We propose PiNcH, a methodology to detect the presence of a drone and its current status leveraging just the communication traffic exchanged between the drone and its Remote Controller (RC). PiNcH is built applying standard classification algorithms to the eavesdropped traffic, analyzing features such as packets inter-arrival time and size. PiNcH does not require either any special hardware or to transmit any signal. Indeed, it is fully passive and it resorts to cheap and general purpose hardware. To evaluate the effectiveness of our solution, we collected real communication measurements from the 3DR SOLO drone, being the most popular open-source hardware, running the widespread ArduCopter open-source firmware, mounted on-board on a wide range of commercial amateur drones. Then, we test our solution against different publicly available wireless traces. The results prove that PiNcH can efficiently and effectively: (i) identify the presence of the drone in several heterogeneous scenarios; (ii) identify the current state of a powered-on drone, i.e., flying or lying on the ground; (iii) discriminate the movement of the drone; and, finally, (iv) estimate a lower bound on the time required to identify a drone with the requested level of assurance. The quality and viability of our solution do prove that network traffic analysis can be successfully adopted for drone identification and pave the way for future research in the area.
Fingerprinting of wireless radio traffic at the network layer is emerging as a promising technique to uniquely identify devices in the wild. Authors in @cite_6 proved that the extraction of unique fingerprints provides a reliable and robust means for device identification.
@cite_6
1981870545 2148633529 1874399837 1985671681
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. The broadcast nature of the wireless medium poses a serious challenge to the security of wireless communications and networks. Traditional computational security strategies always fail to prevent the eavesdropper from overhearing the communications. Recently, the unique characteristics of wireless channel fingerprints, e.g. randomness and reciprocity, have been successfully used to enhance the security of the wireless network. We expand our previous work on exploring particular physical parameters of fingerprints, and focus on a new scheme in which we use the RAKE receiver to differentiate users under low transmission power. RAKE receiver may capture and estimate fingerprints through poor autocorrelation of the spreading coding. As the fingerprint is incomputable and unpredictable, and may only be detected by the pair of transmitter and receiver, it may be fully utilized to design an efficient authentication method. Simulation results strongly verify the efficiency of our proposed method, especially in the case of low SNR. 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. We demonstrate the feasibility of finger-printing the radio of wireless sensor nodes (Chipcon 1000 radio, 433MHz). We show that, with this type of devices, a receiver can create device radio finger-prints and subsequently identify origins of messages exchanged between the devices, even if message contents and device identifiers are hidden. We further analyze the implications of device fingerprinting on the security of sensor networking protocols, specifically, we propose two new mechanisms for the detection of wormholes in sensor networks.