Recent literature has demonstrated the improved data discovery and delivery efficiency gained through applying the named data networking (NDN) to a variety of information-centric Internet of things (IoT) applications. However, from the data security perspective, the development of NDN-IoT raises several new authentication challenges. Particularly, NDN-IoT authentication requires per-packet-level signatures, thus imposing intolerably high computational and time costs on the resource-poor IoT end devices. This paper proposes an effective solution by seamlessly integrating the lightweight and unforgeable physical-layer identity (PHY-ID) into the existing NDN signature scheme for the mobile edge computing (MEC)-enabled NDN-IoT networks. The PHY-ID generation exploits the inherent per-signal-level device-specific radio-frequency imperfections of IoT devices, including the in-phase quadrature-phase imbalance, and thereby avoids adding any implementation complexity to the constrained IoT devices. We derive the offline maximum entropy-based quantization rule and propose an online two-step authentication scheme to improve the accuracy of the authentication decision-making. Consequently, a cooperative MEC device can securely execute the costly signing task on behalf of the authenticated IoT device in an optimal manner. The evaluation results demonstrate 1) elevated authentication time efficiency, 2) robustness to several impersonation attacks including the replay attack and the computation-based spoofing attack, and 3) increased differentiation rate and correct authentication probability through applying our integration design in MEC-enabled NDN-IoT networks.
MECDs can access the local device-specific PHY information faster than the core content router @cite_18 and equip more computational resources than IoT devices. It is anticipated that the MECDs can make accurate authentication decisions based on their knowledge of the local devices' PHY fingerprints and further leverage PHY fingerprinting to strengthen the system robustness for preventing the aforementioned computation-based impersonation attacks.
@cite_18
2776818420 2149510322 1981870545 1874399837
Physical (PHY)-layer authentication systems can exploit channel state information of radio transmitters to detect spoofing attacks in wireless networks. The use of multiple landmarks each with multiple antennas enhances the spatial resolution of radio transmitters, and thus improves the spoofing detection accuracy of PHY-layer authentication. Unlike most existing PHY-layer authentication schemes that apply hypothesis tests and rely on the known radio channel model, we propose a logistic regression-based authentication to remove the assumption on the known channel model, and thus be applicable to more generic wireless networks. The Frank–Wolfe algorithm is used to estimate the parameters of the logistic regression model, in which the convex problem under a @math -norm constraint is solved for weight sparsity to avoid over-fitting in the learning process. We design a distributed Frank–Wolfe-based PHY-layer authentication to further reduce the communication overhead between the landmarks and the security agent. Then, we construct an incremental aggregated gradient-based scheme to provide online authentication with a higher accuracy and lower computation overhead. Simulation and experimental results validate the accuracy of the proposed authentication schemes, and show the reduced communication and computation overheads. We propose a PHY-authentication protocol to detect spoofing attacks in wireless networks, exploiting the rapid-decorrelation property of radio channels with distance. In this protocol, a PHY-authentication scheme that exploits channel estimations that already exist in most wireless systems, cooperates with any existing---either simple or advanced---higher-layer process, such as IEEE 802.11i. With little additional system overhead, our scheme reduces the workload of the higher-layer process, or provides some degree of spoofing protection for naked" wireless systems, such as some sensor networks. We describe the performance of our approach as a function of the spoofing pattern and the snapshot performance that can be easily measured through field tests. We discuss the implementation issues of the authentication protocol on 802.11 testbeds and verify its performance via field tests in a typical office building. We propose a new fingerprinting technique that differentiates between unique devices over a Wireless Local Area Network (WLAN) simply through the timing analysis of 802.11 probe request frames. Our technique can be applied to spoof detection, network reconnaissance, and implementation of access control against masquerading attacks. Experimental results indicate that our technique is consistent and accurate in differentiating between unique devices. In contrast with existing wireless fingerprinting techniques, our technique is passive, non-invasive and does not require the co-operation of fingerprintee hosts. Node forgery or impersonation, in which legitimate cryptographic credentials are captured by an adversary, constitutes one major security threat facing wireless networks. The fact that mobile devices are prone to be compromised and reverse engineered significantly increases the risk of such attacks in which adversaries can obtain secret keys on trusted nodes and impersonate the legitimate node. One promising approach toward thwarting these attacks is through the extraction of unique fingerprints that can provide a reliable and robust means for device identification. These fingerprints can be extracted from transmitted signal by analyzing information across the protocol stack. In this paper, the first unified and comprehensive tutorial in the area of wireless device fingerprinting for security applications is presented. In particular, we aim to provide a detailed treatment on developing novel wireless security solutions using device fingerprinting techniques. The objectives are three-fold: (i) to introduce a comprehensive taxonomy of wireless features that can be used in fingerprinting, (ii) to provide a systematic review on fingerprint algorithms including both white-list based and unsupervised learning approaches, and (iii) to identify key open research problems in the area of device fingerprinting and feature extraction, as applied to wireless security.