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1. Behavioral fingerprinting of Internet‐of‐Things devices

   https://doi.org/10.1002/widm.1337  

   Bezawada, Bruhadeshwar ; Ray, Indrakshi ; Ray, Indrajit ( September 2019 , WIREs Data Mining and Knowledge Discovery)

   Rapid advances in the Internet‐of‐Things (IoT) domain have led to the development of several useful and interesting devices that have enhanced the quality of home living and industrial automation. The vulnerabilities in the IoT devices have rendered them susceptible to compromise and forgery. The problem of device authentication, that is, the question of whether a device's identity is what it claims to be, is still an open problem. Device fingerprinting seems to be a promising authentication mechanism. Device fingerprinting profiles a device based on information available about the device and generate a robust, verifiable and unique identity for the device. Existing approaches for device fingerprinting may not be feasible or cost‐effective for the IoT domain due to the resource constraints and heterogeneity of the IoT devices. Due to resource and cost constraints, behavioral fingerprinting provides promising directions for fingerprinting IoT devices. Behavioral fingerprinting allows security researchers to understand the behavioral profile of a device and to establish some guidelines regarding the device operations. In this article, we discuss existing approaches for behavioral fingerprinting of devices in general and evaluate their applicability for IoT devices. Furthermore, we discuss potential approaches for fingerprinting IoT devices and give an overview of some of the preliminary attempts to fingerprint IoT devices. We conclude by highlighting the future research directions for fingerprinting in the IoT domain.

   This article is categorized under:

   Application Areas > Science and Technology

   Application Areas > Internet

   Technologies > Machine Learning

   Application Areas > Industry Specific Applications

    

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2. P-HIP: A Lightweight and Privacy-Aware Host Identity Protocol for Internet of Things

   https://doi.org/10.1109/JIOT.2020.3009024  

   Hossain, Mahmud ; Hasan, Ragib ( July 2020 , IEEE Internet of Things Journal)

   null (Ed.)

   The Host Identity Protocol (HIP) has emerged as the most suitable solution to uniquely identify smart devices in the mobile and distributed Internet of Things (IoT) systems, such as smart cities, homes, cars, and healthcare. The HIP provides authentication methods that enable secure communications between HIP peers. However, the authentication methods provided by the HIP cannot be adopted by the IoT devices with limited processing power because of the computation-intensive cryptographic operations involved in hash generation, signature validation, and session key establishment. Moreover, IoT devices cannot utilize the HIP as is to communicate securely in the low power and lossy networks as there is a considerable communication overhead, such as packet fragmentation and reassembly, for exchanging certificates over a lossy link. Additionally, the use of static host identifiers makes IoT devices vulnerable to cyber espionage and user-targeted attacks. In this article, we propose an authentication scheme, P-HIP, that protects the identity privacy of an IoT device by enabling the device to compute and use unique host identifiers from networks to networks and sessions to sessions. To make the HIP suitable for resource-constrained IoT devices, P-HIP provides methods that unburden IoT devices from computation-intensive operations, such as modular exponentiation, involved in authentication and session-key exchange. Additionally, P-HIP minimizes the communication overheads for exchanging certificates in lossy networks. We implement a prototype of P-HIP on Contiki enabled IoT that shows P-HIP can reduce computation costs, communication overheads, and the session-key establishment time when used by low-powered devices in a lossy network. 

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3. Behavioral Fingerprinting of IoT Devices

   https://doi.org/10.1145/3266444.3266452  

   Bezawada, Bruhadeshwar ; Bachani, Maalvika ; Peterson, Jordan ; Shirazi, Hossein ; Ray, Indrakshi ; Ray, Indrajit ( October 2018 , Proceedings of the 2018 Workshop on Attacks and Solutions in Hardware Security)

   The Internet-of-Things (IoT) has brought in new challenges in device identification --what the device is, and authentication --is the device the one it claims to be. Traditionally, the authentication problem is solved by means of a cryptographic protocol. However, the computational complexity of cryptographic protocols and/or problems related to key management, render almost all cryptography based authentication protocols impractical for IoT. The problem of device identification is, on the other hand, sadly neglected. Almost always an artificially created identity is softly associated with the device. We believe that device fingerprinting can be used to solve both these problems effectively. In this work, we present a methodology to perform IoT device behavioral fingerprinting that can be employed to undertake strong device identification. A device behavior is approximated using features extracted from the network traffic of the device. These features are used to train a machine learning model that can be used to detect similar device-types. We validate our approach using five-fold cross validation; we report a identification rate of 93-100 and a mean accuracy of 99%, across all our experiments. Furthermore, we show preliminary results for fingerprinting device categories, i.e., identifying different devices having similar functionality. 

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4. Securing Internet of Things Devices Using the Network Context

   https://doi.org/10.1109/TII.2019.2954100  

   Trnka, Michal ; Svacina, Jan ; Cerny, Tomas ; Song, Eunjee ; Hong, Jiman ; Bures, Miroslav ( November 2019 , IEEE Transactions on Industrial Informatics)

   Internet of Things (IoT) devices have been widely adopted in recent years. Unlike conventional information systems, IoT solutions have greater access to real-world contextual data and are typically deployed in an environment that cannot be fully controlled, and these circumstances create new challenges and opportunities. In this article, we leverage the knowledge that an IoT device has about its network context to provide an additional security factor. The device periodically scans a network and reports a list of all devices in the network. The server analyzes movements in the network and subsequently reacts to suspicious events. This article describes how our method can detect network changes, retrieved only from scanning devices in the network. To demonstrate the proposed solution, we perform a multi-week case study on a network with hundreds of active devices and confirm that our method can detect network anomalies or changes. 

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5. MicPrint: acoustic sensor fingerprinting for spoof-resistant mobile device authentication

   https://doi.org/10.1145/3360774.3360801  

   Lee, Yongwoo ; Li, Jingjie ; Kim, Younghyun ( November 2019 , Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services)

   Smartphones are the most commonly used computing platform for accessing sensitive and important information placed on the Internet. Authenticating the smartphone's identity in addition to the user's identity is a widely adopted security augmentation method since conventional user authentication methods, such as password entry, often fail to provide strong protection by itself. In this paper, we propose a sensor-based device fingerprinting technique for identifying and authenticating individual mobile devices. Our technique, called MicPrint, exploits the unique characteristics of embedded microphones in mobile devices due to manufacturing variations in order to uniquely identify each device. Unlike conventional sensor-based device fingerprinting that are prone to spoofing attack via malware, MicPrint is fundamentally spoof-resistant since it uses acoustic features that are prominent only when the user blocks the microphone hole. This simple user intervention acts as implicit permission to fingerprint the sensor and can effectively prevent unauthorized fingerprinting using malware. We implement MicPrint on Google Pixel 1 and Samsung Nexus to evaluate the accuracy of device identification. We also evaluate its security against simple raw data attacks and sophisticated impersonation attacks. The results show that after several incremental training cycles under various environmental noises, MicPrint can achieve high accuracy and reliability for both smartphone models. 

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