Biblio
In cyberspace, a digital signature is a mathematical technique that plays a significant role, especially in validating the authenticity of digital messages, emails, or documents. Furthermore, the digital signature mechanism allows the recipient to trust the authenticity of the received message that is coming from the said sender and that the message was not altered in transit. Moreover, a digital signature provides a solution to the problems of tampering and impersonation in digital communications. In a real-life example, it is equivalent to a handwritten signature or stamp seal, but it offers more security. This paper proposes a scheme to enable users to digitally sign their communications by validating their identity through users’ mobile devices. This is done by utilizing the user’s ambient Wi-Fi-enabled devices. Moreover, the proposed scheme depends on something that a user possesses (i.e., Wi-Fi-enabled devices), and something that is in the user’s environment (i.e., ambient Wi-Fi access points) where the validation process is implemented, in a way that requires no effort from users and removes the "weak link" from the validation process. The proposed scheme was experimentally examined.
Military communities have come to rely heavily on commercial off the shelf (COTS) standards and technologies for Internet of Things (IoT) operations. One of the major obstacles to military use of COTS IoT devices is the security of data transfer. In this paper, we successfully design and develop a lightweight, trust-based security architecture to support routing in a mobile IoT network. Specifically, we modify the RPL IoT routing algorithm using common security techniques, including a nonce identity value, timestamp, and network whitelist. Our approach allows RPL to select a routing path over a mobile IoT wireless network based on a computed node trust value and average received signal strength indicator (ARSSI) value across network members. We conducted simulations using the Cooja network simulator and Wireshark to validate the algorithm against stipulated threat models. We demonstrate that our algorithm can protect the network against Denial of Service (DoS) and Sybil based identity attacks. We also show that the control overhead required for our algorithm is less than 5% and that the packet delivery rate improves by nearly 10%.
Recent advances in Cross-Technology Communication (CTC) enable the coexistence and collaboration among heterogeneous wireless devices operating in the same ISM band (e.g., Wi-Fi, ZigBee, and Bluetooth in 2.4 GHz). However, state-of-the-art CTC schemes are vulnerable to spoofing attacks since there is no practice authentication mechanism yet. This paper proposes a scheme to enable the spoofing attack detection for CTC in heterogeneous wireless networks by using physical layer information. First, we propose a model to detect ZigBee packets and measure the corresponding Received Signal Strength (RSS) on Wi-Fi devices. Then, we design a collaborative mechanism between Wi-Fi and ZigBee devices to detect the spoofing attack. Finally, we implement and evaluate our methods through experiments on commercial off-the- shelf (COTS) Wi-Fi and ZigBee devices. Our results show that it is possible to measure the RSS of ZigBee packets on Wi-Fi device and detect spoofing attack with both a high detection rate and a low false positive rate in heterogeneous wireless networks.
Vehicular Ad Hoc Networks (VANETs) enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that bring many benefits and conveniences to improve the road safety and drive comfort in future transportation systems. Sybil attack is considered one of the most risky threats in VANETs since a Sybil attacker can generate multiple fake identities with false messages to severely impair the normal functions of safety-related applications. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. To avoid the inaccurate position estimation according to predefined radio propagation models in previous RSSI-based detection methods, Voiceprint adopts the RSSI time series as the vehicular speech and compares the similarity among all received time series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without the support of the centralized infrastructure. It has more accurate detection rate in different dynamic environments. Extensive simulations and real-world experiments demonstrate that the proposed Voiceprint is an effective method considering the cost, complexity and performance.
Vehicular ad hoc networks (VANETs) are designed to provide traffic safety by exploiting the inter-vehicular communications. Vehicles build awareness of traffic in their surroundings using information broadcast by other vehicles, such as speed, location and heading, to proactively avoid collisions. The effectiveness of these VANET traffic safety applications is particularly dependent on the accuracy of the location information advertised by each vehicle. Therefore, traffic safety can be compromised when Sybil attackers maliciously advertise false locations or other inaccurate GPS readings are sent. The most effective way to detect a Sybil attack or correct the noise in the GPS readings is localizing vehicles based on the physical features of their transmission signals. The current localization techniques either are designed for networks where the nodes are immobile or suffer from inaccuracy in high-interference environments. In this paper, we present a RSSI-based localization technique that uses mobile nodes for localizing another mobile node and adjusts itself based on the heterogeneous interference levels in the environment. We show via simulation that our localization mechanism is more accurate than the other mechanisms and more resistant to environments with high interference and mobility.
Primary user emulation (PUE) attack is one of the main threats affecting cognitive radio (CR) networks. The PUE can forge the same signal as the real primary user (PU) in order to use the licensed channel and cause deny of service (DoS). Therefore, it is important to locate the position of the PUE in order to stop and avoid any further attack. Several techniques have been proposed for localization, including the received signal strength indication RSSI, Triangulation, and Physical Network Layer Coding. However, the area surrounding the real PU is always affected by uncertainty. This uncertainty can be described as a lost (cost) function and conditional probability to be taken into consideration while proclaiming if a PU/PUE is the real PU or not. In this paper, we proposed a combination of a Bayesian model and trilateration technique. In the first part a trilateration technique is used to have a good approximation of the PUE position making use of the RSSI between the anchor nodes and the PU/PUE. In the second part, a Bayesian decision theory is used to claim the legitimacy of the PU based on the lost function and the conditional probability to help to determine the existence of the PUE attacker in the uncertainty area.
This paper proposes a novel wireless MAC-layer approach towards achieving channel access anonymity. Nodes autonomously select periodic TDMA-like time-slots for channel access by employing a novel channel sensing strategy, and they do so without explicitly sharing any identity information with other nodes in the network. An add-on hardware module for the proposed channel sensing has been developed and the proposed protocol has been implemented in Tinyos-2.x. Extensive evaluation has been done on a test-bed consisting of Mica2 hardware, where we have studied the protocol's functionality and convergence characteristics. The functionality results collected at a sniffer node using RSSI traces validate the syntax and semantics of the protocol. Experimentally evaluated convergence characteristics from the Tinyos test-bed were also found to be satisfactory.