Biblio

This paper dives into the growing world of IoT botnets that have taken the world by storm in the past five years. Though alone an IP camera cannot produce enough traffic to be considered a DDoS. But a botnet that has over 150,000 connected IP cameras can generate as much as 1 Tbps in traffic. Botnets catch many by surprise because their attacks and infections may not be as apparent as a DDoS, some other cases include using these cameras and printers for extracting information or quietly mine cryptocurrency at the IoT device owner's expense. Here we analyze damages on IoT hacking and define botnet architecture. An overview of Mirai botnet and cryptojacking provided to better understand the IoT botnets.

To measure machinery vibration, a sensor system consisting of a 3-axis accelerometer, ADXL345, attached to a self-contained system-on-a-chip with integrated Wi-Fi capabilities, ESP8266, is a low-cost solution. In this work, we first show that in such a system, the widely used direct-read-and-send method which samples and sends individually acquired vibration data points to the server is not effective, especially using Wi-Fi connection. We show that the micro delays in each individual data transmission will limit the sensor sampling rate and will also affect the time of the acquired data points not evenly spaced. Then, we propose that vibration should be sampled in batches before sending the acquired data out from the sensor node. The vibration for each batch should be acquired continuously without any form of interruption in between the sampling process to ensure the data points are evenly spaced. To fill the data gaps between the batches, we propose the use of compressive sampling technique. Our experimental results show that the maximum sampling rate of the direct-read-and-send method is 350Hz with a standard uncertainty of 12.4, and the method loses more information compared to our proposed solution that can measure the vibration wirelessly and continuously up to 633Hz. The gaps filled using compressive sampling can achieve an accuracy in terms of mean absolute error (MAE) of up to 0.06 with a standard uncertainty of 0.002, making the low-cost vibration sensor node a cost-effective solution.

In November 2019, the government of Iran enforced a week-long total Internet blackout that prevented the majority of Internet connectivity into and within the nation. This work elaborates upon the Iranian Internet blackout by characterizing the event through Internet-scale, near realtime network traffic measurements. Beginning with an investigation of compromised machines scanning the Internet, nearly 50 TB of network traffic data was analyzed. This work discovers 856,625 compromised IP addresses, with 17,182 attributed to the Iranian Internet space. By the second day of the Internet shut down, these numbers dropped by 18.46% and 92.81%, respectively. Empirical analysis of the Internet-of-Things (IoT) paradigm revealed that over 90% of compromised Iranian hosts were fingerprinted as IoT devices, which saw a significant drop throughout the shutdown (96.17% decrease by the blackout's second day). Further examination correlates BGP reachability metrics and related data with geolocation databases to statistically evaluate the number of reachable Iranian ASNs (dropping from approximately 1100 to under 200 reachable networks). In-depth investigation reveals the top affected ASNs, providing network forensic evidence of the longitudinal unplugging of such key networks. Lastly, the impact's interruption of the Bitcoin cryptomining market is highlighted, disclosing a massive spike in unsuccessful (i.e., pending) transactions. When combined, these network traffic measurements provide a multidimensional perspective of the Iranian Internet shutdown.

With the advancement in the growth of Internet-of-Things (IoT), its number of applications has also increased such as in healthcare, smart cities, vehicles, industries, household appliances, and Smart Grids (SG). One of the major applications of IoT is the SG and smart meter which consists of a large number of internet-connected sensors and can communicate bi-directionally in real-time. The SG network involves smart meters, data collectors, generators, and sensors connected with the internet. SG networks involve the generation, distribution, transmission, and consumption of electrical power supplies. It consists of Household Area Network (HAN), and Neighborhood Area Network (NAN) for communication. Smart meters can communicate bidirectionally with consumers and provide real-time information to utility offices. But this communication channel is a wide-open network for data transmission. Therefore, it makes the SG network and smart meter vulnerable to outside hacker and various Cyber-Physical System (CPS) attacks such as False Data Injection (FDI), inserting malicious data, erroneous data, manipulating the sensor reading values. Here cryptography techniques can play a major role along with the private blockchain model for secure data transmission in smart meters. Hence, to overcome these existing issues and challenges in smart meter communication we have proposed a blockchain-based system model for secure communication along with a novel Advanced Elliptic Curve Cryptography Digital Signature (AECCDS) algorithm in Fog Computing (FC) environment. Here FC nodes will work as miners at the edge of smart meters for secure and real-time communication. The algorithm is implemented using iFogSim, Geth version 1.9.25, Ganache, Truffle for compiling smart contracts, Anaconda (Python editor), and ATOM as language editor for the smart contracts.

Recent years have seen an increase in medical big data, which can be attributed to a paradigm shift experienced in medical data sharing induced by the growth of medical technology and the Internet of Things. The evidence of this potential has been proved during the recent covid-19 pandemic, which was characterised by the use of medical wearable devices to help with the medical data exchange between the healthcare providers and patients in a bid to contain the pandemic. However, the use of these technologies has also raised questions and concerns about security and privacy risks. To assist in resolving this issue, this paper proposes a blockchain-based access control framework for managing access to users’ medical data. This is facilitated by using a smart contract on the blockchain, which allows for delegated access control and secure user authentication. This solution leverages blockchain technology’s inherent autonomy and immutability to solve the existing access control challenges. We have presented the solution in the form of a medical wearable sensor prototype and a mobile app that uses the Ethereum blockchain in a real data sharing control scenario. Based on the empirical results, the proposed solution has proven effective. It has the potential to facilitate reliable data exchange while also protecting sensitive health information against potential threats. When subjected to security analysis and evaluation, the system exhibits performance improvements in data privacy levels, high security and lightweight access control design compared to the current centralised access control models.

Multi-factor authentication (MFA) systems are being deployed for user authentication in online and personal device systems, whereas physical spaces mostly rely on single-factor authentication; examples are entering offices and homes, airport security, and classroom attendance. The Internet of Things (IoT) growth and market interest has created a diverse set of low-cost and flexible sensors and actuators that can be used for MFA. However, combining multiple authentication factors in a physical space adds several challenges, such as complex deployment, reduced usability, and increased energy consumption. We introduce MAFIA (Multi-layered Architecture For IoT-based Authentication), a novel architecture for co-located user authentication composed of multiple IoT devices. In MAFIA, we improve the security of physical spaces while considering usability, privacy, energy consumption, and deployment complexity. MAFIA is composed of three layers that define specific purposes for devices, guiding developers in the authentication design while providing a clear understanding of the trade-offs for different configurations. We describe a case study for an Automated Classroom Attendance System, where we evaluated three distinct types of authentication setups and showed that the most secure setup had a greater usability penalty, while the other two setups had similar attributes in terms of security, privacy, complexity, and usability but varied highly in their energy consumption.

Anomaly detection (AD) is very important across several real-world problems in the heavy industries and Internet-of-Things (IoT) domains. Traditional methods so far have categorized anomaly detection into (a) unsupervised, (b) semi-supervised and (c) supervised techniques. A relatively unexplored direction is the development of context aware anomaly detection systems which can build on top of any of these three techniques by using side information. Context can be captured from a different modality such as semantic graphs encoding grouping of sensors governed by the physics of the asset. Process flow diagrams of an operational plant depicting causal relationships between sensors can also provide useful context for ML algorithms. Capturing such semantics by itself can be pretty challenging, however, our paper mainly focuses on, (a) designing and implementing effective anomaly detection pipelines using sparse Gaussian Graphical Models with various statistical distance metrics, and (b) differentiating these pipelines by embedding contextual semantics inferred from graphs so as to obtain better KPIs in practice. The motivation for the latter of these two has been explained above, and the former in particular is well motivated by the relatively mediocre performance of highly parametric deep learning methods for small tabular datasets (compared to images) such as IoT sensor data. In contrast to such traditional automated deep learning (AutoAI) techniques, our anomaly detection system is based on developing semantics-driven industry specific ML pipelines which perform scalable computation evaluating several models to identify the best model. We benchmark our AD method against state-of-the-art AD techniques on publicly available UCI datasets. We also conduct a case study on IoT sensor and semantic data procured from a large thermal energy asset to evaluate the importance of semantics in enhancing our pipelines. In addition, we also provide explainable insights for our model which provide a complete perspective to a reliability engineer.

In vehicular cyber-physical systems, the mounted cameras on the vehicles, together with the fixed roadside cameras, can produce pictorial data for multiple purposes. In this process, ensuring the security and privacy of vehicles while guaranteeing efficient data transmission among vehicles is critical. This motivates us to propose a trusted anonymous authentication scheme for vehicular cyber-physical systems and Internet-of-Things. Our scheme is designed based on a three-tier architecture which contains cloud layer, fog layer, and user layer. It utilizes bilinear-free certificateless signcryption to realize a secure and trusted anonymous authentication efficiently. We verify its effectiveness through theoretical analyses in terms of correctness, security, and efficiency. Furthermore, our simulation results demonstrate that the communication overhead, the computation overhead, and the packet loss rate of the proposed scheme are significantly better than those of the state-of-the-art techniques. Particularly, the proposed scheme can speed up the computation process at least 10× compared to all the state-of-the-art approaches.

Nowadays, physical tampering and counterfeiting of electronic devices are still an important security problem and have a great impact on large-scale and distributed applications, such as Internet-of-Things. Physical Unclonable Functions (PUFs) have the potential to be a fundamental means to guarantee intrinsic hardware security, since they promise immunity against most of known attack models. However, inner nature of PUF circuits hinders a wider adoption since responses turn out to be noisy and not stable during time. To overcome this issue, most of PUF implementations require a fuzzy extraction scheme, able to recover responses stability by exploiting error correction codes (ECCs). In this paper, we propose a Reed-Muller (RM) ECC design, meant to be embedded into a fuzzy extractor, that can be efficiently configured in terms of area/delay constraints in order to get reliable responses from PUFs. We provide implementation details and experimental evidences of area/delay efficiency through syntheses on medium-range FPGA device.

The Internet of Things (IoT) is rapidly emerging as one of the dominant computing paradigms of this decade. Applications range from in-home entertainment to large-scale industrial deployments such as controlling assembly lines and monitoring traffic. While IoT devices are in many respects similar to traditional computers, user expectations and deployment scenarios as well as cost and hardware constraints are sufficiently different to create new security challenges as well as new opportunities. This is especially true for large-scale IoT deployments in which a central entity deploys and controls a large number of IoT devices with minimal human interaction. Like traditional computers, IoT devices are subject to attack and compromise. Large IoT deployments consisting of many nearly identical devices are especially attractive targets. At the same time, recovery from root compromise by conventional means becomes costly and slow, even more so if the devices are dispersed over a large geographical area. In the worst case, technicians have to travel to all devices and manually recover them. Data center solutions such as the Intelligent Platform Management Interface (IPMI) which rely on separate service processors and network connections are not only not supported by existing IoT hardware, but are unlikely to be in the foreseeable future due to the cost constraints of mainstream IoT devices. This paper presents CIDER, a system that can recover IoT devices within a short amount of time, even if attackers have taken root control of every device in a large deployment. The recovery requires minimal manual intervention. After the administrator has identified the compromise and produced an updated firmware image, he/she can instruct CIDER to force the devices to reset and to install the patched firmware on the devices. We demonstrate the universality and practicality of CIDER by implementing it on three popular IoT platforms (HummingBoard Edge, Raspberry Pi Compute Module 3 and Nucleo-L476RG) spanning the range from high to low end. Our evaluation shows that the performance overhead of CIDER is generally negligible.

Due to the proliferation of Internet-of-Things (IoT) environments, humans working with heterogeneous, smart objects in public IoT environments become more popular than ever before. This situation often requires to establish trust relationships between a user and a smart object for their secure interactions, but without the presence of prior interactions. In this work, we are interested in how a smart object can grant an access right to a human user in the absence of any prior knowledge in which some users may be malicious aiming to breach security goals of the IoT system. To solve this problem, we propose a trust-aware, role-based access control system, namely TARAS, which provides adaptive authorization to users based on dynamic trust estimation. In TARAS, for the initial trust establishment, we take a multidisciplinary approach by adopting the concept of I-sharing from psychology. The I-sharing follows the rationale that people with similar roles and traits are more likely to respond in a similar way. This theory provides a powerful tool to quickly establish trust between a smart object and a new user with no prior interactions. In addition, TARAS can adaptively filter malicious users out by revoking their access rights based on adaptive, dynamic trust estimation. Our experimental results show that the proposed TARAS mechanism can maximize system integrity in terms of correctly detecting malicious or benign users while maximizing service availability to users particularly when the system is fine-tuned based on the identified optimal setting in terms of an optimal trust threshold.

Training the future cybersecurity workforce to respond to emerging threats requires introduction of novel educational interventions into the cybersecurity curriculum. To be effective, these interventions have to incorporate trending knowledge from cybersecurity and other related domains while allowing for experiential learning through hands-on experimentation. To date, the traditional interdisciplinary approach for cybersecurity training has infused political science, law, economics or linguistics knowledge into the cybersecurity curriculum, allowing for limited experimentation. Cybersecurity students were left with little opportunity to acquire knowledge, skills, and abilities in domains outside of these. Also, students in outside majors had no options to get into cybersecurity. With this in mind, we developed an interdisciplinary course for experiential learning in the fields of cybersecurity and interaction design. The inaugural course teaches students from cybersecurity, user interaction design, and visual design the principles of designing for secure use - or secure design - and allows them to apply them for prototyping of Internet-of-Things (IoT) products for smart homes. This paper elaborates on the concepts of secure design and how our approach enhances the training of the future cybersecurity workforce.

Autonomic resource management for distributed edge computing systems provides an effective means of enabling dynamic placement and adaptation in the face of network changes, load dynamics, and failures. However, adaptation in-and-of-itself offers a side channel by which malicious entities can extract valuable information. An attacker can take advantage of autonomic resource management techniques to fool a system into misallocating resources and crippling applications. Using a few scenarios, we outline how attacks can be launched using partial knowledge of the resource management substrate - with as little as a single compromised node. We argue that any system that provides adaptation must consider resource management as an attack surface. As such, we propose ADAPT2, a framework that incorporates concepts taken from Moving-Target Defense and state estimation techniques to ensure correctness and obfuscate resource management, thereby protecting valuable system and application information from leaking.

This work takes a novel approach to classifying the behavior of devices by exploiting the single-purpose nature of IoT devices and analyzing the complexity and variance of their network traffic. We develop a formalized measurement of complexity for IoT devices, and use this measurement to precisely tune an anomaly detection algorithm for each device. We postulate that IoT devices with low complexity lead to a high confidence in their behavioral model and have a correspondingly more precise decision boundary on their predicted behavior. Conversely, complex general purpose devices have lower confidence and a more generalized decision boundary. We show that there is a positive correlation to our complexity measure and the number of outliers found by an anomaly detection algorithm. By tuning this decision boundary based on device complexity we are able to build a behavioral framework for each device that reduces false positive outliers. Finally, we propose an architecture that can use this tuned behavioral model to rank each flow on the network and calculate a trust score ranking of all traffic to and from a device which allows the network to autonomously make access control decisions on a per-flow basis.

Mobile ad hoc network (MANET) is a kind of mobile multi-hop network which can transmit data through intermediate nodes, it has been widely used and become important since the growing of the market of Internet of Things (IoT). However, the transmissions on MANET are vulnerable, it usually suffered with many internal or external attacks, and the research on security topics of MANET are becoming more and more hot recently. Blackhole Attack is one of the most famous attacks to MANET. In this paper, we focus on the Blackhole Attack in AODV protocol, and use NS-3 network simulator to study the impact of Blackhole Attack on network performance parameters, such as the Throughput, End-to-End Delay and Packet Loss Rate. We further analyze the changes in network performance by adjusting the number of blackhole nodes and total nodes, and the movement speed of mobile nodes. The experimental results not only reflect the behaviors of the Blackhole Attack and its damage to the network, but also provide the characteristics of Blackhole Attacks clearly. This is helpful to the research of Blackhole Attack feature extraction and MANET security measurement.

The integration of Internet-of-Things and pervasive computing in medical devices have made the modern healthcare system “smart.” Today, the function of the healthcare system is not limited to treat the patients only. With the help of implantable medical devices and wearables, Smart Healthcare System (SHS) can continuously monitor different vital signs of a patient and automatically detect and prevent critical medical conditions. However, these increasing functionalities of SHS raise several security concerns and attackers can exploit the SHS in numerous ways: they can impede normal function of the SHS, inject false data to change vital signs, and tamper a medical device to change the outcome of a medical emergency. In this paper, we propose HealthGuard, a novel machine learning-based security framework to detect malicious activities in a SHS. HealthGuard observes the vital signs of different connected devices of a SHS and correlates the vitals to understand the changes in body functions of the patient to distinguish benign and malicious activities. HealthGuard utilizes four different machine learning-based detection techniques (Artificial Neural Network, Decision Tree, Random Forest, k-Nearest Neighbor) to detect malicious activities in a SHS. We trained HealthGuard with data collected for eight different smart medical devices for twelve benign events including seven normal user activities and five disease-affected events. Furthermore, we evaluated the performance of HealthGuard against three different malicious threats. Our extensive evaluation shows that HealthGuard is an effective security framework for SHS with an accuracy of 91 % and an F1 score of 90 %.

Introduction of IoT is a big step towards the convergence of physical and virtual world as everyday objects are connected to the internet nowadays. But due to its diversity and resource constraint nature, the security of these devices in the real world has become a major challenge. Although a number of security frameworks have been suggested to ensure the security of IoT devices, frameworks for auditing this security are rare. We propose an open-source framework to audit the security of IoT devices covering hardware, firmware and communication vulnerabilities. Using existing open-source tools, we formulate a modular approach towards the implementation of the proposed framework. Standout features in the suggested framework are its modular design, extensibility, scalability, tools integration and primarily autonomous nature. The principal focus of the framework is to automate the process of auditing. The paper further mentions some tools that can be incorporated in different modules of the framework. Finally, we validate the feasibility of our framework by auditing an IoT device using proposed toolchain.

Internet of Things is nowadays growing faster than ever before. Operators are planning or already creating dedicated networks for this type of devices. There is a need to create dedicated solutions for this type of network, especially solutions related to information security. In this article we present a mechanism of security-aware routing, which takes into account the evaluation of trust in devices and packet flows. We use trust relationships between flows and network nodes to create secure SDN paths, not ignoring also QoS and energy criteria. The system uses SDN infrastructure, enriched with Cognitive Packet Networks (CPN) mechanisms. Routing decisions are made by Random Neural Networks, trained with data fetched with Cognitive Packets. The proposed network architecture, implementing the security-by-design concept, was designed and is being implemented within the SerIoT project to demonstrate secure networks for the Internet of Things (IoT).

Designing secure, scalable, and resilient IoT networks is a challenging task because of resource-constrained devices and no guarantees of reliable network connectivity. Fog computing improves the resiliency of IoT, but its security model assumes that fog nodes are fully trusted. We relax this latter constraint by proposing a solution that guarantees confidentiality of messages exchanged through semi-honest fog nodes thanks to a lightweight proxy re-encryption scheme. We demonstrate the feasibility of the solution by applying it to IoT networks of low-power devices through experiments on microcontrollers and ARM-based architectures.

When relying on public key infrastructure (PKI) for authentication, whether a party can be trusted primarily depends on its certificate status. Bob's certificate status can be retrieved by Alice through her interaction with Certificate Authority (CA) in the PKI. More specifically, Alice can download Certificate Revocation List (CRL) and then check whether the serial number of the Bob's certificate appears in this list. If not found, Alice knows that Bob can be trusted. Once downloaded, a CRL can be used offline for arbitrary many times till it expires, which saves the bandwidth to an extreme. However, if the number of revoked certificates becomes too large, the size of the CRL will exceed the RAM of Alice's device. This conflict between bandwidth and RAM consumption becomes even more challenging for the Internet-of-Things (IoT), since the IoT end-devices is usually constrained by both factors. To solve this problem in PKI-based authentication in IoT, we proposed two novel lightweight CRL protocols with maximum flexibility tailored for constrained IoT end-devices. The first one is based on generalized Merkle hash tree and the second is based on Bloom filter. We also provided quantitative theorems for CRL parameter configuration, which help strike perfect balance among bandwidth, RAM usage and security in various practical IoT scenarios. Furthermore, we thoroughly evaluated the proposed CRL protocols and exhibited their outstanding efficiency in terms of RAM and bandwidth consumption. In addition, our formal treatment of the security of a CRL protocol can also be of independent interest.

The healthcare sector is exploring the incorporation of digital solutions in order to improve access, reduce costs, increase quality and enhance their capacity in reaching a higher number of citizens. However, this opens healthcare organisations' systems to external elements used within or beyond their premises, new risks and vulnerabilities in what regards cyber threats and incidents. We propose the creation of a Security Assessment as a Service (SAaaS) crosslayered system that is able to identify vulnerabilities and proactively assess and mitigate threats in an IT healthcare ecosystem exposed to external devices and interfaces, considering that most users are not experts (even technologically illiterate") in cyber security and, thus, unaware of security tactics or policies whatsoever. The SAaaS can be integrated in an IT healthcare environment allowing the monitoring of existing and new devices, the limitation of connectivity and privileges to new devices, assess a device's cybersecurity risk and - based on the device's behaviour - the assignment and revoking of privileges. The SAaaS brings a controlled cyber aware environment that assures security, confidentiality and trust, even in the presence of non-trusted devices and environments.

Vehicular Ad-hoc NETwork (VANET) is a vital transportation technology that facilitates the vehicles to share sensitive information (such as steep-curve warnings and black ice on the road) with each other and with the surrounding infrastructure in real-time to avoid accidents and enable comfortable driving experience.To achieve these goals, VANET requires a secure environment for authentic, reliable and trusted information dissemination among the network entities. However, VANET is prone to different attacks resulting in the dissemination of compromised/false information among network nodes. One way to manage a secure and trusted network is to introduce trust among the vehicular nodes. To this end, various Trust Models (TMs) are developed for VANET and can be broadly categorized into three classes, Entity-oriented Trust Models (ETM), Data oriented Trust Models (DTM) and Hybrid Trust Models (HTM). These TMs evaluate trust based on the received information (data), the vehicle (entity) or both through different mechanisms. In this paper, we present a comparative study of the three TMs. Furthermore, we evaluate these TMs against the different trust, security and quality-of-service related benchmarks. Simulation results revealed that all these TMs have deficiencies in terms of end-to-end delays, event detection probabilities and false positive rates. This study can be used as a guideline for researchers to design new efficient and effective TMs for VANET.

The internet-of-things (IoT) consists of embedded devices and their networks of communication as they form decentralized frameworks of ubiquitous computing services. Within such decentralized systems the potential for malicious actors to impact the system is significant, with far-reaching consequences. Hence this work addresses the challenge of providing IoT systems engineers with a framework to elicit privacy and security design considerations, specifically for indoor adaptive smart environments. It introduces a new ambient intelligence indoor adaptive environment framework (CORE) which leverages multiple forms of data, and aims to elicit the privacy and security needs of this representative system. This contributes both a new adaptive IoT framework, but also an approach to systematically derive privacy and security design requirements via a combined and modified OCTAVE-Allegro and Privacy-by-Design methodology. This process also informs the future developments and evaluations of the CORE system, toward engineering more secure and private IoT systems.

Popularization of the Internet-of-Things (IoT) has brought widespread concerns on IoT security, especially in face of several recent security incidents related to IoT devices. Due to the resource-constrained nature of many IoT devices, security offloading has been proposed to provide good-enough security for IoT with minimum overhead on the devices. In this paper, we investigate the inevitable risk associated with security offloading: the unprotected and unmonitored transmission from IoT devices to the offloaded security mechanisms. An important challenge in modeling the security risk is the dynamic nature of IoT due to demand fluctuations and infrastructure instability. We propose a stochastic model to capture both the expected and worst-case security risks of an IoT system. We then propose a framework to efficiently address the optimal robust deployment of security mechanisms in IoT. We use results from extensive simulations to demonstrate the superb performance and efficiency of our approach compared to several other algorithms.

While the Internet of Things (IoT) is embraced as important tools for efficiency and productivity, it is becoming an increasingly attractive target for cybercriminals. This work represents the first endeavor to develop practical Puncturable Attribute Based Encryption schemes that are light-weight and applicable in IoTs. In the proposed scheme, the attribute-based encryption is adopted for fine grained access control. The secret keys are puncturable to revoke the decryption capability for selected messages, recipients, or time periods, thus protecting selected important messages even if the current key is compromised. In contrast to conventional forward encryption, a distinguishing merit of the proposed approach is that the recipients can update their keys by themselves without key re-issuing from the key distributor. It does not require frequent communications between IoT devices and the key distribution center, neither does it need deleting components to expunge existing keys to produce a new key. Moreover, we devise a novel approach which efficiently integrates attribute-based key and punctured keys such that the key size is roughly the same as that of the original attribute-based encryption. We prove the correctness of the proposed scheme and its security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. We also implement the proposed scheme on Raspberry Pi and observe that the computation efficiency of the proposed approach is comparable to the original attribute-based encryption. Both encryption and decryption can be completed within tens of milliseconds.