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DefenseChain: Consortium Blockchain for CyberThreat Intelligence Sharing and DefenseSoumya Purohit, Prasad Calyam, Songjie Wang, RajaniKanth Yempalla, Justin VargheseUniversity of Missouri, Email: {spvm7, rypbc, jv8dz}@mail.missouri.edu, {wangso, calyamp}@missouri.eduAbstract—Cloud-hosted applications are prone to targetedattacks such as DDoS, advanced persistent threats, cryptojacking which threaten service availability. Recently, methods forthreat information sharing and defense require co-operation andtrust between multiple domains/entities. There is a need formechanisms that establish distributed trust to allow for sucha collective defense. In this paper, we present a novel threatintelligence sharing and defense system, namely “DefenseChain”,to allow organizations to have incentive-based and trustworthyco-operation to mitigate the impact of cyber attacks. Oursolution approach features a consortium Blockchain platform toobtain threat data and select suitable peers to help with attackdetection and mitigation. We propose an economic model forcreation and sustenance of the consortium with peers througha reputation estimation scheme that uses ‘Quality of Detection’and ‘Quality of Mitigation’ metrics. Our evaluation experimentswith DefenseChain implementation are performed on an OpenCloud testbed with Hyperledger Composer and in a simulationenvironment. Our results show that the DefenseChain systemoverall performs better than state-of-the-art decision makingschemes in choosing the most appropriate detector and mitigatorpeers. In addition, we show that our DefenseChain achieves betterperformance trade-offs in terms of metrics such as detection time,mitigation time and attack reoccurence rate. Lastly, our validation results demonstrate that our DefenseChain can effectivelyidentify rational/irrational service providers.Index Terms—Blockchain, Threat Intelligence Sharing, Distributed Trust, Cyber Security, Reputation SystemI. I NTRODUCTIONCloud-hosted services are targeted by ever-growing Distributed Denial of Service (DDoS) attacks that aim to disruptthe service of major industries, conglomerates and communityorganizations [1]. Attacks such as Advanced Persistent Threats(APTs) also cause economic damage and leakage of sensitiveinformation through sophisticated malicious attack codes [2].Another targeted attack type can be seen in the cryptojackingattacks [3], where criminals compromise enterprise resourcesfor illegal bitcoin mining revenue gains.To defend against such targeted attacks, a co-operative andcollaborative attack threat intelligence sharing platform canhelp raise the situational awareness and foster mechanismsto protect targeted assets through pertinent detection andmitigation of attacks. The platform can produce proactivemeasurements and actionable information that can be available to multiple domains/entities in a federation [4]. Fig. 1illustrates various threat sharing scenarios supported by anexemplar platform, where organizations gain actionable threatdata by paying an admission fee. Moreover, organizations978-1-7281-7091-6/20/ 31.00 2020 IEEEFig. 1: Illustration of the threat intelligence sharing probleminvolving a combination of cases with free riding and falsereporting associated with/without an admission fee.under a line of attack in close proximity can leverage theplatform to form alliances for collaborative defense by sharingthe burden [5]. However, such beneficial platform scenariosare vulnerable to ‘false reporting’ and ‘free riding’ issues [6].False reporting is caused by federation members maliciouslyreporting cyber attack incidents in order to waste resourcesof the other federation organizations. Additionally, free ridingcan occur when an organization takes advantage of platformservices without making contributions to the federation incases that require member co-operation for collective benefit.Creation of threat intelligence sharing platforms requiresovercoming other substantial challenges that include issuessuch as: why should one domain share its threat intelligenceinformation with another domain? How can we opt-in thedomains/entities that are proximal (i.e., in geographic distanceor units that are distributed but belong to the same organization) or distant (i.e., relatively far in geographic distanceor belonging to different organizations) for collective attackdefense? How can the platform be used for co-ordinated threatdetection and attack impact mitigation in a timely manner withdistributed trust? A subset of these issues have been addressedin prior works using methods such as crowdsourcing withincentives [7] [8]. Reputation systems also have been proposedwith algorithms to counter the impact of having false reportingand free riding peers [9] [10] [11]. However, there is a lackof works that use Blockchain solutions that can potentially beused to establish distributed trust, integrate reputation systemsand create automated access control for threat intelligence data

sharing in a scalable and transparent manner.In this paper, we address the above threat intelligencesharing challenges and propose a novel “DefenseChain” platform for a two-stage (i.e., attack detection followed attackimpact mitigation) cyber defense using a Blockchain referencearchitecture. Specifically, we adopt a permissioned/consortiumBlockchain architecture whose benefits include: (a) relativelyshort deployment duration and less resource-intensive properties in terms of the consensus mechanism, and (b) more effective than a permissionless/public Blockchain architecture forprotected data sharing amongst a federation of organizations.Our solution approach also features a novel reputation systemand uses a set of protocols to rate the peers objectively in termsof ‘Quality of Detection’ (QoD) and ‘Quality of Mitigation’(QoM) metrics for cyber defense. The values of the metrics areuniquely calculated using our prior work on the Dolus systemthat uses a “attack defense by pretense” paradigm to countertargeted attacks such as DDoS, APTs and cryptojacking [12].These metrics are also utilized in an economic model thatwe propose for creation and sustenance of the consortiumwith proximal and distant peers in manner that eliminatesfalse reporting and free-riding issues by e.g., charging a finein non-ideal cases, and integrating incentives for successfullyservicing detection and mitigation requests within deadlines.Lastly, we implement our DefenseChain platform using theHyperledger Composer in a NSF Cloud testbed [13]. Ourexperimental testbed is realistic and comprises of a federationof domains, including a number service provider peers cooperating with different domains in order to perform attackdetection and impact mitigation independently, as well as a setof users trying to access a targeted application server, whichis being disrupted by a set of attackers. Based on experimentresults from this testbed and simulation experiments, weshow the benefits of our novel co-operative real-time threatintelligence sharing platform capabilities in comparison withstate-of-the-art solutions such as [14] [15] [16] and [6].The main contributions summary of this paper is as follows: We propose a consortium Blockchain based “DefenseChain” platform for real-time threat intelligencesharing as part of a ‘defense by pretense’ strategy.We equip our DefenseChain platform to provide distributed trust through analysis of QoD and QoM of peersto help a requester to choose the effective detector(s) andmitigator(s) to defend against targeted cyber attacks.We build a reputation system in DefenseChain usingan economic model to rate QoD and QoM using bothobjective and subjective metrics for providing appropriate reward/penalty to the co-operating rational/irrationalpeers. The reward/penalty uses metrics such as detectiontime, mitigation time and attack reoccurence rate.We evaluate our DefenseChain implementation throughcomparisons with state-of-the-art schemes for decisionmaking in choosing the best detector and mitigator peers.Our results show that DefenseChain outperforms theexisting schemes by dynamically providing mitigationstrategies through enforcement of chaincode-based policies to counter impending/active cyber attacks.The remainder of the paper is organized as follows: SectionII discusses prior related works. Section III presents background on threat intelligence sharing requirements and detailsvarious system components within a Blockchain referencearchitecture. Section IV describes the performance evaluationexperiments and results. Section V concludes the paper.II. R ELATED W ORKSA. Threat Intelligence SharingDue to the constant increase in the number and complexityof cyber attack incidents, organizations are eager to haveproactive and actionable knowledge for efficiently defendingtheir valuable assets i.e., cloud-hosted applications. Towardsthis end, they need to develop the practice to share threatintelligence information amongst their peers in order to effectively and collectively detect cyber attacks, and stand uprobust defenses that mitigate the attack impact on their assets.Several works have been performed to enable cyber defenders to explore threat intelligence sharing capabilities andconstruct effective defenses against the ever-changing cyberthreat landscape. The authors in [17] and [18] identify gaps inexisting technologies and introduce the Cyber Threat Intelligence model (CTI) and a related cyber threat intelligence ontology approach, respectively. The work in [19] details a novelapproach based on Structured Threat Information eXpression(STIX) to deal with system diversity during threat informationsharing. An encryption strategy for threat intelligence sharingis proposed in [20] in the form of a privacy preservingprotocol. The CYBEX work in [21] details an incentivizedapproach and uses the concept of an admission fee, as well asinteraction models organizations for cybersecurity informationexchange to defend against attackers in a dynamic game.The novelty of our work is in the design of a threatintelligence sharing platform using consortium Blockchain inorder to implement a ‘defense by pretense’ paradigm for cyberdefense as detailed in the work on the Dolus system [12]. Weadapt the two-stage ensemble learning scheme to trigger cooperation between multiple domains who collectively providedetection and impact mitigation to defend a domain targetedby attackers through DDoS, APTs and cryptojacking.B. Reputation SystemsSeveral different reputation systems have been proposed inprior works that address the issues of false reporting and freeriding [8], [9], [10]. The work in [8] proposed the design of acrowdsourcing tournament to maximize a service provider’sutility in crowdsourcing and provide continuous incentivesfor users by rewarding them based on the rank achieved.The authors in [9] presented schemes to eliminate dishonestbehavior with the help from a trusted third party. In a relatedeffort [22], a reputation system is developed that overcomesthe limitations in decentralized systems and quantifies thereputation by removing human opinion from the transactions.E-commerce applications [23] have also adopted reputation

systems that use Blockchain solutions for implementation ofprivacy-preserving mechanisms involving Proof of Stake fordetermining any new block to be accepted instead of acceptingthe highest difficulty block. The authors in [24] designeda trust model that evaluates trust based on the reputationbuilt up on historical interactions and indirect opinions aboutthe sender. The work in [25] introduces a proxy to transferreputation values between anonymous contributions, and areputation anonymization scheme is shown to prevent theinadvertent leakage of privacy.The closest related work to our work is in [6]. Therein,a reputation and reward scheme is proposed that considers potential information frauds and allows automatic smartcontract execution based on malicious peers. We adapt theirBeta reputation that is used for probabilistic rating and toidentify and reward honest participants. Our work also borrowsthe idea of using a InterPlanetary File System (IPFS) [26]for creation of the reputation system and to store deviceattributes as well as threat data in an off-chain manner in ourBlockchain architecture. We include the concept of a deposit,and a request/response deadline to eliminate free-riding casesand false reporting similar to the work in [6]. Furthermore,we propose a novel objective evaluation of attack detectionand impact mitigation through real-time threat intelligencesharing using novel QoD and QoM metrics. Our reputationsystem also features a trust-based model implemented usingthreat detection and attack impact mitigation protocols that aremotivated by prior work in [10] for incentivizing domains ina federation to co-operate and trust each other.C. Blockchain for Building TrustThere have been several studies that utilize Blockchainas a solution in order to solve the problems inherent intraditional transactional models. CrowdBC [27] is an exemplarwork that implements a reward/penalty scheme using smartcontracts, and explores the ability to abstract a user’s realworld identity for providing a unique method to ensure dataprivacy. In the area of IoT and sensor networks, works suchas [28] proposed security models based on Blockchain toensure the validity and integrity of cryptographic authentication data. A Blockchain-based security model is proposed forforensic evidence preservation [29] in order to allow storageof metadata e.g., pieces of evidence using smart contractsamongst the different entities involved in an investigationprocess. Similarly, iShare [30] features a security model thatleverages Blockchain to collect cyber attack information andshares it across organizations in an anonymous fashion. Theanonymity afforded by this approach serves as inspiration toour approach to threat intelligence sharing across a federationof proximal/distant domains. Anonymity issues have also beentackled in [31], where Blockchain is used to enable anonymousreputation estimation as part of establishing privacy-preservingtrust for vehicular ad hoc networks.Our work on DefenseChain is motivated by the aboveworks in the context of designing our reputation system using Blockchain technologies, and for incentivizing federationpeers via an economic model based on a deposit fee receivedfrom potential detector and mitigator peers.III. D EFENSE C HAIN C ONCEPTS AND S YSTEM D ESIGNA. Threat Intelligence SharingWith the growing intensity and scale of cyber attacks oncloud-hosted applications, it is critical to share the threat intelligence data within multiple domains in order to rapidly detectcyber attack threats and effectively mitigate the attack impactsin a collaborative manner. The shared threat information canbe classified as the requester’s IP, attack start and attackstop times, bytes and packets captured, etc. Such informationattributes can be communicated across federation peers so thatdetection and mitigation can be carried out using co-operationstrategies that create a win-win for the peers who are impactedand who are willing to offer cyber defense services.Fig. 2: A dynamic coordination setup in DefenseChain forthreat intelligence sharing amongst proximal and distant controller nodes during mitigation of targeted attacks.The attack/defense model we consider is similar to theexemplar model considered in the Dolus system [12], where afederation of autonomous systems co-operate using a ‘defenseby pretense’ paradigm to effectively block attacker trafficcloser to the source side. The defense by pretense strategyalso buys time for the cyber defenders by creation of illusionof attack success, while a robust defense strategy is beingput in place through the co-operation of the federation peers.In our federation of users, we assume threat intelligencesharing is carried out differently for proximal and distantpeers through separate controller nodes, as depicted in Fig 2.The Proximal Controller with a Hyperledger (i.e., an exemplar permissioned Blockchain technology) setup performs theattack traffic redirection from the slave-switch and the rootswitch to a Quarantine Virtual Machine (QVM). This actionsafely allows the requester machine to access the new machinewithout any disruption. In the case of a Distant Controlleroffering a mitigation service, the transfer of attack traffic fromthe slave-switch to the root-switch takes a relatively longertime due to the geographic distance or due to the time neededto establish trust. Similarly, attack traffic redirection to theQVM in the case of the Distant Controller happens same asin the case of the Proximal Controller, however with a delay.We model the detection and mitigation as two separatesteps. The detection involves the identification of vulnerabilityof attack, suspiciousness score of a domain resource node,

Fig. 3: Proposed DefenseChain reference architecture that features on-chain/off-chain components within a federation of peersinvolving a cloud-hosted application, dedicated controllers with Hyperledger configurations, IPFS and QVMs integration.and the attack time duration. Also, the mitigation criticallyfocuses on the type of mitigation performed, redirection ofattack traffic and spoofing of the server’s IP. With our proposedeconomic model detailed later in Section III-E, we addressthese problems by harnessing an incentive-based approach thatdevelops a foundation of distributed trust.B. DefenseChain Platform OverviewFig. 3 illustrates our proposed reference architecture in afederation where a cloud service provider is hosting severalservers belonging to different organization peers that maybe vulnerable to cyber attack threats. Roles of the peersinvolved in the federation area defined in Section III-C. Thecentral part of our DefenseChain architecture is the consortium Blockchain-based trust setup created on top of theDolus defense by pretense implementation as outlined in [12].Within this federation, we assume that there are organizationpeers requesting for a detection and mitigation service fromcooperating domains. Furthermore, each domain can performtheir service using a suitable mitigation strategy such as e.g.,moving target defense, defense by pretense, network firewalldefense using blacklisting, etc. Our DefenseChain rates thedetection and mitigation service quality of the peer(s), andprovides the requesting peer(s) with the flexibility to choosethe domain that can provide the higher levels of service qualitymeasured through the QoD and QoM metrics that are detailedin Section III-D. Furthermore, through our economic modeldescribed in Section III-E, we implement an incentivizedapproach that allows the mitigator domains to collaborate andalso eliminates the issues of free riding and false reporting.Additionally, our platform design includes on-chain and offchain components for storage, processing and sharing of threatintelligence information. We elaborate on these components inthe following:On-Chain: this component fetches and displays the detailssuch as e.g., attacker IP, source IP, number of packets, spoofedIP, blacklisted IP from the IPFS. These details are fed into thedetection and mitigation chaincodes that initialize and manageledger state through transactions submitted by applications. Inour DefenseChain, they help in calculation of QoD and QoMin a federation of peers, respectively.Off-Chain: this component stores information such as thepacket capture, bandwidth capture and device attributes data.Depending on the number of transactions and the attacksencountered, the storage of the related data will require largeamounts of storage (in the order of tera bytes or even petabytes in core network domain scenarios). For this purpose,we utilize the IPFS concept from [26] as an off-chain storagethat interacts periodically through the Oraclize [32] service.The hashes of the IPFS data are refer

fenders to explore threat intelligence sharing capabilities and construct effective defenses against the ever-changing cyber threat landscape. The authors in [17] and [18] identify gaps in existing technologies and introduce the Cyber Threat Intelli-gence model (CTI) and a related cyber threat intelligence on-tology approach, respectively.