Secure Multifactor Remote Access User Authentication Framework for IoT Networks

Mohammed Alshahrani

doi:10.32604/cmc.2021.015310

College of Computing and Information Technology, The University of Bisha, Saudi Arabia
*Corresponding Author: Mohammed Mujib Alshahrani. Email: mmalshahrani@ub.edu.sa
Received: 14 November 2020; Accepted: 08 January 2021

Abstract: The term IoT refers to the interconnection and exchange of data among devices/sensors. IoT devices are often small, low cost, and have limited resources. The IoT issues and challenges are growing increasingly. Security and privacy issues are among the most important concerns in IoT applications, such as smart buildings. Remote cybersecurity attacks are the attacks which do not require physical access to the IoT networks, where the attacker can remotely access and communicate with the IoT devices through a wireless communication channel. Thus, remote cybersecurity attacks are a significant threat. Emerging applications in smart environments such as smart buildings require remote access for both users and resources. Since the user/building communication channel is insecure, a lightweight and secure authentication protocol is required. In this paper, we propose a new secure remote user mutual authentication protocol based on transitory identities and multi-factor authentication for IoT smart building environment. The protocol ensures that only legitimate users can authenticate with smart building controllers in an anonymous, unlinkable, and untraceable manner. The protocol also avoids clock synchronization problem and can resist quantum computing attacks. The security of the protocol is evaluated using two different methods: (1) informal analysis; (2) model check using the automated validation of internet security protocols and applications (AVISPA) toolkit. The communication overhead and computational cost of the proposed are analyzed. The security and performance analysis show that our protocol is secure and efficient.

Keywords: Internet of things; threats; smart building; attacks; remote access; authentication; smart buildings

In the last few years, the world has witnessed a huge revolution in information and computing technologies of the 21st century. Internet of Things (IoT) is one of the most emerging releases of this revolution [1].

The core concept of IoT is adding sense to non-living objects to perform the information processing and take decisions automatically without any presence of human or living bodies’ functionality in the system, and bring all physical entities in one affordable integrated digital fold sharing defined communication system among it [2].

IoT network composes various physical entities together in the same networks, and most of these entities are not well manufactured for IoT systems. Hence, these embedded devices do not include and support high-security mechanisms. Thus, it will be exposed to various malicious attacks in both privacy and security dimensions. While the cybersecurity issues are gradually increasing with the increase of the IoT capacities of adapting entities, the heterogeneous entities introduce new security and privacy issues. Recently, many IoT devices were brought down by a distributed denial of service (DDoS) attack carried out using the Mirai IoT botnet [3]. Mirai propagates by brute-forcing IoT devices using a list of common, default passwords to attempt to log into the IoT devices. Thus, depending only on password-based authentication is a weak method as passwords are often easy to guess by attackers launching brute-force attacks.

Several proposals have been published on user authentication [4–8]. Recent progress in user authentication has focused on incorporating context information. Many contributions incorporated various types of context information, namely physical contexts, device contexts, and historical information, among others.

Among these contributions, Jeong et al. [4] introduced a one-time password-based user authentication scheme using a smart card for a smart home. This scheme is lightweight because it depends on one-way hash function operations. However, the scheme failed to achieve some important security properties: mutual authentication between GWN and smart devices, traceability, unlinkability, and user anonymity.

Han et al. [9] proposed a secure key agreement scheme for smart home systems. The proposed scheme is suitable for consumer electronics devices in a smart home.

Li [10] proposed a lightweight key establishment protocol, and an initial session key was established between the nodes and control. The mutual authentication between the user and control was not provided in their scheme.

Santoso et al. [11] introduced a remote user authentication scheme for a smart home using elliptic curve cryptography (ECC). The authors did not achieve two key security properties, named anonymity and traceability. Furthermore, the scheme is susceptible to privileged-insider and stolen smart card attacks.

Kumar et al. [6] introduced a lightweight and secure session key establishment scheme for IoT smart homes. They capitalized on a short authentication token to establish a session key between and smart device.

Recently, Shuai et al. [12] proposed a remote authentication scheme for smart homes using ECC. The authors eliminate the need to store the verification table for authentication purposes. However, the authors failed to provide satisfactory performance.

Although researchers have proposed some remote user authentication schemes for smart buildings, they are not lightweight or secure enough to be suitable for the smart building nodes due to resources-constraints nature of such sensors. Besides, few mutual authentication and key agreement schemes have been proposed. Moreover, almost none of these contributions considered using physical context awareness (i.e., location awareness) and transaction history for authentication. Motivated by the importance of authentication based on location awareness and transaction history for remote access in IoT smart building, this paper devoted to design a secure scheme not only provides mutual authentication but also achieves some important security properties such as anonymity, untraceability and unlinkability of transmitted information as well as authentication based on location awareness and transaction history. The contributions of this paper are as follows:

• We propose a new anonymous remote user mutual authentication protocol designed for the IoT smart building network. Our protocol guarantees key security properties: confidentiality, integrity, anonymity, unlinkability, and untraceability [13].

• Our protocol avoids the clock synchronization problem by not relying on timestamps to ensure safe protection against message reuse attacks, and it also can stop quantum computing attacks.

• We propose location awareness and transaction history to improve the authentication process of the user remote access of IoT smart buildings.

• The security of our protocol is proved by using the widely accepted Burrows–Abadi–Needham (BAN) logic, and assessed by using the AVISPA simulator tool. Besides, an informal security analysis of the proposed scheme is discussed.

• We compare our proposed protocol with other related protocols. Comparison results show that our protocol is more secure and efficient than the previously proposed related protocols.

The remaining parts of this paper are structured as follows. In Section 2, we describe the proposed system model. In Section 3, we describe in depth our secure protocol. In Section 4, we evaluate the security of our protocol. In Section 5, we evaluate the performance of our protocol. Finally, Section 6 concludes the paper.

This section introduces the used network model and adversary model of the proposed scheme.

The network model consists of four entities, namely registration authority (RA), the end-user, the smart building controller node (CRN), and the smart device (SD) in the building (see Fig. 1).

Every time the end-user wishes to access the IoT smart building devices remotely, they have to provide the building controller with their current location. Moreover, we employ transaction history in our system in a lightweight way. We cryptographically hash the transactions between the building controller and the user in a secure way. Then, we capitalize on these hashed transactions to improve the authentication process in our system. Thus, we combine location awareness and hashed transaction history to enhance user remote access to IoT smart buildings.

We introduced two different techniques that were derived from the transaction histories: first, a robust cumulative cryptographically-hashed historical transactions (CCHH) technique based on a one-way cryptographic hash function is used to validate and ensure the integrity of the data transmitted between the end-user and the building controller, and to maintain the anonymity of the communication parties; second, a robust cumulative location tracker (CLT) technique is used to ensure the genuineness of the user and the freshness of the temporary session keys. These CCHH and CLT techniques are briefly explained, as follows:

At the core of the proposed protocol is a lightweight challenge-response mechanism that relies on a chain of cryptographically-hashed historical transactions (CCHH) of all communication sessions between the user and the controller. The user and the controller maintain a synchronized database, called CCHH database, containing hashes generated during previous authentication sessions singed with the temporal session secret keys that change in every session. The synchronized CCHH database’s values are utilized in generating the transitory identities of both the user and the controller, where transitory identities TID is constructed by hashing the real identity and the xCCHH. Note that x denotes the corresponding cumulative cryptographically-hashed historical transactions value that is stored in CCHH database. The use of the transitory identities improves the smart building’s privacy by achieving key security properties, namely, anonymity, unlinkability and untraceability.

A physical context awareness, namely location, is employed in our system to check if a mobile device’s current location is approximate to the previously recorded location within a given time. The location of the mobile device is checked using the linear motion equation (δLMAX= V * δT) to calculate the time it would take a given user to move from a previous location to a current location, where δT denotes the time required for a user to move from the previous location Lp to the current location Lc. In contrast, V denotes the maximum velocity that the user could have. These locations from all sessions (previous locations + current locations) between the mobile device and the controller are cumulatively hashed and stored securely in a synchronized database, called CLT database, maintained by both the mobile device and the controller. These synchronized databases of cumulative hashes are capitalized on to achieve a challenge-response authentication between the mobile device and the controller to stop remote attacks such as Mirai attack. Meaning that, in case of suspicion of remote access attack, the controller challenges the knowledge of the mobile device about the previous locations stored in the database, the mobile device then has to send back the correct corresponding location; Otherwise, the session is terminated, and the mobile device is flagged as a malicious. Moreover, the synchronized CLT database’s values can generate the temporal session secret keys between the mobile device and the controller. The session secret SK is constructed by hashing the secret key and the xCLT. Note that x denotes the corresponding cumulative cryptographically-hashed location tracker value that is stored in CLT database.

In our system, data security between the end-user and the IoT smart device is ensured by encrypting the payload using the AES 128 CCM algorithm (16 Bytes). According to a report on lightweight cryptography done by Kerry McKay from The National Institute of Standards and Technology [14], AES 128 CCM (Counter with CBC-MAC) mode is by far the most widely used-symmetric key algorithm. It can also be chosen which would provide additional benefit of data integrity.

To evaluate the security properties of the proposed scheme, we define the adversarial model as follows.

• The CRN is assumed trustworthy. However, an attacker may be able to infiltrate HN’s database. He may steal or manipulate database information.

• The attacker can eavesdrop on all communication links in the network. He can also damage or replace transmitted messages or replay previously sent messages.

• We consider the well-known Dolev–Yao threat model [15]. It assumes that two communicating parties communicate over an insecure channel. We rely on this model to provide the security analysis and simulation of our scheme.

In this section, a secure remote user access authentication protocol based on transitory identities and multi-factor authentication is presented for IoT smart building systems, which resists all known attacks and supports the desirable security features. The abstract notations used to describe our authentication protocol are listed in Tab. 1. The proposed protocol consists of four phases: (1) initialization phase; (2) registration phase; (3) login and authentication phase; and (4) password change phase. These phases are explained as follows:

The manufacturer does the initialization phase before the devices are handed to the owner of the smart building. The mobile device will be loaded with a unique symmetric key Kur shared between the registration authority RA and the mobile device. Lastly, the controller will be loaded with a unique symmetric key Kgr shared between the registration authority and controller.

When the user first turns on the mobile device, the user picks up an identity IDUi and a password PWi. Next, the user sends his/her identity and password to RA in a secure way, using the shared symmetric key Kur.

When RA receives the message, it will store the mobile device information, and generate a temporary identity for the user IDUi and temporary secret key TSK, and computes the following parameters:

where SN, DMN, ESN are context information of the mobile device namely serial number, device manufacturer name, and unique equipment serial number, respectively.

RA also generates INV1 and INV2 to be used as initial values by both mobile device and controller for CKH and CLT technique, respectively. Then, RA sends {IDUi, TSK, S1, S2, INV1, INV2} and {IDUi, TSK, SN, DMN, ESN, S1, INV1, INV2} to Ui and controller using the shared secret keys Kur and Kgr, respectively (see Fig. 2).

The user enters his/her identity IDUi and password PWi into mobile device, the mobile device computes *S1 = h(IDUi, TSK, SN, DMN, ESN) and checks if *S1 = S1. If they match, the user is considered legitimate and can access the application on his/her mobile device. Otherwise, the mobile device drops the login request, increments the value of the counter by 1, and check if it reaches the predetermined value, for instance, 3. If the number of attempts exceeds the predetermined value, the mobile device terminates the login request immediately until the user re-register.

When Ui wishes to control any IoT device in the smart building, it will have to authenticate themselves with the controller first. Hence, they will follow the below steps:

1. Ui accesses the mobile device and enter the correct username IDUi and password PWi.

8. Controller checks the genuineness of Lc using the formula (δLMAX= V * δT). If it is legitimate, the controller will update the cumulative location tracker xCLT = h(Lc, INV2). Otherwise, controller challenges mobile devices knowledge of one of the previous CLT values.

5. Mobile device uses DIDSD and N to establish a secure communication session with SD.

6. Mobile device computes TIDUi2= h(IDUi, x+1CCHH) for upcoming session with CRN

7. Mobile device computes DIDCRN2= h(IDCRN, x+1CCHH) for upcoming session with CRN

The above steps are summarized in Fig. 3. By the end of processing each message, CCHH database is updated (see Fig. 4), and by the end of each session, CLT table is updated (see Fig. 5).

In this section, the user Ui can change his/her password without any interaction with CRN by performing the following operations.

2. Mobile device computes *S1 and checks if *S1 = S1. If it is not hold, the mobile device rejects the password change request. Otherwise, the mobile allows Ui to enter a new IDUi and Pwi.

As aforementioned in the previous section, Ui and CRN securely maintain two synchronized databases, namely CCHH and CLT, of cumulative hashed values. These values can be capitalized on to introduce a historical factor for authenticating Ui, as illustrated in Fig. 6. Authentication using a historical factor helps us achieve mutual authentication through a challenge-response process, where mutual authentication is so important in securing communication between devices. This two-way challenge/response allows the controller to verify the authenticity of Ui, so that it can stop malicious attacks such as the Mirai attack. Cumulative hash history-based authentication challenges Ui to show a proof of knowledge of past cumulative hash values. The approach involves securely storing the cumulative hash values related to the interaction over time between the Ui and CRN in CCHH and CLT databases. Thus, when CRN receives an authentication request message from Ui, it triggers a challenge/response process. It generates a challenge c (information about random cumulative hash value xCCHH stored previously), hashes the challenge c with the secret key and xCLT h(SK, c, xCLT), and sends it to Ui.

Figure 4: Cumulative cryptographically-hashed historical transactions (CCHH) table

Ui sends the response back using h(SK, r, xCLT), where r is the response (cumulative hash value xCCHH). Once CRN receives the response, it checks if the received xCCHH value is correct. If yes, CRN will accept Ui and resume the authentication process. Otherwise, Ui will be rejected and flagged as malicious.

It is worth mentioning that the challenge-response mechanism is triggered by CRN when the Lc, that is provided by Ui, is not approximated.

In this section, we discuss different known attacks, and we explain how our protocol successfully resists such attacks.

In the following, we analyze different important adversarial attacks/security properties and how our scheme stops these attacks and achieves these properties.

The replay attack is defeated using CCHH and CLT’s cumulative values that security change in every message. Furthermore, the mobile device and the controller use secure unique identities in every session. Besides, the keyed-hash message authentication cod (HF) value, which is attached in each message, changes in every single message. Hence, replay attack is detected.

In the authentication phase of our scheme, an adversary A can record all transmitted parameters between Ui and CRN. He collects the tuple 〈DIDCRN1,TIDUi1,UC,HF〉 from Ui to CRN, and the tuple 〈TIDUi2, DIDCRN2, CU, HF〉 from CRN to Ui. Notice that the session key SK1 = h(TSK, ISV1). From the intercepted parameters, A cannot reach TSK and ISV1 because they are protected by the one-wayness of h (.). Moreover, A cannot compute xCCHH = h(HF, SK1) or xCLT = h(Lc, INV2) because he does know SK1 or Lc or INV2. The same is applied to the parameters sent from CRN to Ui. Therefore, the privacy of the SK1, SK2, xCCHH, xCLT, and Lc are preserved, and hence, the scheme protects against an eavesdropping attack.

This attack is stopped using the TIDUi and HF, which are protected using the one-way hash function. Besides, TIDUi is constructed from different secure parameters TIDUi = h(IDUi, ISV1), and change in every message as ISV1 is updated in every message. Hence, the attacker is unable to create a valid temporary identity without the corresponding IDUi, ISV1.

Our protocol is protected against this attack using TIDUi, N, and HF. So, this attack can be defeated.

This attack is defeated using the temporary secret key SK and N, which change in every session. Moreover, SK is constructed using secure parameters and protected using a one-way hash function.

The forward/backward security is an important security property, which means that any past or future sessions keys will not be affected when any temporary session key is exposed. Forward/backward security is achieved using the SK and N, which dynamically change in every session.

This attack is defeated using the SK and N, which dynamically change in every session.

Recent advances in quantum computing put the security of the current IoT at risk using these cryptographic schemes. Grover’s algorithm speeds up this process of brute force search dramatically using quantum computers. Thus, we rely on hash functions and symmetric schemes that are relatively easy to prevent quantum attacks by enlarging key and output sizes.

In our proposed protocol, the user UI never stores its identity IDUi and password PWi credentials in its mobile device’s memory because it stores the hash value S1, contributing to verifying IDUi and PWi entered by the user. When the attacker tries to obtain user credentials from S1 physically, they will fail as the one-way hash function protects S1. Hence, our proposed protocol can successfully stop the user credentials attack.

User anonymity unlinkability and untraceability are crucial security properties in the authentication. Anonymity ensures the mobile device’s real identity is kept secure and the mobile device remains unidentifiable among the other set of devices. Thus, the attacker cannot identify the devices’ real identities as the real identity of the mobile device is kept secure and we use transitory identities that change in every session. We also ensured that an attacker cannot link the different sessions initiated by a particular mobile device to the same UI. Also, the adversary cannot relate two or more sessions to the same Ui. Hence, our protocol achieves anonymity, unlinkability, and untraceability of the conducted sessions.

4.1.11 Authentication Based on Cumulative Hashed Transaction History and Location

GPS location is utilized in our protocol to check whether mobile device’s previous location is proximate to the current location. Tracking the GPS location of Ui will contribute to stopping remote cybersecurity attacks such as the as discussed in this Section Mirai attack. Additionally, both CRN and Ui maintain a synchronized database of cumulative hashes generated from the previous sessions, as discussed in this Section. These synchronized databases improve the overall security by applying the challenge/response mechanism and ensuring the uniqueness and freshness of the identities and established sessions; thus, securing the smart building system from known attacks.

The BAN logic was introduced by Burrows et al. [16] in 1989. It is a widely accepted model to describe and analyze authentication protocols. It has been widely employed to verify the protocols’ security and provide proof of correctness of the authentication protocols [17]. Hence, we capitalize on it to formally prove that our authentication scheme achieves mutual authentication between an IoT device N and controller C.

We start by presenting a summarized introduction about the important symbols and the rules of BAN logic. Then, we will proceed with our formal proof.

Let N (client) and C (server) be participators, and let X and Y denote a parameter, formula or expression. We define the following notations:

• N ⇌ C: X is a secret known only to N and C, and possibly to parties trusted by them.

Furthermore, the following commonly used BAN logic rules are utilized to prove that our authentication scheme ensures secure mutual authentication and key agreement, as follows:

• Message meaning rule: If N sees X encrypted with Y and if N believes Y is a secret key shared with C, then N believes C once said X.

• Nonce verification rule: If N believes X is fresh and N believes C once said X, then N believes C believes X.

• Jurisdiction rule: If N believes C has jurisdiction over X and N believes C believes X, then N believes X.

• Freshness conjuncatenation rule: If one part of a formula is fresh, then the entire formula must also be fresh; so, if N believes X is fresh, then N believes X and Y are fresh.

In this section, we define the main goals of the analysis of our authentication scheme as follows:

• Goal 1: CRN believes Ui believes SK1 is a secure, shared parameter between Ui and CRN.

• Goal 3: Ui believes CRN believes SK2 is a secure, shared parameter between Ui and CRN.

The idealized messages that are exchanged in the authentication phase between a user Ui and the controller CRN are listed below:

• A3: Ui believes CCHH is a secure, shared parameter between Ui and CRN: Ui ∣≡ (Ui ↔CCHHCRN)

• A4: CRN believes ISV 1 is a secure, shared parameter between Ui and CRN: CRN ∣≡ (Uj ↔ISV1CRN)

• A5: Ui believes CRN has jurisdiction over CCHH, SK2, and HF: Ui∣≡ CRN∣⇒{CCHH, SK2, HF}

• A6: CRN believes Ui has jurisdiction over ISV1, SK1 and HF: CRN∣≡ Ui∣⇒{ISV1, SK1, HF}

We now start analyzing our authentication scheme to prove that our scheme achieves mutual authentication between Ui and CRN.

S2: From assumption A4 and S1, and by applying the message meaning rule, we derive:

CRN ∣≡(Uj ↔ISV1CRN ),CRN ⊲〈DIDCRN1,DIDUi1,UC,HF〉Ui↔SK1CRNCRN∣≡Ui |~〈DIDCRN1,DIDUi1,UC,HF〉Ui↔SK1CRN

S4: From derivations S3 and S2, and by applying the nonce verification rule, we derive:

GWN∣≡#〈DIDCRN1,DIDUi1,UC,HF〉Ui↔SK1CRN, CRN∣≡Ui|~〈DIDCRN1,DIDUi1,UC,HF〉Ui↔SK1CRNCRN∣≡Ui∣≡〈DIDCRN1,DIDUi1,UC,HF〉Ui↔SK1CRN

S6: From assumption A3 and derivation S5 and by applying the message meaning rule, we derive:

Ui ∣≡(Ui ↔CCHHCRN ), Uj ⊲〈DIDUi2,DIDCRN2,HM,HF〉Ui↔SK2CRN Ui∣≡ GWN |~〈DIDUi2,DIDCRN2,HM,HF〉Ui↔SK2CRN

S7: From assumptions A1 and A2, and applying freshness-conjuncatenation rule, we get:

Uj ∣≡#〈DIDUi2,DIDCRN2,HM,HF〉Ui↔SK2CRN , Ui∣≡ GWN |~〈DIDUi2,DIDCRN2,HM,HF〉Ui↔SK2CRN Ui∣≡ CRN ∣≡〈DIDUi2,DIDCRN2,HM,HF〉Ui↔SK2CRN

CRN∣≡ Ui ∣⇒{ ISV1, SK1,HF }, CRN∣≡Ui∣≡〈DIDCRN1,DIDUi1,UC,HF〉Ui↔SK1CRNCRN ∣≡〈DIDCRN1,DIDUi1,UC,HF〉Ui↔SK1CRN

CRN∣≡#〈DIDCRN1,DIDUi1,UC,HF〉Ui↔SK1CRN, CRN∣≡Ui∣≡〈DIDCRN1,DIDUi1,UC,HF〉Ui↔SK1CRNCRN ∣≡ Ui ∣≡(Uj ↔SK1GWN ) (Goal 1)

S11: From derivation S10 and assumption A6 and applying jurisdiction rule, we get:

CRN∣≡ Ui ∣⇒{ISV1, SK1,HF}, CRN ∣≡ Ui ∣≡(Uj ↔SK1GWN )CRN ∣≡(Ui ↔SK1CRN) (Goal 2)

Uj ∣≡#〈DIDUi2,DIDCRN2,HM,HF〉Ui↔SK2CRN,Ui∣≡CRN∣≡〈DIDUi2,DIDCRN2,HM,HF〉Ui↔SK2CRNUi∣≡CRN∣≡(Uj↔SK2CRN) (Goal 3)

S13: From assumption A5, and derivation S12, and by applying jurisdiction rule, we get:

Uj∣≡GWN∣⇒{N2,SK,V2},Uj ∣≡ CRN ∣≡(Uj ↔SK2CRN )Ui ∣≡(Ui ↔SK2GWN) (Goal 4)

Hence, our authentication scheme achieves mutual authentication and key agreement between Ui and CRN.

Our protocol is evaluated using the Automated Validation of Internet Security Protocols and Applications (AVISPA) toolkit, which is widely used as a toolkit in the research community for security protocol validation [18]. Fig. 7 depicts the HLPSL code for role UI.

The simulation via AVISPA is done using two widely-accepted back-end model checkers: The On-the-Fly Model-Checker (OFMC) and the Constraint Logic-based Attack Searcher (CL-AtSe).

Fig. 8 shows the CL-AtSe back-end checker report that assures that our protocol is SAFE and free from attack. Fig. 9 shows the OFMC back-end checker report, which proves that our protocol is SAFE and satisfies the specified security goals.

However, we were not able to use the TA4SP back-end model checker due to its limitation in supporting XOR operation, while The SATMC model checker has reported NOT SUPPORTED.

In this section, we evaluate our protocol’s performance in terms of communication overhead and computation costs.

The length of the parameters of the transmitted message DIDCRN1, TIDUi1, UC, HF, CU are 128 bits, 128 bits, 256 bits, 160, and 256 bits, respectively.

In our proposed protocol, the transmitted messages Ui → CRN and CRN → Ui require (128 + 128 + 256 + 160) = 681 bits, (128 + 128 + 256 + 160) = 681 bits, respectively. The communication overheads of our scheme are shown in Tab. 2. Also, it can be noticed that our protocol requires only two messages for a successful mutual authentication between Ui and CRN.

Our protocol is computationally lightweight designed for IoT smart building. Our protocol ensures high security using only a simple hash function and XOR computations; Hence consuming little computation overheads. However, this protocol’s novelty is adding multiple security layers (e.g., GPS location tracker, CCHH and CLT technique, challenge/response mechanism, and ensuring transitory identities, it also provides relatively low computation cost.

Our protocol uses two operations as aforementioned, namely XOR operation and one-way hash function. Let Th and Txor be the computation times of one hash and one XOR operations, respectively. Considering the authentication steps involved in our protocol and outlined in Fig. 3, the Ui performs 7 hash and 1 XOR operations, which yields a total computation cost of 12 ×Th+ 2 ×Txor. The computation time of XOR operation is very trivial and can be ignored, so we can assume Txor≈ 0. On the other hand, the controller node CRN performs 10 hash and 2 XOR operations, which yield a total computation cost of 10 ×Th+ 2 ×Txor. Therefore, the total computation cost of N is 10 ×Th+ 2 ×Txor≈ 10Th, while the computation cost of CRN corresponds to 10 ×Th+ 2 ×Txor≈ 10 ×Th. The computation cost is summarized in Tab. 3.

We present a comparison between our proposed scheme and other most related schemes in terms of communication cost based on transmissions in both directions between IoT node and gateway. We use the number of exchanged messages for a successful authentication as the key to the communication cost comparison. As presented in Tab. 4, our scheme requires 4 messages and 2304 bits total number of bits for successful mutual authentication. In general, the comparison shows that our scheme is comparatively more cost-efficient than the other related works in terms of the number of exchanged messages and the total number of bits, and just a little less cost efficient than that of Kumar et al. [6] because our scheme adds additional functionality and security features are not provided by Kumar et al.; such as mutual authentication between the user and smart device, mutual authentication between user and gateway, password guessing attack, password change attack, stolen smartphone/smart card attack, and password change phase, physical context awareness (i.e., location awareness), and historical authentication.

Table 4: Comparison of communication cost between the proposed scheme and other most related schemes

We proposed, in the current paper, a secure remote mobile device authentication protocol. The proposed protocol allows only legitimate users to authenticate with the IoT devices via the smart building getaway and exchange a symmetric session key for future secure communications. The security evaluation of the protocol, both through informal analysis and formal model checking (using AVISPA toolkit), shows that our scheme is secure against known attack techniques.

Despite the encouraging results, more work remains to be done. In our future work, we will implement the proposed protocol using OMNET++ and conduct live security tests using penetration testing tools such as Kali Linux. We will also explore how to strengthen our framework’s securit to thwart impersonation by adapting continuous authentication schemes, such as the approach proposed by Tsai et al. [19]. In their work [19], Tsai et al. introduced a passive continuous authentication system based on physiological and soft biometrics technologies, where face recognition is mainly used to control the authentication process In contrast, soft biometric is used to prevent and deal with any potential security breach, such as account hijacking.

Acknowledgement: The authors would like to thank University of Bisha for support him doing this research.

Conflicts of Interest: The author declares that he has no conflicts of interest to report regarding the present study.

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Computers, Materials & Continua DOI:10.32604/cmc.2021.015310
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