Anomaly detection and privacy preservation inCloud-Centric Internet of Things

Ismail ButunDepartment of Mechatronics Engineering

Bursa Technical UniversityBursa, TURKEY

e–mail: ismail.butun@btu.edu.tr

Burak KantarciDepartment of Electrical & Computer Engineering

Clarkson UniversityPotsdam, NY, USA

e–mail: bkantarc@clarkson.edu

Melike Erol-KantarciDepartment of Electrical & Computer Engineering

Clarkson UniversityPotsdam, NY, USA

Email: merolkan@clarkson.edu

Abstract—Internet of Things (IoT) concept provides a numberof opportunities to improve our daily lives while also creatinga potential risk of increasing the vulnerability of personalinformation to security and privacy breaches. Data collectedfrom IoT is usually offloaded to the Cloud which may furtherleave data prone to a variety of attacks if security and privacyissues are not handled properly. Anomaly detection has been oneof the widely adopted security measures in wired and wirelessnetworks. However, it is not straight forward to apply mostof the anomaly detection techniques to IoT and cloud. Oneof the main challenges is deriving outlier features from thevast volume of data pumped from IoT to the cloud. Otherchallenges include the large number of sources generating data,heterogenous connectivity and traffic patterns of IoT devices,cloud services being offered at geographically remote places andcausing IoT data to be stored in different countries with differentlegislations. This paper, for the first time, presents the challengesand opportunities in anomaly detection for IoT and cloud. Itfirst introduces the prominent features and application fieldsof IoT and Cloud, then discusses security and privacy risksto personal information and finally focuses on solutions fromanomaly detection perspective.

I. INTRODUCTION

Internet of Things (IoT) describes the next generation ofInternet, where the physical things or objects are connected,accessed and identified through the Internet [1]. Many yearsof research and development in wireless sensor technologieshave significantly extended the sensing capabilities of devices,and therefore paved the way the concept of IoT which is nowextending to ambient intelligence and autonomous control. Todate, a number of technologies are involved in IoT, such aswireless sensor networks (WSNs), intelligent sensing, remotesensing, Radio Frequency Identification (RFID), Near FieldCommunications (NFC), low energy wireless communications,cloud computing, and so on. IoT find unique applications inhealth systems, smart homes and environments, smart grid andtransportation [2].

The nexus of IoT and cloud brings in various challengeswhere the fundamental challenge comes from the volume,

variety, velocity, veracity and value of the data produced byIoT devices and offloaded to cloud. These properties of IoTdata corresponds to the 5V model of big data where variety ofdata types, velocity of data production and processing speed,volume of data size, veracity of data reliability and trust andvalue of the resulting worth derived from processing the datarefers to the nature of IoT data. The difficulties with storingand processing big data on individual devices led to offloadingdata to cloud servers which offer on-demand, elastic, scalable,pay-per use self-service models. Hence, cloud computingprovides tools and platforms for storing and processing bigIoT data. One of the well-known programming models toprocess large amounts of data on Cloud is MapReduce. ApacheHadoopTM is an open source MapReduce implementationand basically a framework that allows for the distributedprocessing of large data sets across clusters of computers usingsimple programming models. It is designed to scale up fromsingle servers to thousands of machines, each offering localcomputation and storage. These platforms have raised serioussecurity concerns in the past. According to a recent whitepaper by Zettaset Inc. [3], such cloud platforms introduce vul-nerability to personal and corporate information. Distributedcomputing approaches allow data to be processed anywherewhere resources are available. Furthermore, multiple copies orfragments of data may be processed at different servers addingcomplexity to security [3]. Adding to those, when web servicemodels and unsecured communication channels are adopted,security risks significantly grow.

Anomaly detection is a widely used security measure inwired and wireless networks that allow finding anomalouspatterns that do not conform with the anticipated behaviourof a system or data. The fundamental challenges of anomalydetection in IoT are: (i) the difficulty of defining anticipatedbehavior because data may be distorted with sensing inac-curacies, environmental affects, noise, etc, (ii) IoT data andits behavior is application dependent, (iii) IoT devices andtheir traffic are heterogenous, therefore hard to derive patterns,

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(iv) IoT devices can be mobile and generate application andenvironment dependent data [4]. In this paper, we provide aroadmap for anomaly detection schemes for IoT and cloud.We first present the anomaly detection techniques for sensingsystems and discuss their applicability to IoT. Then, we focuson security and privacy issues of data stored and processed onthe cloud.

The rest of the paper is organized as follows. In SectionII, we provide a brief survey of anomaly detection techniquesfor sensing systems. We focus on IoT and big data relatedchallenges and potential anomaly detection-based solutions inSection III. Cloud related challenges and opportunities areaddressed in Section IV. In Section V, we provide a discussionof future directions and Section VI concludes our paper.

II. ANOMALY DETECTION IN SENSING SYSTEMS

Anomaly detection has been widely studied in the context ofwireless sensor networks (WSNs). In this section, we providea brief survey and a classification of these studies. A morecomprehensive treatment of intrusion detection and anomalydetection has been presented in [5]. In this paper, we aimto investigate the applicability of existing schemes to IoT andcloud and provide a road map for potential approaches that arenovel and useful in these areas. Anomaly-detection approachescan be classified as distributed, centralized (or stand-alone) andhierarchical approaches. A comparative summary is providedin Table 1.

A. Distributed Approaches:

In [10],the authors have proposed a rule-based anomaly-detection scheme. Rule-based techniques are in general fastand they scale well however derivation of rules may requiresome effort. Machine learning has been widely employed inrule-based systems. Roman et al. [11] also proposed a dis-tributed and cooperative detection approach that relies on thebroadcast nature of sensor communications and takes advan-tage of the high density of sensors being deployed in the field.In [16], the authors have proposed another distributed and rule-based anomaly detection approach which specifically targetsblackhole and selective forwarding attacks where this work isextended in [17] to include a specification based approach.Statistical anomaly-detection have been also considered inWSNs. Onat and Miri [24] proposes a distributed, statisticalanomaly based approach which exploits the stability of theneighborhood information of the WSN and defines anomaliesbased on average receive power and average packet arrivalrate. In [25], the authors employ support vector machinesto minimizes communication overhead while performing in-network anomaly detection.

B. Centralized Approaches:

Although distributed approaches are more flexible andscalable for WSNs, centralized approaches are simpler toimplement and incur less computation cost on the sensors.Ngai et al. [18] proposes a centralized, statistical anomalydetection scheme which targets routing pattern anomalies, in

particular when sinkhole attacks are implemented. In [26], theauthors propose a reputation based approach and use heuristicranking algorithms to identify most likely bad nodes in thenetwork.

C. Hierarchical Approaches:

Hierarchical approaches offer scalability in large networks.Chen et al. [12] have proposed a rule based hierarchicalapproach that monitors group of nodes and routing tables fordetection. In [13], the authors have proposed a rule basedapproach that takes packet dropping rate into account. Theresearch in [15] have proposed a specification based approachthat clusters data according to standard deviation from theaverage inter-cluster distance. In [19] the authors have con-sidered a statistical anomaly based approach and used hiddenMarkov models while [21] and [22] have incorporated gametheory along with Markov decision process. An hierarchicaland reputation based approach has been proposed in [27].Finally, [14] have combined several rule based approaches toimprove the performance of hierarchical anomaly detectionschemes.

D. Stand Alone Detection Approaches:

A stand alone anomaly detection scheme has been proposedby Onat and Miri [20] where each node individually detectattacks by keeping short term dynamic statistics of events.In this scheme local attacks can be discovered only. Anotherstand alone approach defines rules for physical, MAC, routingand application layers and aims to detect anomalies at alllayers of a network stack in WSNs [23].

III. ANOMALY DETECTION IN IOT

The studies surveyed in the previous section are primarilydesigned for sensing systems however they are useful indiscussing which classes of approaches are more appropriatefor IoT.

Heterogenous network topology is very common in IoTenvironments, therefore clustering is natural tool to handlethe complexity of the various environments. With this respect,hierarchical intrusion and anomaly detection approaches canbe adopted in IoT. Statistical detection, reputation and gametheoretic methods can be adopted in clusters of things orobjects. These techniques require a long histogram of data andthey have slow learning or voting patterns which is convenientfor the big data generated by IoT devices.

Anomaly detection methodologies can be further dividedinto 3 categories ( see Figure 1) [5]:

• Statistical• Data-mining• Artificial-intelligence (AI)

All of these methods demand either statistical analysis of dataor learning from previous data samples. Statistical methodscan be uni-vriate, multi-variate or time-series based. Datamining techniques can use expert systems, description lan-guages, state machines, or clustering. AI techniques can be

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TABLE ICOMPARISON OF THE ANOMALY DETECTION SCHEMES PROPOSED FOR WSNS, WITH THEIR APPLICABILITY TO IOT AND CLOUD

Proposed sys-tem

Architecture Detection technique Highlighting features Applicabilityto IoT

Applicabilityto Cloud

Da Silva et al.[10]

Distributed Rule based approach (intervalrule)

Scalable, robust and fast intrusion detection. no yes

Roman et al.[11]

Distributed andCooperative

Spontaneous watchdogs Relies on the broadcast nature of sensor com-munications and takes advantage of the highdensity of sensors being deployed in the field.

no yes

Chen et al. [12] Hierarchical Rule based approach Uses monitoring group of nodes and routingtables for detection

yes no

Su et al. [13] Hierarchical Rule based approach (packetdropping rate)

Saves energy, extends the network lifetime. Onthe other hand, new nodes cannot be added tothe network.

yes no

Strikos [14] Hierarchical Rule based approach Combined already existing approaches, in orderto achieve a more complete solution. Neithersimulation results, nor real world experimentalresults are provided.

yes no

Rajasegarar etal. [15]

Hierarchical Specification based approach,data clustering (standard devi-ation from the average inter-cluster distance)

Achieved comparable performance with thecentralized schemes.

yes no

Krontiris et al.[16]

Distributed andCooperative

Rule based approach (packetdropping rate)

Detects only blackhole and selective forwardingattacks. Besides, proposed solution works onlywhen there is one attacker.

no yes

Krontiris et al.[17]

Distributed andCooperative

Specification based approach Proposed solution works only when there is oneattacker.

no yes

Ngai et al. [18] Centralized(BS)

Statistical based anomaly detec-tion (parametric), routing pat-tern anomalies

Specified to detect Sinkhole attacks only. yes yes

Doumit andAgrawal [19]

Hierarchical Statistical anomaly basedapproach (parametric), hiddenMarkov model

Focused on the accuracy of the data gathered,rather than the security of the nodes or the links.

yes no

Onat and Miri[20]

Stand alone Statistical based anomaly detec-tion (real time traffic on thenodes, arrival process)

Keeps short term dynamic statistics usinga multi-level sliding window event storagescheme. The scheme works on each node, there-fore the detections are local and nodes are notaware of the attacks globally (network-wide).

yes no

Agah et al. [21][22]

Hierarchical Game theory along withMarkov decision process

Only one of the clusters of the network ismonitored at a time. This leaves the rest of thenetwork un-protected.

yes no

Bhuse andGupta [23]

Stand-alone Rule based approaches (forphysical, MAC, routing and ap-plication layers)

Proposed lightweight techniques that would de-tect anomalies at all layers of a network stackin WSNs.

yes yes

Onat and Miri[24]

Distributed andCooperative

Statistical anomaly based ap-proach (average receive powerand average packet arrival rate)

Exploits the stability of the neighborhood infor-mation of the WSN nodes.

yes no

Rajasegarar etal. [25]

Distributed Anomaly based approach, sup-port vector machine

Minimizes communication overhead while per-forming in-network anomaly detection.

no yes

Wang et al. [26] Centralized(data sink)

Reputation based approach Uses heuristic ranking algorithms to identifymost likely bad nodes in the network.

yes no

Bao et al. [27] Hierarchical Reputation based approach Uses high scalable cluster-based hierarchicaltrust management protocol to effectively identi-fying the selfish and malicious nodes.

yes no

Bayesian, Markov, Fuzzy, genetic algorithms, neural networksor principal component analysis methods.

For the deployment of anomaly detection methodologiestowards the security if IoT and Cloud, agent based deploymentstrategy seems to be a good choice. In this kind of deployment,in order to detect various anomalies effectively; multipleagents work cooperatively and share their local findings amongeach other. The steps of an agent-based system has beendefined in [5].

From the legislation (law) point of view: With the emer-

gence of IoT and Cloud, new regulatory approaches to ensureits privacy and security have become necessary. Geographi-cally limited national legislation does not seem to be appro-priate for IoT and Cloud. However, self-regulation (which hasbeen applied up until now) will not be sufficient to ensureeffective privacy and security. An adequate legal frameworkmust consider the underlying technology and would be estab-lished by an international legislator, which is supplementedby the private sector according to their specific needs andtherefore will become easily adjustable when necessary [7].

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Fig. 1. Classification of anomaly detection methodologies [5].

IV. ANOMALY DETECTION IN CLOUD-CENTRIC IOT

In the IoT architecture, applications that interact with thesensors require massive processing power, tremendous storagecapacity and huge network bandwidth in order to handle bigdata obtained through sensing services provided by large-scalesensor networks. Cloud computing offers rapid access to ashared pool of virtualized resources based on the pay as you gofashion [6], [28]–[30]. Therefore in [31], significant amount ofthe big data will be contributed by the IoT sensors, the authorsdefine the cloud as the front-end of the IoT architecture toenable real time processing and storage.

Cloud computing mainly offers three service models,namely Infrastructure as a Service (IaaS), Platform as aService (PaaS) and Software as a Service (SaaS) [9], [32].In IaaS model, the service providers offer compute servers,data storage, load balancers and security firewalls as a servicevia a virtual infrastructure manager whereas PaaS enablesdelivering a computing platform, and the service content inPaaS can be programming languages, frameworks, mashupeditors or structured data. In SaaS delivery model, softwareand databases are provided as services via web browsers. Asseen in Fig. 2, these service models require a different level ofsecurity mechanisms in the Cloud environment. Given that thesecurity requirements of IaaS, PaaS, and SaaS layers of a cloudsystem differs from security requirements of a conventionalcomputing system, the authors in [9] point out the complexity

Fig. 2. Service plane in cloud computing [33] and corresponding securityplane

of building a holistic security solution for a cloud system.Suggested solutions include shutting down unused services,keeping patches updated, reducing permissions and accessrights of applications and also users, filtering out anoma-lous packets directed to specific ports or services, isolatingresources to ensure security of data during processing: byisolating the processor caches in virtual machines,and isolatingthose virtual caches from the hypervisor cache, avoiding IPspoofing and using encrypted protocols wherever possible, andavoiding ARP poisoning by using static ARP tables, or havingARP tables are logged otherwise.

Performing real time analytics on large volumes of datagathered through IoT objects requires efficient methods forstorage, filtering, transformation and retrieval. Data and theanalytics resources can be either public or private. In [6],four cases (i.e., private data-public analytics, private data-private analytics, public data-private analytics, public data-public analytics) have been studied, and it has been reportedthat in private cases (data is private, analytical models areprivate, or both), security is a big concern and must be ensuredby the Cloud service providers.

With the advent of mobile computing, privacy concernsof the cloud users have increased. For instance, whenevera country border is passed, the service provider needs tobe changed. The networks may allow service providers tosniff lots of private information passing through them suchas the web sites that are visited, social web site posts, thenumbers dialed or SMS messages. The short list of the privacyproblems to be faced whenever Cloud services are used [8] isas follows: The need of real-time anomaly detection; privacyof data mining and relevant analysis; and the need of accesscontrol.

There is a clear need for privacy preservation in cloud-centric IoT due to the fact that IoT generates vast amountof data, connects billions of devices that pervasively persistin our surroundings while Cloud platforms allow these datato be processed and stored in remotely. Anomaly detection

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is anticipated to play a key role in securing cloud-centricsince data is expected to have patterns and diversifying froma certain pattern could provide indication of a possible attack.Therefore, in this paper, we study and report the applica-bility of previously proposed anomaly detection to IoT andCloud. Table I illustrates a comprehensive summary of theseschemes and presents a comparison on the applicability ofthese schemes to IoT and cloud applications. The followingconclusion can be made from Table 1:

• Clustering (hierarchical) based IDSs are applicable to IoThowever these approaches are not common and suitablefor Cloud.

• Distributed and collaborative IDSs are applicable toCloud. Agent based intelligent systems provide rapidresponse to detect and intercept intrusions, which is aprominent feature for Cloud. They require high process-ing power and RAM; hence they are not applicable forIoT.

• Statistical detection based IDSs are applicable to IoT.Since they require long term histogram of the intrusivedata, they are not applicable to Cloud.

• Game theory based IDSs are applicable to IoT. However,due to their slow learning patterns, they aren’t applicableto Cloud applications.

• Anomaly detection based IDSs are both applicable to IoTand Cloud.

• Watchdog based IDSs are applicable to Cloud. Theyprovide fast response which requires peers to commu-nicate continuously (power consuming for IoT devices).Therefore these protocols are not suitable for IoT.

• Reputation based IDSs are applicable to IoT. Since theyhave slow voting patterns, they are not suitable applicablefor Cloud applications.

V. FUTURE DIRECTIONS

Security of cloud-centric IoT can be addressed from threeperspectives: (i)Identity and application related security andprivacy issues, (ii) Compliance related issues and (iii) legalissues. Compliance covers data recovery and business continu-ity, availability of logs and audit trails. In Figure 3, we presentthe summary of the security challenges in cloud-centric IoT.

Anomaly detection is anticipated to play a key role in secur-ing IoT and cloud since data is expected to have patterns anddiversifying from a certain pattern could provide indication ofa possible attack. Existing anomaly detection techniques forsensing systems can provide the basis for anomaly detectionin cloud-centric IoT. However, scalability, heterogeneity andlimited resources of IoT devices combined with distributedstorage and processing on the cloud introduce challenges topreviously proposed approaches. Rule-based systems do notscale well with the dynamic environment of cloud-centric IoT.Statistical methods and learning-based method are anticipatedto perform well, however they also suffer from the difficultyof deriving usful features from the big data generated by IoTdevices. Anomaly detection methodology for the cloud-centricIoT is expected to:

Fig. 3. Summary of the security challenges in cloud-centric IoT

• Keep the utilization of system resources at minimumbecause most IoT devices run on limited resources

• Be light-weight, i.e. it should avoid introducing unneces-sary overhead

• Be reliable and minimize false positives and false nega-tives in the detection phase

• Avoid introducing new weaknesses to the system,• Run continuously and remain transparent to the system

and the users,• Support inter-operability

VI. CONCLUSION

In this paper we present security and privacy risks inIoT and cloud and discuss potential solutions from anomalydetection aspect. We first provide a brief literature review onthe anomaly detection schemes in sensor systems. Then, wediscuss the challenges in adopting existing security measuresin IoT and cloud and propose potential solutions to some ofthe core problems. This paper provides a road map for futurestudies on anomaly detection for IoT and cloud.

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