A New Distributed MIKEY Mode to Secure e-Health Applications

Mohammed Riyadh Abdmeziem

, Djamel Tandjaoui

and Imed Romdhani

LSI, USTHB: Bab Ezzouar, Algiers, Algeria

Security Division, CERIST: Ben Aknoun, Algiers, Algeria

School of Computing, Edinburgh Napier University, Edinburgh, U.K.

Keywords

Internet of Things (IoT), e-Health, Key Management Protocols, MIKEY, Security, Privacy, Cooperation.

Abstract

Securing e-health applications in the context of Internet of Things (IoT) is challenging. Indeed, resources

scarcity in such environment hinders the implementation of existing standard based protocols. Among these

protocols, MIKEY (Multimedia Internet KEYing) aims at establishing security credentials between two com-

municating entities. However, the existing MIKEY modes fail to meet IoT specificities. In particular, the

pre-shared key mode is energy efficient, but suffers from severe scalability issues. On the other hand, asym-

metric modes such as the public key mode are scalable, but are highly resource consuming. To address this

issue, we combine two previously proposed approaches to introduce a new distributed MIKEY mode. In-

deed, relying on a cooperative approach, a set of third parties is used to discharge the constrained nodes from

heavy computational operations. Doing so, the pre-shared mode is used in the constrained part of the network,

while the public key mode is used in the unconstrained part of the network. Preliminary results show that our

proposed mode is energy preserving whereas its security properties are kept safe.

INTRODUCTION

Internet of Things (IoT) is based on the pervasive

presence around us of various wireless technologies

such as Radio-Frequency IDentification (RFID) tags,

sensors, actuators and mobile phones, in which com-

puting and communication systems are seamlessly

embedded (Atzori et al., 2010). It is considered as

one of the most important communication develop-

ment in recent years. It makes our everyday ob-

jects (e.g. health sensors, industrial equipements, ve-

hicules, clothes, etc.) connected to each other and to

the Internet (Abdmeziem et al., 2016). Among the

different applications of IoT, e-health is gaining more

and more attention in the IoT world. In fact, popula-

tion ageing and the increase of survival chances from

disabling accidents lead to an increased demand for

continuous health care and monitoring (Dohr et al.,

2010).

Compared to other IoT applications, e-health ap-

plications are more vulnerable to attacks due to the

high sensitivity of the generated data (Li and Lou,

2010). This data is private in nature, and any secu-

rity vulnerability regarding the confidentiality would

seriously repulse patients from adopting e-health ap-

plications. For example, personal health

information such as an early stage of pregnancy or

details

of certain medical conditions must be kept

confidential. The leaked data can be used for illegal

activities.

In addition, any malicious alteration of

health records

would engender dramatic

consequences, as it could

trigger wrong medical

prescriptions, or delay emergency interventions.

Securing data communications

for e-health

applications passes inevitably through

key

management protocols. They are in charge of de-

livering security credentials to the different involved

entities. These credentials are used to make sure that

only authorized entities can access and modify data.

This is particularly relevant in an e-health scenario

considering its sensitivity.

MIKEY is a key management protocol that aims

to provide security associations to be used as an input

for security protocols. The main motivation behind

its design is to ensure end to end security while re-

maining simple and efficient (low-latency, low band-

width consumption, low computational workload,

small code size, and minimum number of roundtrips)

(Arkko et al., 2004). The flexibility of MIKEY allows

the designers to leverage upon several modes accord-

ing to the specificities of the network scenario. Thus,

MIKEY seems to be the adequate protocol that can

Abdmeziem, M., Tandjaoui, D. and Romdhani, I.

A New Distributed MIKEY Mode to Secure e-Health Applications.

DOI: 10.5220/0005945300880095

In Proceedings of the International Conference on Internet of Things and Big Data (IoTBD 2016), pages 88-95

ISBN: 978-989-758-183-0

be extended to ensure secure communications in IoT.

However, MIKEY various modes have not originally

been designed to be implemented in constrained en-

vironments with power and computation limitations,

weak reliability of wireless links and high scalability

requirements.

In this paper, we introduce an extension of our

two previous approches (Abdmeziem and Tandjaoui,

2014) (Abdmeziem and Tandjaoui, 2015) to propose

a new standard-based cooperative key management

scheme. In fact, we design a new distributed hybrid

mode for MIKEY protocol combining the Pre-shared

key mode with the Public key mode. To do so, we

divide our network model into two segments. The

first segment covers the communication channel be-

tween the constrained nodes and a set of third parties,

to which the heavy computational operations are of-

floaded. To lighten the overhead on constrained en-

tities, only symmetric operations are used (i.e. pre-

shared key mode). The second segment covers the

communication channel between the third parties and

any remote entity to which gathered data is transmit-

ted. In this segment, asymmetric operations are used

(i.e. public key mode).

The proposed distributed hybrid mode allows us

to mitigate the disadvantages of both Pre-shared key,

and the Public Key modes while benefiting from their

advantages. Indeed, the constrained nodes do not suf-

fer from the scalability issue, as they can establish a

secret with any remote entity without having a previ-

ously shared knowledge. In the same time, they are

only involved in simple operations, which are com-

pliant with e-health applications limitations.

As a first assessment of our approach, we con-

ducted a theoretical analysis of its security properties.

Furthermore, we formally validated the analysis using

Avispa tool (Moedersheim and Drielsma, 2003). The

obtained results showed that our approach keeps the

security properties safe while being energy efficient.

The remaining of the paper is organized as fol-

lows. Section 2 provides a general overview on

MIKEY protocol. In section 3, we introduce our new

MIKEY mode. Firstly, we present our network ar-

chitecture. Then, we set our assumptions, before de-

tailing the protocol’s functioning. In section 4, we

analyze the security properties of our proposed mode.

Existing security approaches are reviewed in section

5. Section 6 concludes the paper and sets our future

directions.

BACKGROUND

In the following, we provide the necessary back-

ground on MIKEY’s functioning (Arkko et al., 2004).

Table 1: Terminology table.

Notation

Descripti

Initiator

Responder

{

d at a

}

Data encrypted with key k

[d at a] Optional data

PSK

Pre-Shared key

MAC

Message Authentication Code

Public Key of x

CERT

Certificate of x

TEK

Traffic Encryption Key

TGK

TEK Generation Key

RAND

Fresh value used for key

generation

auth key Authentication key

encr key Encryption key

HDR

MIKEY header

Timestamp

Identity of x

Security policies

KEMAC

{TGK}

encr key/envelopekey

||MAC

PKE

{envelopekey}

PK R

Sign

Signature of x

Figure 1: Pre-shared key mode signaling flow.

Figure 2: Public key mode signaling flow.

A New Distributed MIKEY Mode to Secure e-Health Applications

MIKEY considers two entities that aim to

establish a

shared secret. One of the two entities

assumes the Ini-

tiator role, whereas the second one

assumes the Re-

sponder role. The key distribution

modes are defined

as follows (the different used

notations are described

in Table 1):

Pre-shared Key Mode: in this mode, both the Ini-

tiator and the Responder share a PSK from which

two keys are derived, encr key and auth key. An ini-

tialisation phase where the key is distributed is as-

sumed. To establish a session, the Initiator randomly

generates a TGK, and sends it to the Responder as

part of the first message (i.e. I MESSAGE). This

latter is replay protected with timestamps, encrypted

with encr key and authenticated through a MAC us-

ing auth key. An optional verification response (i.e.

R MESSAGE) from the Responder provides mutual

authentication. R MESSAGE contains a MAC com-

puted upon both Initiator and Responder identities,

and the same timestamp contained in I MESSAGE

using auth key (Fig. 1).

In the pre-shared key mode, only symmetric oper-

ations are involved. Hence, this mode fits well with

the IoT constrained environement, as it can be run

with limited energy and power resources. Neverthe-

less, this mode suffers from a severe scalability issue.

In fact, a pre-establishment phase is required where a

shared key is set between the involved parties.

Public Key Mode: in this mode, the Initiator trans-

mits the genrated TGK based on an ”envelope key”

approach. The Initiator encrypts and authenticates

the TGK using a randomly/pseudo-randomly chosen

envelope key, and sends it as part of I MESSAGE. In

addition, it includes the envelope key encrypted with

the Responder public key PK

. According to (Arkko

et al., 2004), the mandatory asymmetric primitive to

implement is RSA (Rivest et al., 1978). In case where

the Responder owns several public keys, the Initia-

tor specifies the used key in the optional CHASH pa-

rameter. Both ID

and CERT

are also optional. It

is worth mentioning that I MESSAGE is signed us-

ing PK

, and replay protected with timestamps. Sim-

ilar to the Pre-shared key mode, an optional response

message (R MESSAGE) ensures mutual authentica-

tion (Fig. 2).

The Public key mode is based on asymmetric

primitives (i.e. RSA). These latter use complex ex-

ponential operations, which prove to be difficult to

run on constrained devices. On the other side, this

mode does not require from the involved entities to

pre-share credentials. Thus, two entities with no pre-

vious shared knowledge can establish a secure com-

munication channel.

In addition to the two previous modes, a third

mode called ”Diffie-Hellman mode” is defined. This

latter is mainly based on the Diffie-Hellman key ex-

change protocol. This mode has a higher computa-

tional and communication overhead compared to the

public key and the Pre-shared modes. Due to its in-

adequacy with our constrained e-health scenario, this

mode is ruled out.

CONTRIBUTION

In this section, we present our new distributed mode

for MIKEY protocol. Firstly, we introduce our e-

health network architecture. Secondly, we define a

set of assumptions before detailing the different ex-

changed messages.

3.1 NeTwork Architecture

We consider an end to end communication channel

between smart objects (i.e. sensor nodes) and any

remote server. This choice is motivated by the high

sensitivity of gathered data in e-health applications.

Hence, key management protocols are required be-

tween the two entities to secure their communica-

tions. These protocols have to deal with the resources

capabilities of the involved entities, along with the

fact that no prior knowledge is established between

them.

IP-enabled smart objects are in charge of sensing

health related data (e.g. blood pressure, blood glu-

cose level, temperature level, etc.). They are planted

in the human body. Gathered data is transmitted to re-

mote entities that are in charge of the processing and

analysis. In our approach, we consider four main el-

ements: the mobile and contextual sensors, the third

parties, the remote server and the certification author-

ity. (Fig. 3).

Mobile and Contextual Sensor (Smart Object): the

sensors are planted in, on, or around a human

body to collect health-related data (e.g. blood

pressure, blood glucose level, temperature level,

etc.).

Third Party: compared to the standard MIKEY

modes, the third parties represent an additional

component in our proposed hybrid mode. A third

party could be any entity that is able to perform

high consuming computations.

Remote Entity: the remote entity receives the

gathered data for further processing. A remote

server could be used by caregiver services in or-

der to take appropriate decisions according to pa-

tient’s data.

IoTBD 2016 - International Conference on Internet of Things and Big Data

Figure 3: Mikey hybrid mode: Network architecture.

Certification Authority: the certification authority

is required to guarantee authentication between

the third parties and the remote server by deliv-

ering valid and authenticated certificates.

The network is thus heterogeneous combining

nodes

with various capabilities both in terms of com-

puting power and energy resources. Smart objects

have limited computational power, memory and en-

ergy resources. They are unable to perform public

key cryptographic operations. However, the third par-

ties and the remote server are equiped with high en-

ergy, computing power and storage capabilities. They

can take the form of a server hardware or being dis-

tributed in a Cloud infrastructure with flexible re-

sources (Chang and Ramachandran, 2016).

The mapping with MIKEY concepts is defined as

follows.

The Initiator role is mapped with the smart object

(also designated as constrained node)

The Responder role is mapped with the remote en-

tity. This latter can be set in hospitals automati-

cally triggering an exchange in order to check on

patient’s vital signs.

3.2 Assumptions

Before presenting the details of our protocol, we set

the following assumptions:

Sensor nodes are able to perform symmetric en-

cryption. Both third parties and the remote server

are able to perform asymmetric cryptographic op-

erations.

The third parties are not necessarily trusted.

The certification authority is a trusted entity. It de-

livers authenticated cryptographic credentials to

the third parties and to the remote server.

Each sensor node is able to keep a list of remote

third parties. This list is pre-established during an

initialization phase.

Each sensor node shares a PSK with each third

party.

3.3 Message Exchanges

In our proposed mode, the network is divided into two

segments. The first segment is defined by the commu-

nication channel linking the constrained nodes to the

third parties. This segment involves the constrained

part of our network model. Hence, we propose to

consider using the Pre-shared key mode of MIKEY.

The second segment is defined by the communica-

tion channel linking the third parties to the remote

server. This segment does not suffer from resources

constraints, thus, we propose to consider using the

Public key mode of MIKEY.

After an initialization phase where each con-

strained node is pre-loaded with the identities of a set

of third parties, along with the different PSK, our pro-

posed MIKEY mode proceeds with successive mes-

sages. Table. 1 summarizes the notations used, and

Fig. 4 illustrates the signaling flow. To remain stan-

dard compliant, the messages headers, along with var-

ious message parameters are kept unchanged (RFC

3830 (Arkko et al., 2004)). In the following, we detail

the different exchanged messages.

A New Distributed MIKEY Mode to Secure e-Health Applications

I TPi MESSAGE: the Initiator randomly gener-

ates a secret TGK, which will be used later to

further derive keying materials at both I and R

sides. The TGK is split into n parts TGK

TGK

, TGK

. Each part is sent to the

appropriate TP

in I TPi MESSAGE. The

message is replay pro-

tected with timestamps,

encrypted and authen-

ticated using the pre shared PSK. The general

structure of the message is as follows.

∀i ∈ { 1, n} {HDR, T, RAND,[ID

], [ID

SP}

PSK

, KEMAC

Because wireless connection is the main media in

e-health applications, and in IoT in general, I ap-

plies an error redundancy scheme to the generated

TGK. The aim is to enable R retrieving the secret

without requiring the reception of all the pack-

ets, in case where some of them were lost during

the transmission process. For instance, the widely

used Reed-Solomon scheme can be applied (Reed

and Solomon, 1960).

TPi I MESSAGE:

upon receiving

I TPi

MESSAGE, each TP

authenticates and

decrypts

the received message using its corresponding

PSK. An optional verification response

sent from

to I provides mutual authentication.

The

structure of the message is as follows.

∀i ∈ {1, n} {HDR, T,[ID

]}

PSK

TPi R MESSAGE: after having properly authen-

ticated the received I TPi MESSAGE, TP

ran-

domly generates an envelope key. This lat-

ter is

used to encrypt and authenticate the

received

TGK

part, which is included in

TPi R

MESSAGE. The envelope key is encrypted

with

the public key of R and included in the mes-

sage. In addition, TP

’s signature that covers all

the fields of the message is also included. The

message is then sent to R. The structure of the

message is as follows.

∀i ∈ { 1, n} {HDR, T, RAND,[ID

], [CERT

[ID

], SP, KEMAC

[CHASH], PKE}

, SIGN

R TPi MESSAGE: upon successful authentica-

tion and decryption of TPi R MESSAGE by R, the

TGK is retrieved. In fact, after having received

enough packets containing the different TGK

, R

reconstructs the original TGK. An optional veri-

fication response sent from R to TP

provides mu-

tual authentication. The structure of the message

is as follows.

∀i ∈ {1, n} {HDR, T,[ID

]}

R I MESSAGE: using the established TGK, R

encrypts and authenticates a verification message

(i.e. R I MESSAGE). This latter is sent to I,

which authenticates the received message. A suc-

cessful authentication is considered as a proof of

R’s knowledge of TGK. It is worth noting that

I MESSAGE is optional and only sent if ID

has

been included in the different exchanges. The

structure of the message is as follows.

{

HDR

[

]

}

TEK

The reconstructed TGK is used to derive further

keying materials. The derivation process is de-

tailed in MIKEY RFC3830(Arkko et al., 2004).

Both I and R are then able to derive state connec-

tion

keys for encryption and authentication of the

exchanged data. A secure end to end channel is

hence created between highly constrained sensors

and

remote unconstrained servers. Our proposed

mode

takes advantage of both the Pre-shared and

Public-key

modes, while limiting their disadvan-

tages.

4 SECURITY ANALYSIS

4.1 Key Exchange Properties

In this section, we briefly analyze the security fea-

tures of our proposed mode based on the proprieties

presented in (Roman et al., 2011). For the following

discussion, we consider our communication channel

split into two segments: Seg1) from I to the TP

and

Seg2) from the TP

to R (see Fig. 3)

Confidentiality: regarding Seg1, the exchanged

messages between I and the different TP

are en-

crypted using the corresponding PSK

. Based on RFC

3830 (Arkko et al., 2004), we advocate the use of

AES-CCM mode that defines AES-CBC for MAC

generation and AES-CTR for encryption (Dworkin,

2007). Nowadays, more and more tiny sensors in-

clude AES hardware coprocessor, which would help

to decrease the overhead. Regarding Seg2, commu-

nications are secured using Public Key Encryption.

According to RFC 3830 (Arkko et al., 2004), RSA is

used as a cryptographic primitive (Rivest et al., 1978).

The certification authority is in charge of delivering

the required certificates.

Authentication and Integrity: in our protocol,

communications are authenticated using MACs in

Seg1 and digital signatures in Seg2. Thus, the ex-

changed data is guaranteed to remain genuine. This

property ensures that the data has not been altered,

and has been sent from legitimate entities (and to

legitimate entities, as verification messages can be

IoTBD 2016 - International Conference on Internet of Things and Big Data

Figure 4: Distributed MIKEY mode: message exchanges.

added to provide mutual authentication). Further-

more, nonces (i.e. time-stamps) are included in the

exchanged messages for protection against replay at-

tacks.

Distribution: similar to the Pre-shared mode, an

initialization phase is required to distribute the shared

PSK between the constrained nodes and the TP

. This

phase is generally performed off-line. Nevertheless,

in Seg2 and similar to the Public key mode, TP

and

R establish a secure channel in an online mode tak-

ing advantage from the asymmetric primitives. As a

consequence, upon an initial distribution in Seg1, our

mode can be run without any external intervention al-

lowing automatic updates.

Overhead: the constrained entities are only in-

volved in symmetric operations, which are much less

resource consuming than asymmetric ones. Actually,

the powerful third parties take in charge all asym-

metric operations. Indeed, limiting computation so-

licitations for the constrained nodes decreases their

power consumption and thus increases their battery

life-time.

Resilience: involving several third parties in the

key exchange process makes our mode highly re-

silient. To compromise and recover the exchanged se-

cret TGK, an attacker would need to corrupt all third

parties, as TGK is split into numerous shares. Thus,

unless an attacker compromises all TP

, it is nearly

impossible to recover the original TGK. As a result,

our hybrid mode does not assume the third parties to

be trusted.

Extensibility and Scalability: in an e-health sce-

nario, new sensors can be integrated at any time. We

can easily imagine a physician prescribing the im-

plantation of a new sensor for various medical pur-

poses. Our protocol requires an initialization phase

where the sensor (i.e. I) is set with a list of TP

iden-

tities, along with the PSK

that are shared with each

. However, our protocol proceeds without any op-

eration regarding the TP

or R. After the initialization

phase, the joining sensor is ready to establish an end

to end secure channel with any remote entity.

Storage: due to recent hardware advances in flash

memory, smart objects provide considerable amounts

of storage space (Tsiftes and Dunkels, 2011). This

space is used in our hybrid mode to store the TP

’s

identities list along with the corresponding PSK

. Fur-

thermore, we assume that the number of TP

will not

exceed a reasonable threshold. Thus, storage space is

not considered as an issue in our protocol deployment.

4.2 Formal Validation

To prove that our protocol does not violate the re-

quired security properties, in particular, confidential-

ity, authentication, delivery proof and replay protec-

tion, we carried out an analysis using Avispa tool

(Moedersheim and Drielsma, 2003). AVISPA (Au-

tomated Validation of Internet Security Protocol and

Applications) is a state-of-the-art verification tool for

security protocols that includes a set of model check-

ers with a common front end. The tool follows the

Dolev-Yao intruder model (Dolev and Yao, 1981) to

intercept messages, or to insert modified data. It per-

forms analytical rules to state whether the protocol is

safe or not. In case of unsafety, the tool provides a

trace highlighting the steps that led to the attack.

Protocol models in Avispa are written in a role-

based language called High Level Protocol Specifi-

cation Language, or HLPSL (Chevalier et al., 2004).

The actions of the different entities are specified in

a module called basic role, while their interactions

are defined by composing multiple basic roles to-

gether into a composed role. In addition, the secu-

rity goals of the analyzed protocol are specified in

the goal section before launching the analysis. Be-

sides, Avispa uses several different automatic proto-

col analysis techniques to validate the analyzed proto-

col against the specified security goals such as the on-

the-fly model-checker (OFMC), and the constraint-

logic based attack searcher (CL-AtSe).

In our modeling, we first specified a basic role

to describe the actions of the different involved en-

A New Distributed MIKEY Mode to Secure e-Health Applications

Figure 5: Avispa output (OFMC).

Figure 6: Avispa output (CL − AtSe).

tities. Then, we specified how the participants inter-

act with each other in a composed role. The differ-

ent roles have been implemented using the HLPSL

language, and introduced as an input for Avispa tool.

The specification has been anayzed against the Dolev-

Yao intruder model using the OFMC, and the CL-

AtSe backends. The results have been indicated in

reports for each backend model produced by Avispa

tool. They show that our protocol is ”SAFE” against

OFMC (Fig. 5), and CL − AtSe (Fig. 6). Based on the

obtained results, we can affirm that our distributed hy-

brid mode is safe with respect to the specified security

goals.

5 RELATED WORK

In our literature review, we distinguish two main re-

search axes. The first one is focused on the secu-

rity approaches designed upon standard based pro-

tocols, while the second one is focused on the ap-

proaches based on the offloading of heavy computa-

tional operations to third parties. Numerous energy

aware approaches have been introduced for the IP-

based IoT. In (Hui and Thubert, 2011), the compres-

sion of IPV6 headers, extension headers along with

UDP headers has been standardized through 6LoW-

PAN. Authors in (Raza et al., 2011) presented 6LoW-

PAN compressions for IPsec payload headers (AH

and ESP). In (Raza et al., 2012b), an IKE com-

pression scheme has also been proposed providing a

lightweight automatic way to establish security as-

sociations for IPsec. Likewise, header compression

layers for DTLS and HIP DEX have been introduced

in (Raza et al., 2012a), (Hummen et al., 2013), and

(Sahraoui and Bilami, 2015). Furthermore, in (Ab-

dmeziem and Tandjaoui, 2014), authors introduced a

compression scheme in addition to a new exchange

mode to reduce MIKEY TICKET overhead.

Besides the proposed standard-based schemes,

several approaches that aim to offload resource con-

suming operations to third entities have been pro-

posed. Authors in (Saied and Olivereau, 2012) in-

troduced collaboration for HIP. The idea is to take

advantage of more powerful nodes in the neighbor-

hood of a constrained node to carry heavy compu-

tations in a distributed way. Likewise, IKE session

establishment delegation to a gateway have been pro-

posed in (Bonetto et al., 2012). Furthermore, authors

in (Freeman et al., 2007) introduced a delegation pro-

cedure that enables a client to delegate certificate vali-

dation to a trusted server. While the precedent delega-

tion approaches reduce the computational load at the

constrained node, they break the end to end principle

by requiring a third trusted party. Authors in (Abd-

meziem and Tandjaoui, 2015), addressed the prece-

dent issue by enhancing the existing schemes to en-

sure the end to end property.

The proposed approach in this paper can be posi-

tioned in both axes. In fact, it is based on the offload-

ing of heavy asymmetric operations to third parties,

while being implemented through a standard based

protocol (i.e. MIKEY).

IoTBD 2016 - International Conference on Internet of Things and Big Data

6 CONCLUSIONS

We addressed the problematic of establishing secured

communication channels in the constrained environ-

ment of e-health applications. In fact, we introduced

a new MIKEY mode that combines the pre-shared

key mode with the public key mode. In this mode,

heavy operations are offloaded to dedicated powerful

third parties. Doing so, the constrained entities are

only involved in the symmetric operations of the pre-

shared mode. The public key mode is left to the un-

constrained part of the network. As a result, the con-

strained entities are able to establish a secured chan-

nel with any remote entity without having established

an initial shared knowledge. Indeed, through our dis-

tributed hybrid mode, we benefit from the advantages

of both pre-shared mode (resource preservation) and

public key mode (scalability), while mitigating their

disadvantages. The first preliminary results show that

our mode is secure, and resource preserving at the

same time. In the future, we plan an implementation

on real test-beds to assess its energy consumption per-

formances under real conditions.

REFERENCES

Abdmeziem, M. and Tandjaoui, D. (2015). An end-to-end

secure key management protocol for e-health applica-

tions. Computers & Electrical Engineering.

Abdmeziem, M. R. and Tandjaoui, D. (2014). Tailoring

mikey-ticket to e-health applications in the context of

internet of things. In International Conference on Ad-

vanced Networking, Distributed Systems and Applica-

tions, pages 72–77.

Abdmeziem, M. R., Tandjaoui, D., and Romdhani, I.

(2016). Architecting the internet of things: State of

the art. In Robots and Sensor Clouds, pages 55–75.

Springer International Publishing.

Arkko, J., Lindholm, F., Naslund, M., and Norrman, K.

(2004). Mikey: Multimedia internet keying. RFC

3830, IETF.

Atzori, L., Iera, A., and Morabito, G. (2010). The internet

of things: A survey. Computer Networks, pages 2787–

2805.

Bonetto, R., Bui, N., Lakkundi, V., Olivereau, A., Serbanati,

A., and Rossi, M. (2012). Secure communication for

smart iot objects: Protocol stacks, use cases and prac-

tical examples. In Proc. of IEEE WoWMoM.

Chang, V. and Ramachandran, M. (2016). Towards achiev-

ing data security with the cloud computing adoption

framework. IEEE Transactions on Services Comput-

ing, 9(1):138–151.

Chevalier, Y., Compagna, L., Cuellar, J., Drielsma, P. H.,

Mantovani, J., and S. Modersheim, a. L. V. (2004). A

high level protocol specification language for indus-

trial security sensitive protocols. Proc. SAPS 04. Aus-

trian Computer Society, 2004.

Dohr, A., Modre-Opsrian, R., Drobics, M., Hayn, D., and

Schreier, G. (2010). The internet of things for ambi-

ent assisted living. In Information Technology: New

Generations (ITNG), pages 804–809.

Dolev, D. and Yao, C. (1981). On the security of public key

protocols. FOCS, IEEE, pages 350–357.

Dworkin, M. (2007). Recommendation for block cipher

modes of operation: The ccm mode for authentication

and confidentiality. SP-800-38c, NIST, US department

commerce.

Freeman, T., Housley, R., Malpani, A., Cooper, D., and

Polk, W. (2007). Server-based certificate validation

protocol(scvp). RFC 5055, IETF.

Hui, J. and Thubert, P. (2011). Compression format for ipv6

datagrams over ieee 802.15.4-based networks. RFC

6282, IETF.

Hummen, R., Hiller, J., Henze, M., and Wehrle, K. (2013).

Slimfit a hip dex compression layer for the ip-based

internet of things. WiMob, IEEE, pages 259–266.

Li, M. and Lou, W. (2010). Data security and privacy in

wireless body area networks. Wireless Technologies

for

E-healthcare.

Moedersheim, S. and Drielsma, P. (2003). Avispa project

deliverable d6.2: Specification of the problems in the

high-level specification language. http://www.avispa-

project.org.

Raza, S., Duquennoy, S., Chung, T., Yazar, D., Voigt, T.,

and Roedig, U. (2011). Securing communication in

6lowpan with compressed ipsec. in Proc. of IEEE

DCOSS.

Raza, S., Trabalza, D., and Voigt, T. (2012a). 6lowpan com-

pressed dtls for coap. in Proc. of IEEE DCOSS.

Raza, S., Voigt, T., and Jutvik, V. (2012b). Lightweight

ikev2: A key management solution for both com-

pressed ipsec and ieee 802.15.4 security. IETF/IAB

workshop on Smart Object Security.

Reed, S. and Solomon, G. (1960). Polynomial codes over

certain finite fields. Journal of the Society for Indus-

trial and Applied Mathematics, 8(2):300–304.

Rivest, R. L., Shamir, A., and Adleman, L. (1978). A

method for obtaining digital signatures and public-

key cryptosystems. Communications of the ACM,

21(2):120–126.

Roman, R., Alcaraz, C., Lopez, J., and Sklavos, N. (2011).

key management systems for sensor networks in the

context of internet of things. Computers and Electric

Engineering, 37:147–159.

Sahraoui, S. and Bilami, A. (2015). Efficient hip-based

approach to ensure lightweight end-to-end security in

the internet of things. Computer Networks, 91:26–45.

Saied, Y. B. and Olivereau, A. (2012). Hip tiny exchange

(tex): A distributed key exchange scheme for hip-

based internet of things. in Proc. of ComNet.

Tsiftes, N. and Dunkels, A. (2011). A database in every sen-

sor. Proceedings of the 9th ACM Conference on Em-

bedded Networked Sensor Systems, pages 316–332.

A New Distributed MIKEY Mode to Secure e-Health Applications