishment of models for mining and mining algorithms specific to IoT. The third
section addresses the potential and open issues in the domain, offering a holistic
perspective on the existing landscape and future directions [12].
3. PROPOSED MODEL
The block diagram in Figure 5 depicts the data sourced from various specialized sensor
outlets, undergoes anonymization, storage, and analysis through Artificial Intelligence
(AI) technology. Based on the analysis outcomes, multiple actions are implemented to
optimize safety and efficiency parameters for the intelligent home application in use.
These distinct stages are essential to ensure a secure, confidential, and intelligent living
environment while efficiently utilizing available resources such as power, water, and gas.
o
o
o
o
o
o
o
o
o
Fig 5: Block diagram of a secure and privacy-preserving in Smart home using IoT
enabled AI
o
o
o
o
Technology
Security Parameters Configuration
o
o
Earlier integration into the intelligent home system, a new IoT device undergoes a crucial
process. The IoT platform incorporates cryptographic algorithms to meet the
confidentiality,ointegrity,oandoencryption criteria of specific devices. The success and
widespreadoadoption of robust IoToarchitectures, serving various applications, hinge on
the trust of prospective users [21]. This trust is critical due to potential damages caused
by the theft or misuse of private information, impacting individuals' physical, financial,
and social lives. Implementing sufficient security measures is essential to address
potential safety risks, playing a vital role in safeguarding smart homes [8,9].
o
o
o
o
o
o
o
o
I.
Corrupted or ForgedoData:
o
o
Thisoincidentocan disrupt the proper functioning of the surveillanceosystem, leading to
incorrectodecisionsobasedoon inaccurate information. Causes include deliberate
suspicious IoT system configurations, comprehensive forged data processing by IoT
computers, and data changes in transitionothroughohackedoIoToGatewaysoin Multi-Hop
Interaction [15,16].
II.
Replay Attacks:
o
This intrusion occurs when IoT devices producing data lack an anti-replay system,
allowingotheouseoofoobsoleteoinformationoandotheomaking of erroneous decisions [10].
III.
IP Spoofing and Identity Usurpation:
o
o
Attacks like identification subversion and IP Spoofing pose threats to IoT devices if
advanced protocols for data origin encryption, such as IPsec, are not in place [6,7].
o
o
4. COMPARISON WITH OTHER EXISTING MODELS
IoT Device Groups in a Smart Home:
This section delineates three major groups of IoT devices used in a smart home setting:
Wearables, IoT devices for tracking equipment, and in-home devices for monitoring
environments. Each system detects variables and transmits data readings regularly or
upon meeting specific standards. The choice of IoT Gateway for data transmission
involves a trust protection system [10].
o
o
o
DataoAggregationoand Relaying:
Onceodataoisocollectedoat the IoT Gateway stage, it is either relayed to its endpoint or
stored by the IoT Gateway before transmitting accumulated values. The decision to
aggregate or relay depends on the target system and the interpret value of the
data.oSpecializedoIoToGatewaysomayobeousedoforoheavy-weightoactivitiesonot performed
by IoT devices [4,5].
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
Cloud-Based Data Analytics:
o
o
The gathered data sent by IoT gateways is stored, processed, and analysed in the cloud.
Various approaches, such as Data Generalization and Differential Privacy, are
employedoforodataoprotection.oTheochallengeoliesoinoanonymizing data while minimizing
identity losses. Anonymized data is forwarded to theoAI-basedodataoanalytics tool,
typicallyothroughoaomachine-learningoalgorithm,ocreatingoaorealisticounderstanding of the
controlled environment (smart home). The analysed data informs optimum measures for
the actuator system, with historical data used to optimize machine-learning model
learning and testing [19,15,16].
o
o
5. FUTURE SCOPE AND RECOMMENDATION
o
ArtificialoIntelligenceo(AI)oandoMachineoLearningo(ML) play a pivotal role in enhancing
cybersecurity, extendingotheiropositiveoimpactotooour daily lives through IoT devices,
smarterohomes,oandointelligentovehicles.oA comprehensive security solution is
considered incomplete without integrating AI and ML features.oSolutionsodeveloped with
AI and ML can effectively identify patterns by detecting similarities among past attacks,
issuing instant warnings when encountering similaropatterns.oNotably,oAI/ML excels in
continuously deciphering userobehavior,oadaptingouseopatterns,oand identifying
irregularities [17].
As part of our researchorecommendations,osecurity experts unanimously advocate for
standardizing datasets to facilitate quick data analysis by ML-based solutions. The scale
ofoouroresearchodatasetoisomeasured in exabytes. Once datasets are well-definedoand
standardized,oML-basedosystemsowillosignificantlyocontributeoto combating cyber threats.
Building on our proposedoresearchosolution,oweosuggestocarefully considering whether to
adoptoanounsupervisedoorosupervisedosolutionobasedoonofeatures extracted from our
dataset.oWhileoAIoandoMLosystemsocanooperateoindependently, a modest human
intervention enhances system balance and effectiveness.
6. CONCLUSION
In conclusion, the evolution of the "Internet of Things" (IoT) has transformed it into a
multifaceted solution set, impacting various aspects of our lives. The three primary technical
routes of development include advancements in Sensing,oIdentification,oand Authentication
Technologies,oTransmissionoandoCommunicationoTechnologies,oandoData Computing and
ProcessingoTechnologies. The literature review emphasizes the challenges in IoT,
particularly the preparation and processing of data. Various data mining models have been
proposed, addressing issues of data collection, management, event processing, and mining
services. The importance of standardizing datasets for machine learning (ML) solutions is
highlighted, enabling efficient data analysis and contributing to combating cyber threats.
The proposed model illustrates a secure and privacy-preserving smart home using IoTenabled AI technology. The security parameters configuration outlines measures to address
potential risks, including corrupted or forged data, replay attacks, and IP spoofing. The
comparison with existing models emphasizes the trust protection system in IoT device
groups, data aggregation and relaying at IoT Gateways, and cloud-based data analytics for
optimum measures and historical data optimization.
o
o
o
o
o
o
o
o
o
o
Lookingotootheofuture,otheointegrationoofoArtificialoIntelligence (AI) and Machine Learning
(ML) emerges as a pivotal aspect in enhancing cybersecurity. Recommendations include
standardizing datasets for ML-based solutions and carefully considering the adoption of
unsupervised or supervised solutions.
7. BIBLIOGRAPHY
▪
Dr. S. Hrushikesava Raju, Dr. Lakshmi Ramani Burra, Dr. Ashok Koujalagi, and
Saiyed Faiayaz Waris, "Tourism Enhancer App: User-Friendliness of a Map with
Relevant Features," IOP Conference Series, Materials Science and Engineering,
981, 2, 10.1088/1757-899X/981/2/022067.
▪
Praveen, S.P., Rao, K.T., Janakiramaiah, B., "Effective Allocation of Resources and
Task Scheduling in Cloud Environment using Social Group Optimization," Arabian
Journal for Science and Engineering, 10.1007/s13369-017-2926-z.
▪
Pravin Kshirsagar, Dr. Sudhir Akojwar, "Classification and Prediction of Epilepsy
using FFBPNN with PSO," IEEE International Conference on Communication
Networks, 2015.
▪
Bakhsh, S.T.; Alghamdi, S.; Alsemmeari, R.A.; Hassan, S., "An adaptive intrusion
detection and prevention system for the Internet of Things," Int. J. Distrib. Sens.
Netw. 2019, 15.
▪
Yassin, W.; Udzir, N.; Muda, Z.; Abdullah, A.; Abdullah, M.T., "A Cloud-based
Intrusion Detection Service framework," Proceedings of the 2012 International
Conference on Cyber Security, Cyber Warfare and Digital Forensic (CyberSec),
Kuala Lumpur, Malaysia, 26–28 June 2012.
▪
Thamilarasu, G.; Chawla, S., "Towards Deep-Learning-Driven Intrusion Detection for
the Internet of Things," Sensors 2019, 19, 1977.
▪
Khan, Z.A.; Herrmann, P., "Recent Advancements in Intrusion Detection Systems for
the Internet of Things," Secure. Commun. Netw. 2019, 2019, 1–19.
▪
Bhamare, D.; Salman, T.; Samaka, M.; Erbad, A.; Jain, R., "Feasibility of Supervised
Machine Learning for Cloud Security," Proceedings of the International Conference
on Information Science and Security, Jaipur, India, 16–20 December 2016.
▪
Pravin Kshirsagar And Sudhir Akojwar- July 2016 "Hybrid Heuristic Optimization For
Benchmark Datasets" International Journal Of Computer Applications 146(7):11-16.
▪
Seitz, L.; Selander, G.; Wahlstroem, E.; Erdtman, S.; Tschofenig, H., "Authentication
and Authorization for Constrained Environments (ACE) using the OAuth 2.0
Framework (ACEOAuth)," Internet Engineering Task Force,-24 sep 2019.
▪
Roman, J. Zhou, and J. Lopez, "On the features and challenges of security and
privacy in distributed internet of things," Computer Networks, 57(10):2266–2279,
2013.
▪
Praveen, S.P., Rao, K.T., Janakiramaiah, B., "Effective Allocation of Resources and
Task Scheduling in Cloud Environment using Social Group Optimization," Arabian
Journal for International Journal of Grid and Distributed Computing, Vol 14, No.1,
(2021), pp., 1257-1275.
▪
Rachapudi, V., Nitish, N., Samaikya, S., Sathvik, U.P., Devi, S.A. (2020).,
"Performance Comparison of applications with and without Web Frameworks,"
International Journal of Advanced Trends in Computer Science and Engineering, 9,
1020-1028, 10.30534/ijatcse/2020/19922020.
▪
Anjali Devi, S., Sapkota, P., Rohit Kumar, K., Pooja, S., Sandeep, M.S., "Comparison
of classification algorithms on twitter data using sentiment analysis," International
Journal of Advanced Trends in Computer Science and Engineering, 2020, 9(5), pp.
8170–8173.
▪
SG Akojwar, P Kshirsagar-2016 "A Novel Probabilistic-PSO Based Learning
Algorithm for Optimization of Neural Networks for Benchmark Problems" - WSEAS
TRANSACTIONS on ELECTRONICS, Volume 7, 2016.
▪
Gutiérrez, E. Theodoridis, G. Mylonas, F. Shi, U. Adeel, L. Diez, D. Amaxilatis, J.
Choque, G. Camprodom, J. McCann, and L. Muñoz, "Co-creating the cities of the
future," Sensors, vol. 16, no. 11, p. 1971, Nov. 2016.
▪
P. Kshirsagar and S. Akojwar, "Classification & Detection of Neurological Disorders
using ICA & AR as Feature Extractor," Int. J. Ser. Eng. Sci. IJSES, vol. 1, no. 1, Jan.
2015.
▪
J. Venkatesh, B. Aksanli, C. S. Chan, A. S. Akyurek, and T. S. Rosing, "Modular and
personalized smart health application design in a smart city environment," IEEE
Internet Things J., vol. 5, no. 2, pp. 614–623, Apr. 2018.
▪
ITU Internet Reports 2005: The Internet of Things, 2005.
▪
C. Hai-ming, "Key Technologies and Applications of Internet of Things," Comput. Sci.,
vol. 36, pp. 1-4, 2010.
▪
C.-L. Zhong, Z. Zhu and R.-G. Huang, "Study on the IOT architecture and gateway
technology," Proc. 14th Int. Symp. Distrib. Comput. Appl. Bus. Eng. Sci. (DCABES),
pp. 196-199, Aug. 2015.
▪
M. Bauer, M. Boussard, N. Bui, J. D. Loof, C. Magerkurth, S. Meissner, et al., "IoT
reference architecture," Enabling Things to Talk, pp. 163-211, 2013.
▪
J. J. Cano, "ISACA JOURNAL," 01 September 2016. [Online]. Available:
https://www.isaca.org/resources/isacajournal/issues/2016/volume-5/cyberattackstheinstability-ofsecurity-and-control-knowledge. [Accessed 04 04 2020].
▪
"Packt," 2020. [Online]. Available: https://hub.packtpub.com/25-datasets-deeplearning-iot/. [Accessed 04 05 2020].
▪
J. Ghanchi, 19 March 2019. [Online]. Available: https://thenewstack.io/thepossibilities-of-ai-and-machinelearning-for-cybersecurity/.