A two‐layer attack‐robust protocol for IoT healthcare security

IET CommunicationsVolume 15, Issue 19 p. 2390-2406 ORIGINAL RESEARCH PAPEROpen Access A two-layer attack-robust protocol for IoT healthcare security Two-stage identification-authentication protocol for IoT Sharafi Afsaneh, Sharafi Afsaneh Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, IranSearch for more papers by this authorAdabi Sepideh, Corresponding Author Adabi Sepideh Sepideh_adabi@iau-tnb.ac.ir orcid.org/0000-0003-4379-4528 Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran Correspondence Adabi Sepideh, Vafadar Blvd, Shahid Sadoughi St, Hakimiyeh Exit, Shahid Babaee Highway, Tehran, Iran. Email: Sepideh_adabi@iau-tnb.ac.irSearch for more papers by this authorMovaghar Ali, Movaghar Ali Department of Computer Engineering, Sharif University of Technology, Tehran, IranSearch for more papers by this authorAl-Majeed Salah, Al-Majeed Salah Head of School of Computer Science, University of Lincoln, England, UKSearch for more papers by this author Sharafi Afsaneh, Sharafi Afsaneh Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, IranSearch for more papers by this authorAdabi Sepideh, Corresponding Author Adabi Sepideh Sepideh_adabi@iau-tnb.ac.ir orcid.org/0000-0003-4379-4528 Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran Correspondence Adabi Sepideh, Vafadar Blvd, Shahid Sadoughi St, Hakimiyeh Exit, Shahid Babaee Highway, Tehran, Iran. Email: Sepideh_adabi@iau-tnb.ac.irSearch for more papers by this authorMovaghar Ali, Movaghar Ali Department of Computer Engineering, Sharif University of Technology, Tehran, IranSearch for more papers by this authorAl-Majeed Salah, Al-Majeed Salah Head of School of Computer Science, University of Lincoln, England, UKSearch for more papers by this author First published: 27 September 2021 https://doi.org/10.1049/cmu2.12278AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract The majority of studies in the field of developing identification and authentication protocols for Internet of Things (IoT) used cryptographic algorithms. Using brain signals is also a relatively new approach in this field. EEG signal-based authentication algorithms typically use feature extraction algorithms that require high processing time. On the other hand, the dynamic nature of the EEG signal makes its use for identification/authentication difficult without relying on feature extraction. This paper presents an EEG-and fingerprint-based two-stage identification-authentication protocol for remote healthcare, which is fast, robust, and multilayer-based. A modified Euclidean distance pattern matching method is proposed to match the EEG signal in the identification stage due to its dynamic nature. The authentication stage is also an optimized method with the Genetic Algorithm (GA), which utilizes a modified Diffie–Hellman algorithm. Due to the vulnerability of the Diffie–Hellman algorithm to different types of attacks, the parameters used for this algorithm are extracted from the fingerprint and the EEG signal of the patient to provide a fast and robust authentication method. The proposed method is evaluated using data from patients with spinal cord injuries. Simulating results demonstrated high identification and authentication accuracy of the proposed method. Furthermore, it is extremely fast and efficient. 1 INTRODUCTION The Internet of Things (IoT) is a concept, which describes a world in which everything, including timeless objects, will have a digital identity and computers will manage them [1]. In the world of technology and communications, the IoT is a relatively new concept. Shortly, it is a modern method such that everything (humans, animals, and objects) can be connected through a communication system, including the Internet or Intranet [2]. The IoT enables remote access to and control of devices over the existing network infrastructure. As a result, IoT reduces human participation and increases efficiency, resulting in economic benefits [3]. The IoT has enabled physical devices to communicate more effectively with actuators and sensors. Consequently, "anytime and anywhere" communication can be established in the real world [4-7]. Combining Internet connectivity with smart devices, IoT offers many convenient services for users. Nevertheless, attacking personal data and leaking information can be a consequence of these services [8]. The main reason for this is the heterogeneity and very large scale of objects [9]. On the IoT, traditional security methods cannot be used directly. Therefore, new methods and solutions are required to safeguard IoTs from security threats [10]. In order to ensure an IoT system is secure, it needs attributes such as confidentiality, integrity, authorization, availability, privacy, and authentication [11]. In the IoT area, the question of "How to authenticate objects" is a key question for research. Authentication protocols have been proposed over the years for the purpose of establishing secure sessions in a communication system and confirming the authenticity of these sessions. The session key is created using a public key encryption method in these protocols. Accordingly, [12-15] uses the Extensible Authentication Protocol (EAP). Key Distribution Centres (KDCs) also make use of authentication in [16]. By doing so, each user has only one key that is shared between them. Public key infrastructure (PKI) used in [17, 18] meant authentication was done using public-key cryptography. The Diffie–Hellman protocol, which uses shared keys to authenticate, is another authentication protocol that is commonly used [19-21]. This type of session is assumed to have not been met before and has not yet agreed on a common key. Consequently, they must create a shared key, regardless of whether they are aware of the third party. Recently, biometric techniques (such as fingerprints, iris networks, face recognition etc.) have been used instead of the old methods (i.e. passwords and pinches) to enhance the security of smart devices [21-23]. A biometric system requires physical or biological features, such as fingerprints, ears, and faces. The main problem with these features is their easy acquisition, resulting in their vulnerability to frequent attacks. EEG-based authentication is an alternative method for reducing the likelihood of attacks on authentication systems [24]. The electroencephalogram, or EEG, records electrical activity in the brain through the use of noninvasive electrodes mounted on the head. Compared with other authentication systems, this method has numerous advantages. Despite lost or forgotten passwords, cards, and keys, brainwaves never disappear [25]. A key challenge of the EEG signal in IoT authentication is that its dynamic nature precludes the use of the signal for feature extraction. In spite of creating a secure environment, feature extraction-based protocols with EEG often require a relatively long processing time. On the other hand, a protocol that provides a high level of security despite the challenges of EEG-based authentication is crucial. Therefore, it can be interesting to develop a shared cryptographic key via an EEG protocol using only one signal. In this paper, we present a multilayer, robust, and fast two-stage EEG- and fingerprint-based identification- authentication protocol (EEGFP-Based Security system) to be used in remote healthcare applications. The patient's fingerprint and EEG signal information are received through the mobile application (as an IoT device) and sent to the hospital server via the Internet. After performing pre-processing and matching the received signal with the database signal, the hospital server decides whether to accept or reject the received signal based on the identification protocol. To match the EEG signal at the identification stage, a modified Euclidean distance pattern matching method is proposed as a result of its dynamic nature. By successful identification, the server transmits approval messages and then initiates the second layer of the protocol, which is the authentication process. A generalized Diffie–Hellman protocol with multiple security layers, the so-called Attack-Robust Diffie–Hellman (ARDH), is implemented in the authentication phase. We combine both the EEG signal and the fingerprint, which are unique numbers, in order to generate domain parameters of the Diffie–Hellman protocol instead of creating them randomly. As a result, unlike Diffie–Hellman, our protocol analyzes the equality of these domain parameters in both sides to create multiple security layers. Therefore, the security and robustness of the authentication protocol is improved, and if equality cannot be maintained, the authentication protocol is stopped, and the received signal is considered as an attack. While the suggested method is proposed for the field of healthcare, it is not only limited to this field and may be used in super-security applications as well. The paper is organized as follows: related works are discussed in section 2. Section 3 describes the Electroencephalography. The motivation and problem statement are presented in section 4. In section 5, we discuss the proposed scheme. In section 6, the results of the experiments are presented and discussed. Section 7 provides a security analysis. A Performance Comparison was conducted in section 8. Finally, section 9 focuses on conclusions and future works. 2 RELATED WORKS Our brain generates unique neuronal electrical signals when we perform mental tasks like picturing a shape or performing an event. The brain-computer interface (BCI) can be used to acquire these electrical signals. Research in BCI represents a rapidly growing field of application systems. Its contributions in medical fields ranging from prevention to neuronal rehabilitation after serious injuries. Numerous fields are affected by mind reading and remote communication, including education, self-regulation, production, marketing, security, as well as games and entertainment [26]. It has been pointed out that the EEG can be used for authentication in IoT. Researchers have conducted significant research in the area of brain signal authentication. Jayarathne et al. [27] describe a study of EEG-based biometric authentication in which the authors emphasize the cost-efficient implementation of their systems, regardless of the complexity of the tasks. A major challenge in this regard is the large amount of EEG data that must be processed, transmitted, and stored. It is therefore essential to use EEG compression. Hosseinzadeh et al. [28] have also provided a review paper on ECG-based authentication approaches, providing all the relevant background information on ECG signals and authentication methods. In Nguyen et al., [29], loop compression in EEG data was evaluated in the context of EEG-based user authentication systems. A massive experiment using three EEG data sets has demonstrated that the use of a detoxifier is possible compared to untreated zippers. Most of the studies have employed feature extracting and classification techniques to authenticate EEG signals in IoT. Soni et al. [30] suggest a two-stage authentication method that combines a brain wave pattern as an unlock pattern and a key password. The first level of authentication involves pattern matching. Once the correct pattern of brain signal is provided, the system will then ask for a passkey as the second level. Similarly, Kanaga et al. [31] employed EEG signals and classification for the authentication of IoT devices. The participants are asked to perform a series of events for a specified time period, and their signals are collected. A preprocessing step is performed on these acquired signals, and a set of time and frequency features is extracted and used for training classifiers. Authentication is performed using these classified signals. Because the experimental paradigm is limited in usability and classification accuracy is not satisfactory, identity authentication based on EEG has yet to become commonplace in IoT scenarios. Huang et al. [32] have proposed an audiovisual presentation method for recording subject EEG signals in order to mitigate these problems. Artifacts are removed from the data during the pre-processing step using methods such as reference electrodes, ensemble averaging, and independent component analysis. Adaptive feature selection and bagging ensemble learning algorithms are applied at the feature extraction stage. The authors of [33], Zeng et al. propose a face-based rapid serial visual presentation (RSVP) paradigm for identity authentication. In this paradigm, two kinds of biometric traits, face, and EEG, are combined to generate more specific and stable attributes for authentication. The event-related potential (ERP) components of self-and non-self-faces (including familiar and unfamiliar faces) are investigated, and significant differences are found between different situations. Using this information, an authentication method based on hierarchical discriminant component analysis (HDCA) and genetic algorithms (GA) is proposed to build subject-specific models with optimized fewer channels. In [34], Alladi and Chamola propose a protocol based on hardware security primitives called Physical Unclonable Functions (PUFs). In view of the memory and energy constraints of healthcare IoT devices, this protocol is very lightweight. In order to prove the validity of this protocol, formal security evaluations are performed. Furthermore, they compare it with relevant protocols in the healthcare IoT scenario in terms of computation time and security to show its suitability and robustness. EEG-based two-step approaches are used in [35] to identify the person with high accuracy. To achieve this, support vector machines (SVM) and KNN classification have been employed. A total of 90 EEG signals were recorded from 23 subjects in this study. The results of this study indicate that the accuracy of identification has increased. A person's music listening behavior and their EEG signal are used in [36] to obtain the authentication results, and the EEG signal is extracted both using the feature extraction technique. In [37], the authors describe a brain-based biometric system for smart devices. Specifically, a new psychological protocol has been introduced, which is used to authenticate intelligent devices through a brainstorming session. With the help of experimental and theoretical scenarios, this study examines the challenges posed by biometrics of smart devices. The authors of [38], scrap compression of the raw EEG data is examined in the context of EEG-based user authentication systems. Based on large-scale experiments performed on the three EEG datasets, it has been demonstrated that dissipative compressors can be used as opposed to non-dissipative compressors. The authors of [39] used machine learning and feature extraction techniques to establish authentication in IoT by exhibiting a chain of events to test participants. Using machine learning with EEG signals, a new personal authentication system is presented in [40], which has a graphical password system. In [41], a technique based on IoT for protecting privacy for EEG signal classification is discussed. As compared to its counterparts, this technique outperforms classification accuracy of 88.04%. Table 1 compares the research conducted on authentication (or identification) with the EEG signal and how the identification-authentication process is performed. TABLE 1. Comparison of EEG-based authentication (or identification) Year Ref. No. of Layers Key Exchange No. of Signals Authentication Identification Advantages Disadvantages 2012 [25] 2 ✓ >25 ✓ × Double layer protocol High security Use EEG signal for both stages Character-based image and signal acquisition 2018 [27] 1 × 1 ✓ × Suitable for authentication Monolayer authentication protocol Requires a long signal to extract the appropriate feature 2018 [29] 1 × 1 ✓ × High accuracy Monolayer authentication protocol High processing time 2016 [30] 2 × 6 ✓ × Two-layer protocol High security Use EEG signal for both stages High number of EEG signals Remember the blinking pattern (the corresponding EEG signal) as the unlock pattern 2017 [31] 1 × 1 ✓ × Use for a paralyzed person Single-stage authentication Authentication is purely based on feature extraction 2019 [32] 1 × >4 ✓ × Fast processing as claimed by the author Monolayer authentication protocol Based on a person's visual memory Requires different EEG signals 2019 [33] 2 × 1 ✓ × High accuracy with fewer channels High processing time 2020 [34] 2 ✓ × ✓ × A two-way two-stage authentication protocol with high security Not considered the identification 2013 [35] 2 × >4 × ✓ Highly accurate person identification Two-layer protocol Use EEG signal for both stages High number of EEG signals Classification based 2017 [36] 1 × 1 × ✓ High precision identification method Single-stage method Relatively high processing time for sorting 2018 [37] 1 × 1 ✓ × High matching accuracy Monolayer authentication protocol Suitable for unruly brain state 2019 [38] 1 × 1 ✓ × High accuracy Monolayer authentication protocol Purely feature-based extraction 2019 [39] 1 × 1 ✓ × Relatively high accuracy Monolayer authentication 2020 [40] 2 × × ✓ × Using Graphical passwords instead of alphanumeric passwords authentication More secure Vulnerable to shoulder-surfing attacks 2020 [41] 4 ✓ 1 ✓ × High security and privacy in the storage of patient data High classification accuracy Low processing speed 3 ELECTROENCEPHALOGRAPHY The human brain is an electrochemical organ, and the nerve cells within it transmit electrical signals to the surface of the skull in the microvolt range. EEG can record the electrical activity of the brain by placing electrodes on the head. In contrast to other techniques (e.g. Magnetoencephalography (MEG), Near-Infrared Spectroscopy, Functional Magnetic Resonance Imaging (fMRI), Near-Infrared Spectroscopy (NIRS), and Positron Emission Tomography (PET)), EEG is non-invasive, affordable, and passive [27]. The EEG can be used to diagnose diseases associated with the brain, such as dementia, seizures, psychiatric disorders, and sleep apnea [29]. An analysis of the recorded signal can be done directly by a physician or neuroscientist. Electrode placement on the skull of the brain is carried out according to international standards. Among these standards, the 10-20 system is typical of those proposed by [42] (Figure 1. The 10-10 system [43] and the 10-5 system [44] are extensions of the 10-20 system for acquiring high-resolution EEG signals. FIGURE 1Open in figure viewerPowerPoint EEG 10-20 system, (a) Sagittal plane, (b) axial plane. A1, A2: Ear Lobes; T5,T6,T3,T4: Posterior Temporal Areas; P3,P4: Parietal Areas; F3,F4,F7,F8: Frontal Areas; Fp1,Fp2: Prefrontal Area; O1,O2: Posterior Areas; C3,C4: Central Areas EEGs are typically described in terms of (1) regular activity and (2) transient activity. Bands of rhythmic activity can be classified according to frequency, amplitude, and shape, as well as the sites on the scalp at which they are recorded. The most familiar classification uses EEG waveform frequency [30]. These frequency bands are in some sense matters of nomenclature (i.e. anything that takes place between 8 and 12 Hz can be called "alpha"). Nonetheless, these designations originate from rhythmic activity within a specific frequency range that has a particular distribution over the scalp or is biologically significant. Frequency bands are usually extracted using spectral methods (such as Welch) as implemented in free tools such as EEGLAB or BrainToolbox [45]. The majority of cerebral activity observed in scalp EEGs falls within the range of 1–20 Hz (activity below or above this range may be artefact, under standard clinical recording techniques). Typically, EEG is divided into the delta, theta, alpha, beta, gamma bandwidths to represent the majority of clinically used EEGs [45]. The frequency bands and associated brain states are listed in Table 2 Experimentally, frequency bands are chosen depending on the brain state being explored [27]. TABLE 2. Frequency bands and corresponding brain states Band Frequency (Hz) Brain state Delta 1–4 Primarily associated with deep sleep Theta 4–8 Appear as consciousness slips towards drowsiness Alpha 8–13 Usually found over the occipital region. Indicates relaxed awareness without attention Beta 13–30 Associated with active thinking and concentration Gamma 30–100 Represent binding of different populations of neurons 4 MOTIVATION AND PROBLEM STATEMENT The IoT's security, especially in sensitive applications like healthcare and the military, is one of its biggest challenges. Various researchers have developed protocols for using brain waves to establish security through authentication in these applications. Most of these protocols focus on reducing the size of data by compressing, removing features, and classifying it. Brainwave-based authentication requires using multiple EEG signals at the same time or multiple channels simultaneously. This results in a relatively long processing time for such protocols. Additionally, the dynamic nature of an EEG signal makes it difficult to use one signal as an authentication factor, since the stored signal and the one sent by the user may differ in amplitude and delay. Therefore, it must be possible to perform the authentication process only through the use of a single EEG with a single channel, as is the case with fingerprints. In addition, encryption protocols allow them to authenticate quickly. Consequently, to overcome such problems, this paper presents an approach by using the single-channel EEG signal both for the purpose of identification and authentication, as well as to build various security sub-layers by using the Diffie–Hellman protocol. A genetic algorithm (GA) can, however, facilitate high-speed optimal authentication through the optimization process. A point to be noted is, our method only requires one time series from the EEG, so any channel except those with zero amplitudes can be used. O2 is used here without preference between the channel and any other. 5 PROPOSED SCHEME The proposed patient remote healthcare monitoring system includes five elements: patients' fingerprint and EEG sensors, smart device, hospital medical server, nurse, and emergency services. With the proposed protocol, lightweight secure mutual identification, authentication, and session key generation can be achieved efficiently and the patient's anonymity is preserved. This protocol is called the EEG-Fingerprint Identification-Authentication Protocol (EEGFP-IAP). Figure 2 shows a schematic of our security system. A smart device collects sensor data from the patient and sends it to the hospital's medical server. The medical server is the main entity of the proposed system. After performing identification and authentication, this server decides whether to reject or accept the received information. This server will send an alarm signal to the nurse if the identification-authentication process is successful. As part of the nurse's care, an ambulance is dispatched to the patient's address. Four phases of the proposed scheme are as follows: patient registration identification authentication emergency scenario FIGURE 2Open in figure viewerPowerPoint The scheme of the proposed patient monitoring system Registering is done through a secure channel. It is worth to mention that in some IoT health authentication studies the phase of patient registration is only performed once (like [21, 22]). However, in this paper the patient's EEG signal is used for identification and authentication, and this signal is dynamic in nature. Additionally, it increases the security of the protocol as much as possible. 5.1 Patient registration The patient must announce the hospital on-time ID digits with his/her voice during registration in order to activate the smart device. The smart device then receives information from patient fingerprint and EEG signal sensors via Bluetooth, ZigBee, or infrared communication technologies. After that, it adapts the patient's fingerprint to increase security. As the smart device is near the patient, security considerations do not need to be taken into account. If the smart device matches the received fingerprint with the fingerprint stored on it, all patient information is sent to the hospital server through a secure channel. Figure 3 shows the patient registration process. FIGURE 3Open in figure viewerPowerPoint Patinet registration phase 5.2 Identification As soon as the hospital medical server receives the patient's information, it checks his/her ID. The patient file is opened and the identification process begins if this ID is available on the server. The identification is performed by EEG signal. Initially, noise in the EEG signal is eliminated with an FIR filter. Next as a security sublayer, the server compares the maximum amplitude of the signal stored on the server with that received from the smart device. The identification process will continue if there is a difference of less than 25%. Otherwise, the identification process will be stopped and the received information considered an attack. There should be noted that the number 25% is the primary filter determined by analyzing the various signals and ensures that the patient signal is accepted by the security sublayer and if any other signals pass through, they will below. The two signals in Figure 4a for instance, do not have the same amplitude range, which means the matching operation cannot be performed, and the identification operation is terminated. On the other hand, the two signals are shown in Figure 4b roughly have the same amplitude range, making them matchable. FIGURE 4Open in figure viewerPowerPoint Two signal matching constraint based on maximum amplitude difference; (a) unacceptable, (b) acceptable The proposed identification procedure is an improvement of the Euclidean distance-based pattern matching introduced in [34], which has been chosen due to its minimal processing time and ease of implementation. The Euclidean distance is the distance between two points in Euclidean space (Eq. 1. Figure 5 displays the alignment between the nth point of one time series and the n-th point of another time series. If the distance is small, then the two points may share many similarities, otherwise, the two series appear dramatically different from one another. E D = x 1 − z 1 2 + x 2 − z 2 2 + ⋯ + x N − z N 2 , (1)where { x 1 , x 2 , l c d o t s x N } and { z 1 , z 2 , l c d o t s z N } are elements in two trails with N samples, x, and z, respectively. FIGURE 5Open in figure viewerPowerPoint Euclidean distance of two series Euclidean distance is not an appropriate solution for matching the two signals stored in the database and sent by the patient due to the dynamic nature of the EEG signal. As a result, we present the generalized Euclidean distance in this paper. In the proposed method, the Euclidean distance is calculated by dividing the absolute difference in amplitudes of the two signals by the maximum amplitude of the reference signal, A D = ∑ i A r i − ∑ i A l i max A r , (2)where Ar is the amplitude of the reference signal (stored in the medical server) and Al is the amplitude of the target signal (received from the patient). We set AD = 10 as an acceptance threshold after analyzing the different signals. Identification fails if AD > 10, which means the received signal is a fake and was an attack. Otherwise, the authentication process begins. An overview of the identification process is shown in Figure 6 FIGURE 6Open in figure viewerPowerPoint The general framework of the identification stage 5.3 Authentication After the patient has been successfully identified, the authentication phase begins. A combination of fingerprint information and the patient's EEG signal is used in this step. The generalized Diffie–Hellman protocol is used for authentication, including a number of security sublayers. Additionally, to further increase security, a genetic algorithm (GA) is used for optimal authentication. The non-optimal authentication method is called EEGFP-AP (EEG-Fingerprint Authentication Protocol) and the optimal method is called EEGFP-GAAP (EEG-Fingerprint Genetic Algorithm-based Authentication Protocol). Following, details of EEGFP-AP and EEGFP_GAAP are discussed. 5.3.1 EEGFP-AP protocol The basic Diffie–Hellman protocol is vulnerable to attacks such as the man-in-the-middle attack. In addition, the algorithm uses a lot of processing time, since it randomly generates its domain parameters (p, q). For this reason and to establish a very secure session, in this paper, we obtain the domain parameters from the EEG signal and fingerprint. These numbers are unique, which increases the security of the proposed method, which takes th