ChapterPDF Available

ProtectDDoS: A Platform for Trustworthy Offering and Recommendation of Protections

Authors:

Abstract and Figures

As the dependency of businesses on digital services increases, their vulnerability to cyberattacks increases, too. Besides providing innovative services, business owners must focus on investing in robust cybersecurity mechanisms to countermeasure cyberattacks. Distributed Denial-of-Service (DDoS) attacks remain one of the most dangerous cyberattacks, e.g., leading to service disruption, financial loss, and reputation harm. Although protection measures exist, a catalog of solutions is missing, which could help network operators to access and filter information in order to select suitable protections for specific demands.
Content may be subject to copyright.
ProtectDDoS: A Platform for Trustworthy
Offering and Recommendation of Protections
Muriel Franco, Erion Sula, Bruno Rodrigues, Eder Scheid, Burkhard Stiller
Communication Systems Group CSG, Department of Informatics IfI,
University of Z¨urich UZH
Binzm¨uhlestrasse 14, CH—8050 Z¨urich, Switzerland
E-mail: [franco, rodrigues, scheid, stiller]@ifi.uzh.ch, erion.sula@uzh.ch
Abstract. As the dependency of business on digital services increases,
their vulnerability to cyberattacks increases, too. Besides providing in-
novative and flexible services, business owners must focus on investing in
robust cybersecurity mechanisms to countermeasure cyberattacks. Dis-
tributed Denial-of-Service (DDoS) attacks remain one of the most dan-
gerous cyberattacks, e.g., leading to service disruption, financial loss, and
reputation harm. Although protection measures exist, a catalog of solu-
tions is missing, which could help network operators to access and filter
information in order to select suitable protections for specific demands.
This work presents ProtectDDoS , a platform for offering and recommen-
dations of DDoS protection. ProtectDDoS provides a blockchain-based
catalog, where DDoS protection providers can announce details regard-
ing their services, while users can obtain recommendations of DDoS pro-
tections according to their specific demands (e.g., price, attacks sup-
ported, or geolocation constraints). ProtectDDoS’s Smart Contract (SC)
maintains the integrity of data about protections available and provides
tamper-proof reputations. To evaluate the feasibility and effectiveness of
ProtectDDoS , a prototype was implemented and a case study conducted
to discuss costs, including interactions with the SC.
Keywords: Cybersecurity ·DDoS Protection ·Recommender System ·
Smart Contract (SC) ·Marketplace.
1 Introduction
Denial-of-Service (DoS) attacks represent a significant threat to any commer-
cial organization and individuals, which rely on Internet-based services. In the
last years, such attacks have become more complex and sophisticated, and, in
turn, difficult to predict and mitigate [6]. Even more dangerous are the so-called
Distributed Denial-of-Service (DDoS) attacks, as the attack itself derives from
multiple hosts distributed over the network, such as those using botnets. Con-
sequently, the affected targets (e.g., companies and governments) are usually
confronted with economic impacts. Not only do these attacks cause financial
damages due to the unavailability of the service and loss of online traffic, but in
2 Franco et al.
critical cases, they inflict long-term damage to the corporate reputation, causing
drastic drops of the stock prices [1].
Further, the number of DDoS attacks has almost tripled in the last three
years, with an averaged financial loss of dozens of thousands of USD (United
States Dollar) per hour of the attack. For example, it is estimated that, only
in the United Kingdom (UK) in 2019, DDoS will cost more than USD 1 billion
[4], which includes revenue loss and cyber insurance premiums. These numbers
continue to grow due to the increasing amount of exposed Internet-of-Things
(IoT) devices and Artificial Intelligence (AI) techniques. Cybersecurity predic-
tions point that the number of DDoS attacks globally will reach 17 million by
2020, causing several economic and societal impacts.
Based on this threat landscape, large companies and governments are spend-
ing roughly USD 124 billion on information security products and protection
services. However, many of the problems plaguing cybersecurity are still eco-
nomic in nature [7]. Often systems fail because the organizations do not bear to
assess the full costs of a failure neither the risks involved. It is still more preva-
lent when, for example, considering organizations and users with restrictions of
budget or technical expertise to invest in cybersecurity, such as Small- Medium-
sized Enterprises (SME). Therefore, it is clear that efficient risks analysis and
investments in proper cybersecurity solutions are critical for the next years for
both organizations and governments with services or systems exposed on the
Internet. These investments must not focus solely on reactive protection against
DDoS attacks but also target the planning and decision process of cybersecurity
to predict attacks and possible losses arising from a cyberattack. Therefore, mul-
tiple layers of precaution to protect the critical services against DDoS attacks
are required.
Nowadays, the variety of DDoS protection services has increased as well.
While competition in this sector may have benefits for consumers, such as higher
quality for the same price or diversified products, organizations often struggle
with choosing a protection service that suits their needs. Solutions that help the
offering and selection of DDoS protection can support the organization in the
decision-making process. More specifically, by providing the user with essential
information related to the thousands of DDoS protection services available, tak-
ing into account filters and characteristics of the cyberattack (e.g., fingerprints
and log files) provided by the user. However, there are no intuitive solutions
(e.g., dashboards) that simplify the access to a broad set of DDoS protections
while ensuring the integrity of the information from the protections available,
i.e., tamper-proof information. Besides that, there is still a lack of integration
of catalogs and mechanisms that help to decide which is the most suitable pro-
tection taking into account specific DDoS scenarios and user’ demands.
This paper presents ProtectDDoS, a blockchain-based platform for the of-
fering and supporting the recommendation of protection services against DDoS
attacks. ProtectDDoS provides a blockchain-based catalog where protection pro-
viders can announce protections and interested users can filter its protections
by using different parameters, such as price, type of DDoS attack supported,
ProtectDDoS: Trustworthy Offering and Recommendation of Protections 3
and deployment time. In addition, DDoS attacks fingerprints [10] can be used
as input to find the most suitable protection for a determined type of attack.
Contributions are summarized as follows:
a Smart Contract (SC) is implemented to store (i) the hash of protection
services and the private address of protection providers to verify the origin
and integrity of protections available and (ii) protections’ reputations based
on users’ feedback, which can be used to avoid protections with misbehavior
or insufficient performance for a determined scenario.
offers a dashboard fully integrated with a recommender system for protec-
tion services, called MENTOR, allowing the user to use a web-based interface
to obtain a recommendation of the most suitable solution according to its
demands and predefined filters.
The remaining of this paper is organized as follows. Background and re-
lated work are reviewed in Section II. In Section III, the platform for offering
and recommend DDoS protections is introduced, and implementation details are
provided. In Section IV, the feasibility of the proposed solution is discussed, and
a case study is presented. Section V, a functional evaluation is provided in order
to measure the additional costs of the ProtectDDoS. Finally, in Section VI, a
summary of the paper and comments on future work are provided.
2 Background and Related Work
As businesses strengthen their digital dependency, they also become more vulner-
able to cyber threats. Therefore, besides the need for speed innovation, decision-
makers in cybersecurity (e.g., network operator, company owner, or an expert
team) have to be able to implement robust security mechanisms while managing
costs and risks associated with the business [9]. Such activities involve:
1. Identify security risks and associated costs and (ii) determine impacts of
cybersecurity in the business or service. In turn, it is possible to estimate
the overall impacts (e.g., financial loss occasioned by a business disruption)
in order to decide whether to invest in cybersecurity.
2. React against an imminent cyberattack, or assume the risks, paying for
the damage or delegating that to third-parties (e.g., cyber insurers).
In (1), such an overall estimation can be done using different approaches. For
instance, the Return On Security Investments (ROSI) [12] offers a benchmark to
determine when a specific investment in cybersecurity is recommended based on
the potential financial loss given an assessed risk. Based on that, decision-makers
have to decide how to handle a possible or imminent threat. Between the differ-
ent choices, the decision-maker can (i) determine a plan to prevent cyberattacks
and its impacts proactively. Concerning (2) if an attack happens, prevention is
cheaper than react when an attack already surpassed the infrastructures. If com-
panies do not invest correctly in cybersecurity, the security of their operations
4 Franco et al.
depends on luck, and the impacts of attacks can be devastating, which is not
acceptable by one that has a reputation to maintain.
The market for protection services has grown together with the investments
in cybersecurity. Nowadays, several providers are offering protections for differ-
ent kinds of attacks (e.g., data leaks, DDoS, and malwares) and demands. For
example, [2] provides a repository that lists providers offering many protection
services to address different cybersecurity threats, such as advanced threat pro-
tection, anti-virus, secure communications, and anti-phishing. The number of
protections available is large and is growing in parallel with the investments in
cybersecurity. Only on such a repository, 1200 providers are listed, and one can,
for example, obtain information to contract more than 80 protection services
against DDoS attacks. However, even though there are few catalogs centraliz-
ing information from different cybersecurity solutions [2], there still a lack of
platforms that use such information to simplify the decision-process and cyber-
security planning of companies. Table 1 provides a comparison among different
cybersecurity-oriented solutions that implement approaches to offer or recom-
mend services.
Table 1: Comparison of Related Work in terms of designed functionalities
-Functionalities
Solution User-friendly
Catalog
Supports
Recommendation Filters Allows Integrity
Verification
Reputation
Mechanisms
[3] No Yes Yes No No
[2] Yes No Yes No No
[8] No Yes Yes No No
[5] No Yes Yes No No
[11] Yes No No Yes Yes
ProtectDDoS Yes Yes Yes Yes Yes
In previous work, MENTOR [3], a recommender system for protection ser-
vices was introduced to help during the decision of which is the most suitable
protection for determined demands. This system provides a recommendation
engine that can recommend the most suitable protection based on a list of pa-
rameters and user demands. However, the system is still in its infancy and does
not provide user interfaces, or a catalog for protection providers to submit their
solutions. Also, the reputation of protections based on user feedback is still not
being considered during the recommendation process. In another work, [8] pro-
vides a recommender system to predict cyberattacks by identifying attack paths
and demonstrates how a recommendation method can be used to classify future
cyberattacks. [5] introduced an interactive user interface for security analysts
that recommends what data to protect, visualizes simulated protection impact,
and helps build protection plans. However, none of them supports the different
characteristics of DDoS attacks while offers intuitive interfaces for users to add
their demands and log files to receive recommendations.
ProtectDDoS: Trustworthy Offering and Recommendation of Protections 5
By using the concepts of Blockchain (BC) and Smart Contracts (SC), dif-
ferent solutions have been proposed to enables the validation of the integrity
and origin of solutions for different purposes. BC was initially conceived as a
distributed ledger to be the backbone of the Bitcoin cryptocurrency. However,
its capacity to provide an immutable, trustworthy, and decentralized collection
of records has attracted the attention of both industry and academia [14]. The
concept of SC is implemented by the second generation of BCs, such as Ethereum
and NEO. The fees involved in SC are lower than traditional systems that require
a trusted intermediary. In [11], for example, BUNKER, a BC-based marketplace
for Virtual Network Functions (VNFs), was introduced to provide immutable
and trusted information concerning VNF packages acquired by end-users. The
solution stores the hash of the packages in a BC to guarantee the integrity of the
VNF being acquired by end-users. This kind of feature can be useful for both
providers and users interested in protections since the integrity of the protection,
the provider’s identity as well as its reputation can be verified for any offered
solution before decide for one specific cybersecurity solution.
3ProtectDDoS Platform
ProtectDDoS allows users to describe their demands for protections in order
to obtain a proper level of protection against different types of DDoS attacks,
which facilitates the decision process, from an extensive list of options avail-
able, the most suitable protection. These protection services can be acquired
proactively before an attack happens or to react during an imminent attack.
Thus, ProtectDDoS offers mechanisms to support the decisions required during
cybersecurity planning and management. Besides that, protection providers can
announce their solutions to build a heterogeneous catalog of protections against
DDoS, thus achieving a broad audience of companies and users interested in con-
tract/acquire protections. Also, the ProtectDDoS allows, through a web-based
interface, the users to (i) upload fingerprints of DDoS attacks to find specific
protections, (ii) verify, supported by the BC, the integrity and origin of the in-
formation of different protections, (iii) receive the recommendation of the best
solution according to its demands, and (iv) provide feedback of the contracted
protections, thus supporting a reputation system for the protections available.
The ProtectDDoS ’s code is publicly available at [13].
3.1 Architecture
Figure 1 introduces the architecture of the ProtectDDoS and its main compo-
nents. The architecture is divided into three different layers: the (i) User Layer
provides the components required to actors interact with the ProtectDDoS and
the protections available through an intuitive and modern interface, the (ii) Data
Layer, which is in charge of the steps involved in process and handle information
related to the protections, as well as serving as a link for the upper and lower
layers, and the (iii) BC Layer, which consists of an SC running inside of the
6 Franco et al.
Ethereum BC containing useful information (e.g., hash and reputations of pro-
tections) to be used by the other layers, such as to verify the integrity services’
information or its developer. Also, the connection with MENTOR recommender
system is available by using an API introduced by MENTOR, which is fully
integrated with the ProtectDDoS architecture, thus allowing that calls can be
done to receive a recommendation of the best protection services according to
the previously filters and configurations defined by the user.
Catalog ManagerService Helper
Data Manager Blockchain
Connector
User Layer Data Layer
Smart Contract (SC)
Reputations
Verified
Providers
Web-based
Interface
User
Recommendation
Manager
Protections
Protection
Provider
MENTOR's API
Proof-of-
Feedback
RPC Server
Information
ProviderAddress
ProtectionHash
Blockchain Layer
Fig. 1: ProtectDDoS ’s Conceptual Architecture
In the User Layer, a web-based interface (i.e., dashboard) provides access
the catalog, details of protections, and the recommendation process. The Ser-
vice Helper plays a crucial role in the integration of the catalog and the rec-
ommendation process by applying the predefined filter on the whole dataset
of protections available, thus removing protections that are not suitable to the
user’s requirements. The Catalog Manager requests information from the Data
Layer to build the catalog of available protections, apply the filters, and send the
list of protections to start the recommendation process. Finally, the Recommen-
dation Manager is in charge of constructing the calls for the recommendation
API (i.e., MENTOR’s API). For that, this component transforms the user re-
quirements and information from the selected protections into a defined JSON
data structure [13] containing all relevant information for the recommendation.
The Data Layer contains the Protections database to store all information
of the protections available, such as developer, name, price, and types of at-
tacks supported. Also, a database is provided to store all of the log files (e.g.,
pcap) containing the information regarding the contracted protection perfor-
mance, which helps during the audition and validation of bad or good feedback
provided by users. This database is managed by the Data Manager, which is the
interface with the Data Layer and is in charge of process and answer to requests
for information. Furthermore, the BC Connector is an adaptor implemented to
enable the communication with the SC running inside of the BC. The BC Con-
nector performs the calls to interact with the SC (e.g., verify the protection hash
ProtectDDoS: Trustworthy Offering and Recommendation of Protections 7
or validate the provider address) by sending BC transactions thought a Remote
Procedure Call (RPC) server provided by the BC.
Finally, in the BC Layer, the SC is deployed to store a list of verified providers
based on their address on the BC, the reputations of each protection according
to the users’ feedback, the hash of the proof-of-feedback files, and the hash of the
protection associated with the address of the provider that submitted the service.
It is worth reinforcing that all of this information is immutable and tamper-proof,
which allows any interested party to audit and trust in the information following
the whole history of the stored information.
3.2 Workflow
By accessing the ProtectDDoS, users interested in obtaining protection can verify
the available protection services in a catalog and apply filters to select a set
of characteristics that satisfies his/her demands, such as a maximum price or
protection against a specific type of DDoS attack (cf Figure 2). For that, the
user can select a determined attack-type from a list of attacks supported or also
upload a file containing the fingerprints of a DDoS attack of which protection is
required. Alternatively, a fingerprint can be used as input to the ProtectDDoS ,
which can process to filter a list of protections suitable for such a user’s demands.
Such a list can be sent to the MENTOR [3] recommender system, through a
provided API, in order to receive, as a response, the best protection selected by
the recommendation process implemented in MENTOR.
Also, providers of protection services can access the web-based interface and
store new protections to be available in the catalog. The process of uploading a
new protection service comprises of two essential steps:
1. service’s information is hashed, using the SHA256 algorithm, and then sub-
sequently submitted onto the BC.
2. upon successful storage, the service provider’s address and the transaction
hash are retrieved by the RPC server and stored off-chain.
The first operation is handled using an SC and will cost an amount of Ether
as a fee to be completed (cf. Section V). As the costs to store all information
would be high, only the hash of the service information is stored. When inter-
acting with an SC, an Ethereum account is required. Ethereum was defined to
be used because of its popularity and simplified way to build SCs. The second
operation enables to retrieve the hash of any protection from the BC, thus of-
fering the possibility to any user checks whether the service’s information has
been compromised or if the provider is not verified. Hence, the service hash is
required, and a specific function in the SC needs to be invoked.
Also, a DDoS fingerprint is supported by the ProtectDDoS as defined in the
DDoSDB platform [10]. Parameters that can be configured for the catalog filter
or recommendation of protections are: (i) Service Type, which can be reactive
or proactive, (ii) Attack type (e.g., SYN Flood or DNS Amplification) defined
directly from a list or identified by a using a fingerprint filed optionally uploaded
8 Franco et al.
Dashboard
Service Catalog No
Blockchain Connector
Fetch Data
Off-chain
Backend
Smart Contract
Yes
No
Locally
stored?
Yes
Match
Selection?
Requirements Fingerprint
Yes
No
Contract
Service?
User Layer
Data Layer
Blockchain Layer
Fig. 2: ProtectDDoS ’s Workflow
by the user, (iii) Coverage Region to indicate the location (e.g., continents,
countries, or even cities) where cloud-based protection has to be deployed, (iv)
Deployment time, which represents how long (e.g., in seconds, minutes, or hours)
it may take until the protection is deployed and active, and (v) the budget
available by the user to pay for protection.
4 Proof-of-Concept and Case Study
A Proof-of-Concept (PoC) was implemented to showcase the ProtectDDoS . The
source code is available online [13]. The User Layer was implemented using Re-
actJS 16.8, a popular JavaScript library for building user interfaces, in its latest
version. This library facilitates the overall process of develop the user interface
components, using the JSX syntax extension. It also boosts productivity and
facilitates further maintenance. The Service Helper,Catalog, and Recommenda-
tion Manager were implemented using Python 3.6.5, while the MENTOR’s API
is implemented using Flask 1.0.2. SQLite 3.30.1 was defined as the database to
store information at the Data Layer, and its connection is implemented by using
the SQLAlchemy 1.3, which is an open-source SQL toolkit and object-relation
mapper. For the BC layer, Ethereum was defined as the BC technology to be
used. For the SC development, Solidity, a well-known contract-based program-
ming language for Ethereum, was used.
ProtectDDoS: Trustworthy Offering and Recommendation of Protections 9
A case study is conducted to provide evidence of the feasibility and the
usability of ProtectDDoS . For this case is considered a scenario where (i) a
protection provider wants to submit a new protection to be listed in the platform
and (ii) a user wants to contract reactive DDoS protection against an application
layer attack that is affecting his/her infrastructure. However, the user has a
budget of USD 5000. The protection has to be deployed in a server running in
Europe to ensure the legal aspects of the General Data Protection Regulation
(GDPR). The interface to configure such requirements is publicly available at
[13].
Firstly, protections providers have to submit new services to be listed on
the platform, populating the catalog with different protections against DDoS
attacks. This is done through a Service Upload Tab. Each protection service
comprises two parts: General Information and Technical Details. The generated
hash and the provider account address is then stored in the BC for further val-
idations. The Metamask extension enables users/providers to send transactions
(e.g., hash of protections and feedback) to be stored on the BC. The costs in-
volved in this interaction are discussed in Section V.
After populating the database, protection services are made available in the
platform’s catalog for the user. After configuring their demands, the user can
upload a fingerprint of the DDoS attack to filter services that are more suitable
to protect against this attack. This is done automatically by ProtectDDoS, which
processes the fingerprint and extracts useful information that provides evidence
of the attack type. After submitting their demands, the filter is applied, and a list
of protections that are suitable for this case is available. This list is then sent to
the MENTOR recommender system in order to receive an ordered list with the
most recommended protection on the top. Based on this list containing suitable
services, for example, the recommendation engine can decide that the best option
is a service with deployment time in seconds with features to mitigate this type
of attack, with the cost of USD 2400. Although other solutions are cheaper, they
are providing different features that are not ideal, taking into account the user
demands and fingerprint of the attack being provided.
The user can verify whether the offered protection service has been manipu-
lated or not, i.e., validate the integrity and origin of the protection information
being provided. Thus, the Service Hash and Transaction hash are required. This
information can be easily obtained by clicking on the See More button of a spe-
cific service. At this point, the user could either copy the service hash or have
a closer look at the transaction itself by clicking on the transaction hash. If the
user decides to go for the second option, an Etherscan page will be opened, which
will, in turn, provide further details regarding the transaction itself. Otherwise, if
the user decides to go for the first option, an Ethereum account and the browser
extension Metamask will be required to execute the validation. Thus, through
the Verify Page, the user can fastly validate a particular service by its hash. In
the verification interface, the protection is verified, meaning that this particular
service is stored onto the BC and the integrity of the service is ensured (i.e.,
the information regarding the protection was not modified after its submission).
10 Franco et al.
However, in this case, the provider linked to this service hash is highlighted as
untrusted, meaning that the real identity of this provider can not be ensured.
Furthermore, the user can access the web-based interface and provide feed-
back regarding a previously contracted protection, which includes a rating from
zero to three, comments, and a log file (e.g., pcap format) containing the proof-
of-feedback. This proof-of-feedback is stored in the platform database and its
hash stored in the BC in order to ensure that changes in the log can be easily
identified during further auditions or analysis. By using such a reputation, the
platform can be configured to remove from the recommendation process or even
from the catalog protections that are presenting misbehavior, such as not de-
livering the promised functionalities or with a bad performance to mitigate the
specified DDoS attack.
5 Functional Evaluation
Despite the benefits introduced by the platform, drawbacks have to be considered
when using a public BC. Two crucial dimensions related to Costs in 5.1 and
Security in 5.2 are herein discussed.
5.1 Costs
The drawbacks concern additional fees and time to store information. The fees
are not high but should be considered to store, for instance, a large number of
protections and its reputations. An analysis of the current state of Ethereum
BC was conducted to investigate costs. In the Ethereum BC, there are fees for
every transaction that requires to store data in an SC. This fee is described as
Gas, which is the pricing value paid for the miners to successfully conduct to
execute a transaction or execute a contract on the BC. This fee is paid using
Ether (ETH), which is the cryptocurrency used in the Ethereum BC. Besides
the ETH, fees can also be represented as a sub-unit called Gwei, which is used
to describe small amounts of ETH since 1 ETH is 1 billion of Gwei. For the
costs analysis, the price of 1 ETH is equal to USD 144, as of the quotation in
December 2019.
To execute the functionalities provided by the SC, the contract needs to be
first compiled and successively deployed to the desired network. When doing
so, the owner of the SC will be confronted with costs that occur only once,
i.e., , during deployment. The deployment of the latest, fully working SC at the
time of writing generated a total cost of 0.01041256 ETH, which amounts to
USD 1.50. This cost can be broken into two main components: 520,628 units
of Gas used to deploy the actual contract and 20 Gwei gas price paid per unit.
Important to notice is that whenever the SC gets updated, the owner will have
to deploy it again, and if a new feature is added to the SC, the cost will increase.
In addition, then there is a cost of 0.0076 ETH (USD 1.10) to add a new
provider as Verified. Such costs can be paid by the owner of the catalog or even
by providers that want to announce on the platform.
ProtectDDoS: Trustworthy Offering and Recommendation of Protections 11
When designing the functionality of storing a protection service to the BC,
two possible approaches were investigated: (i) store the full protection service
information or (ii) store only a hash of the protection. Although the approach
(i) enables users to, eventually, verify every characteristic of the protection,
the costs of writing large amounts of data on the BC increase exponentially.
Therefore, the approach (ii) is a more suitable alternative in terms of costs since
the amount to be paid to store a new protection service is lower than writing the
full protection’s information. Upon submission, the provider would pay 0.002154
ETH (USD 0.31 ) to store the generated hash and its address. In case a new
account address for the generated hash has been stored, the system would allow
to store and submit the service again with a cost of 0.001082 ETH (USD
0.16 ). Also, there are costs concerning the storage of ratings provided by the
user and the reputation of each protection service. This cost has to be paid by
the SC owner (i.e., the platform) not to encumber the user with this fee. It is
important to mention that there are no fees to retrieve information from the SC.
Hence, the functions verifyService() and getReputation() does not have any cost
involved.
5.2 Security
One of the main characteristics of blockchain is its ability to unearth, causing
applications to remove trusted third parties while trust levels can be relatively
increased by the transparency and immutability of the process. In the context
of security applications such as the ProtectDDoS, there is an extra concern with
the exposure of confidential data, as well as the handling of protection service
requirements. Therefore, it is important to consider the solution’s deployment
approaches in order to ensure that the stored information is not exposed or tam-
pered with. In this sense, a possible deployment absorbing requests from multiple
clients (i.e., on external premises) implies a centralization process, which is just
the opposite of the decentralization proposed in the use of blockchain (cf. Figure
3).
ProtecDDoS
Internal
Premises
Customer
or
Service
Ethereum
External
Premises
Fig. 3: ProtectDDoS ’s Deployment
In this sense, Figure 3 presents an ideal implementation approach of the
service as a decentralized application and maintained in the internal premises.
Thus, ProtectDDoS operates as a decentralized application where public data on
12 Franco et al.
protection services are announced, and the instance in the internal premises can
act as a reverse proxy selecting, among the services advertised, which ones have
the desired characteristics. Similarly, protection service advertisers also operate
instances of ProtectDDoS on internal premises. Henceforth, aspects of confiden-
tiality and integrity related to the security needs of customers are maintained on
internal premises, as well as the characteristics of the service advertised cannot
be tampered with.
6 Summary and Future work
This paper presented ProtectDDoS a web-based platform that introduces a trust-
worthy catalog and recommendation of protections against DDoS attacks. Pro-
tectDDoS builds on BC-based SCs to allows the validation of the integrity and
the origin (i.e., provider) of protections available. Also, by using SCs, the reputa-
tion of protections can be stored in a tamper-proof way. Moreover, ProtectDDoS
explores the recommendation of protections by integrating with a cybersecu-
rity recommender system, thus allowing users to receive recommendations of
the best protections according to specific demands. ProtectDDoS also allows,
through a user-friendly web interface, the upload of DDoS attacks fingerprints
and the configuration of different parameters to specify user’s specific demands
and characteristics of attacks in order to find the most suitable protection against
a DDoS attack. The feasibility of the solution was evaluated in a prototype im-
plementation and the dedicated case study discussion. Also, an evaluation was
provided in order to measure the benefits and drawbacks of blockchains (e.g.,
additional costs).
Future work includes (i) the support to lease of protections directly from the
platform by using SCs, thus storing and enforcing automatically the agreements
between providers and users, (ii) the development of mechanisms to process
and extract meaningful information from different configurations and log files
provided by users, thus extending the information supported by ProtectDDoS,
and (iii) the proposal of DDoS visualizations to help users to understand attack
behaviors as well as performance of contracted protections. Also, an in-depth
analysis of the recommendation process and the performance of protections rec-
ommended for each DDoS attack has to be conducted. Furthermore, the inte-
gration with cybersecurity economics-aware solutions [9] might be done in order
to provide a more accurate and cost-effective offering and recommendation of
protections.
Acknowledgements
This paper was supported partially by (a) the University of Z¨urich UZH, Switzer-
land and (b) the European Union’s Horizon 2020 Research and Innovation Pro-
gram under Grant Agreement No. 830927, the CONCORDIA project.
ProtectDDoS: Trustworthy Offering and Recommendation of Protections 13
References
1. Abhishta, R. Joosten, L. J. Nieuwenhuis: Comparing Alternatives to Measure
the Impact of DDoS Attack Announcements on Target Stock Prices. Journal of
Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
(JoWUA) 8(4), 1–18, December 2017
2. Cybertango: The Cybersecurity Directory - DDoS Protection Companies, 2019,
https://www.cybertango.io/cybersecurity-vendors/DDoS, Last visit May 1,
2020
3. M. F. Franco, B. Rodrigues, B. Stiller: MENTOR: The Design and Evaluation of
a Protection Services Recommender System. In: 15th International Conference on
Network and Service Management (CNSM 2019). Halifax, Canada, October 2019,
pp. 1–7
4. B. Hellard: DDoS attacks could cost the UK £1bn, 2019, https://www.itpro.
co.uk/security/33279/ddos-attacks-could-cost- the-uk-1bn, Last visit May
1, 2020
5. T. Li, G. Convertino, R. K. Tayi, S. Kazerooni: What Data Should I Protect?:
Recommender and Planning Support for Data Security Analysts. In: 24th Inter-
national Conference on Intelligent User Interfaces (IUI 2019). ACM, Los Angeles,
USA, March 2019, pp. 286–297
6. S. Mansfield-Devine: The Growth and Evolution of DDoS. Network Security pp.
13–20, October 2015
7. T. Moore: Introducing the Economics of Cybersecurity: Principles and Policy Op-
tions. In: Workshop on Deterring CyberAttacks. Washington, DC, USA, April
2010, pp. 1–21
8. N. Polatidis, E. Pimenidis, M. Pavlidis, H. Mouratidis: Recommender Systems
Meeting Security: From Product Recommendation to Cyber-Attack Prediction.
In: G. Boracchi, L. Iliadis, C. Jayne, A. Likas (eds.) Engineering Applications of
Neural Networks. Springer, Athens, Greece, August 2017, pp. 508–519
9. B. Rodrigues, M. F. Franco, G. Paranghi, B. Stiller: SEConomy: A Framework for
the Economic Assessment of Cybersecurity . In: 16th International Conference on
the Economics of Grids, Clouds, Systems, and Services (GECON 2019). Springer,
Leeds, UK, September 2019, pp. 1–9
10. J. Santanna, K. van Hove: DDoSDB: Collecting and Sharing information of DDoS
attacks, 2019, https://ddosdb.org/, Last visit May 1, 2020
11. E. Scheid, M. Keller, M. F. Franco, B. Stiller: BUNKER: a Blockchain-based
trUsted VNF pacKagE Repository. In: 16th International Conference on the Eco-
nomics of Grids, Clouds, Systems, and Services (GECON 2019). Springer, Leeds,
UK, September 2019, pp. 1–8
12. W. Sonnenreich, J. Albanese, B. Stout, et al.: Return On Security Investment
(ROSI)- A Practical Quantitative Model. Journal of Research and practice in In-
formation Technology, vol. 38, 45–52, 2006
13. E. Sula, M. Franco: Web-based Interface for the Recommendation of DDoS Attack
Protections, 2019, https://gitlab.ifi.uzh.ch/franco/ddosrecommendation,
Last visit May 1, 2020
14. T. Bocek and B. Stiller: Smart Contracts - Blockchains in the Wings. Digital
Marketplaces Unleashed, Heidelberg, Germany, January 2017
... The Data Layer and the User Layer communicate through the Communication API. An Integration API allows for external solutions to request information and reuse available miners, which provides integration options, such as for systems to recommend or offer protections against cyberattacks [9], [10]. ...
Chapter
Full-text available
Cybersecurity concerns are one of the significant side effects of an increasingly interconnected world, which inevitably put economic factors into perspective, either directly or indirectly. In this context, it is imperative to understand the significant dependencies between complex and distributed systems (e.g., supply-chain), as well as security and safety risks associated with each actor. This paper proposes SEConomy, a strictly step-based framework to measure economic impact of cybersecurity activities in a distributed ecosystem with several actors. Through the mapping of actors, responsibilities, inter-dependencies, and risks, it is possible to develop specific economic models, which can provide in a combined manner an accurate picture of cybersecurity economic impacts.
Conference Paper
Full-text available
Cyberattacks are the cause of several damages on governments and companies in the last years. Such damage includes not only leaks of sensitive information, but also economic loss due to downtime of services. The security market size worth billions of dollars, which represents investments to acquire protection services and training response teams to operate such services, determines a considerable part of the investment in technologies around the world. Although a vast number of protection services are available, it is neither trivial for network operators nor end-users to choose one of them in order to prevent or mitigate an imminent attack. As the next-generation cybersecurity solutions are on the horizon, systems that simplify their adoption are still required in support of security management tasks. Thus, this paper introduces MENTOR, a support tool for cyber-security, focusing on the recommendation of protection services. MENTOR is able to (a) to deal with different demands from the user and (b) to recommend the adequate protection service in order to provide a proper level of cybersecurity in different scenarios. Four similarity measurements are implemented in order to prove the feasibility of the MENTOR's engine. An evaluation determines the performance and accuracy of each measurement used during the recommendation process.
Conference Paper
Full-text available
Major breaches of sensitive company data, as for Facebook's 50 million user accounts in 2018 or Equifax's 143 million user accounts in 2017, are showing the limitations of reactive data security technologies. Companies and government organizations are turning to proactive data security technologies that secure sensitive data at source. However, data security analysts still face two fundamental challenges in data protection decisions: 1) the information overload from the growing number of data repositories and protection techniques to consider; 2) the optimization of protection plans given the current goals and available resources in the organization. In this work, we propose an intelligent user interface for security analysts that recommends what data to protect, visualizes simulated protection impact, and helps build protection plans. In a domain with limited access to expert users and practices, we elicited user requirements from security analysts in industry and modeled data risks based on architectural and conceptual attributes. Our preliminary evaluation suggests that the design improves understanding and trust of the recommended protections and helps convert risk information in protection plans.
Article
Full-text available
Distributed denial of service (DDoS) attacks are responsible for creating unavailability of online resources. Botnets based on internet of things (IOT) devices are now being used to conduct DDoS attacks. The estimation of direct and indirect economic damages caused by these attacks is a complex problem. In this article we analyze the impact of 45 different DDoS attack announcements on victim firm’s stock prices using three different approaches and compare the results. We show that the assumption of cumulative abnormal returns being normally distributed leads to overestimation/underestimation of the impact. We solve this problem by using an empirical distribution of cumulative abnormal returns for hypothesis testing. Finally, we demonstrate the impact of DDoS attack announcements in each of the cases. © 2017, Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. All rights reserved.
Conference Paper
Full-text available
Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks. The proposed method has been experimentally evaluated and it is shown that it is both practical and effective.
Chapter
Current projects applying blockchain technology to enhance the trust of NFV environments do not consider the VNF repository. However, the blockchain’s properties can enhance trust by allowing to verify a VNF package’s integrity without relying (a) on a Trusted Third Party (TTP) for remote attestation or (b) a secure database. This paper presents BUNKER, a Blockchain-based trUsted VNF packagE Repository, intended to be integrated with traditional database-based package verification environments, acting as a trusted repository containing VNF package information. Moreover, BUNKER allows users to acquire VNFs without the need of a TTP using an Ethereum Smart Contract (SC). The SC automatically transfers license fees to the vendor once a VNF is acquired, and sends the VNF package’s link to the buyer before verifying its integrity.
Chapter
In recent years, electronic contracts have gained attention, especially in the context of the blockchain technology. While public blockchains are considered secure, legally binding under certain circumstances, and without any centralized control, they are applicable to a wide range of application domains, such as smart contracts, public registries, registry of deeds, or virtual organizations. As one of the most prominent blockchain examples, the Bitcoin system has reached large public, financial industry-related, and research interest. Another prominent blockchain example, Ethereum, which is considered a general approach for smart contracts, has taken off too. Nevertheless, various different set of functions, applications, and stakeholders are involved in this smart contract arena. These are highlighted and put into interrelated technical, economic, and legal perspectives.
Article
Distributed Denial of Service (DDoS) attacks show no signs of going away. In fact, they are finding popularity with new groups of users and moving away from the low-grade extortion carried out by small groups of cyber-criminals to being deployed as political weapons and even, it would appear, by individuals bearing grudges. And when they are exploited for the original purpose - of blackmail - DDoS attacks are becoming more complex and sophisticated.
Conference Paper
Organizations need practical security benchmarking tools in order to plan effective security strategies. This paper explores a number of techniques that can be used to measure security within an organization. It proposes a new benchmarking methodology that produces results that tire Of strategic importance to both decision makers and technology implementers. The approach taken reflects a work-in-progress that is a combination of practical experience and direct research.