Biblio
Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively "observing" signatures, ScatterID actively "manipulates" multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4% for identity verification.
This Innovate Practice Work in Progress paper is about education on Cybersecurity, which is essential in training of innovative talents in the era of the Internet. Besides knowledge and skills, it is important as well to enhance the students' awareness of cybersecurity in daily life. Considering that contactless smart cards are common and widely used in various areas, one basic and two advanced contactless smart card experiments were designed innovatively and assigned to junior students in 3-people groups in an introductory cybersecurity summer course. The experimental principles, facilities, contents and arrangement are introduced successively. Classroom tests were managed before and after the experiments, and a box and whisker plot is used to describe the distributions of the scores in both tests. The experimental output and student feedback implied the learning objectives were achieved through the problem-based, active and group learning experience during the experiments.
The Internet of Things (IoT) is a new paradigm in which every-day objects are interconnected between each other and to the Internet. This paradigm is receiving much attention of the scientific community and it is applied in many fields. In some applications, it is useful to prove that a number of objects are simultaneously present in a group. For example, an individual might want to authorize NFC payment with his mobile only if k of his devices are present to ensure that he is the right person. This principle is known as Grouping-Proofs. However, existing Grouping-Proofs schemes are mostly designed for RFID systems and don't fulfill the IoT characteristics. In this paper, we propose a Threshold Grouping-Proofs for IoT applications. Our scheme uses the Key-Policy Attribute-Based Encryption (KP-ABE) protocol to encrypt a message so that it can be decrypted only if at least k objects are simultaneously present in the same location. A security analysis and performance evaluation is conducted to show the effectiveness of our proposal solution.
Multi-tag identification technique has been applied widely in the RFID system to increase flexibility of the system. However, it also brings serious tags collision issues, which demands the efficient anti-collision schemes. In this paper, we propose a Multi-target tags assignment slots algorithm based on Hash function (MTSH) for efficient multi-tag identification. The proposed algorithm can estimate the number of tags and dynamically adjust the frame length. Specifically, according to the number of tags, the proposed algorithm is composed of two cases. when the number of tags is small, a hash function is constructed to map the tags into corresponding slots. When the number of tags is large, the tags are grouped and randomly mapped into slots. During the tag identification, tags will be paired with a certain matching rate and then some tags will exit to improve the efficiency of the system. The simulation results indicate that the proposed algorithm outperforms the traditional anti-collision algorithms in terms of the system throughput, stability and identification efficiency.