Suguo Du

A Probabilistic Misbehavior Detection Scheme toward Efficient Trust Establishment in Delay-Tolerant Networks

Malicious and selfish behaviors represent a serious threat against routing in delay/disruption tolerant networks (DTNs). Due to the unique network characteristics, designing a misbehavior detection scheme in DTN is regarded as a great challenge. In this paper, we propose iTrust, a probabilistic misbehavior detection scheme, for secure DTN routing toward efficient trust establishment. The basic idea of iTrust is introducing a periodically available Trusted Authority (TA) to judge the nodes behavior based on the collected routing evidences and probabilistically checking. We model iTrust as the inspection game and use game theoretical analysis to demonstrate that, by setting an appropriate investigation probability, TA could ensure the security of DTN routing at a reduced cost. To further improve the efficiency of the proposed scheme, we correlate detection probability with a nodes reputation, which allows a dynamic detection probability determined by the trust of the users. The extensive analysis and simulation results demonstrate the effectiveness and efficiency of the proposed scheme.

Security and privacy of collaborative spectrum sensing in cognitive radio networks

Collaborative spectrum sensing is regarded as a promising approach to significantly improve the performance of spectrum sensing in cognitive radio networks. However, due to the open nature of wireless communications and the increasingly available software defined radio platforms, collaborative spectrum sensing also poses many new research challenges, especially in the aspect of security and privacy. In this article, we first identify the potential security threats toward collaborative spectrum sensing in CRNs. Then we review the existing proposals related to secure collaborative spectrum sensing. Furthermore, we identify several new location privacy related attacks in collaborative sensing, which are expected to compromise secondary users¿ location privacy by correlating their sensing reports and their physical location. To thwart these attacks, we propose a novel privacy preserving framework in collaborative spectrum sensing to prevent location privacy leaking. We design and implement a real-world testbed to evaluate the system performance. The attack experiment results show that if there is no any security guarantee, the attackers could successfully compromise a secondary user¿s location privacy at a success rate of more than 90 percent. We also show that the proposed privacy preserving framework could significantly improve the location privacy of secondary users with a minimal effect on the performance of collaborative sensing.

UAV-assisted data gathering in wireless sensor networks

An unmanned aerial vehicle (UAV) is a promising carriage for data gathering in wireless sensor networks since it has sufficient as well as efficient resources both in terms of time and energy due to its direct communication between the UAV and sensor nodes. On the other hand, to realize the data gathering system with UAV in wireless sensor networks, there are still some challenging issues remain such that the highly affected problem by the speed of UAVs and network density, also the heavy conflicts if a lot of sensor nodes concurrently send its own data to the UAV. To solve those problems, we propose a new data gathering algorithm, leveraging both the UAV and mobile agents (MAs) to autonomously collect and process data in wireless sensor networks. Specifically, the UAV dispatches MAs to the network and every MA is responsible for collecting and processing the data from sensor nodes in an area of the network by traveling around that area. The UAV gets desired information via MAs with aggregated sensory data. In this paper, we design a itinerary of MA migration with considering the network density. Simulation results demonstrate that our proposed method is time- and energy-efficient for any density of the network.

Fairness-Aware and Privacy-Preserving Friend Matching Protocol in Mobile Social Networks

Mobile social networks represent a promising cyber-physical system, which connects mobile nodes within a local physical proximity using mobile smart phones as well as wireless communication. In mobile social networks, the mobile users may, however, face the risk of leaking their personal information and location privacy. In this paper, we first model the secure friend discovery process as a generalized privacy-preserving interest and profile matching problem. We identify a new security threat arising from existing secure friend discovery protocols, coined as runaway attack, which can introduce a serious unfairness issue. To thwart this new threat, we introduce a novel blind vector transformation technique, which could hide the correlation between the original vector and transformed results. Based on this, we propose our privacy-preserving and fairness-aware interest and profile matching protocol, which allows one party to match its interest with the profile of another, without revealing its real interest and profile and vice versa. The detailed security analysis as well as real-world implementations demonstrate the effectiveness and efficiency of the proposed protocol.

RENDEZVOUS: towards fast event detecting in wireless sensor and actor networks

Fast event detection is important in wireless sensor and actor networks (WSANs) since actors can perform appropriate actions which sensor nodes are not capable to do. While WSANs inherits the typical constrains of WSNs such as energy and computation limitations of sensor nodes. In this paper, we propose a fast event detecting algorithm named RENDEZVOUS to accelerate the actor’s event detecting process while keep the energy consumption of sensor nodes as minimum. When design RENDEZVOUS, we first study the mobility control of a actor to help the actor move around close to a event by using Reinforcement Learning techniques with collected sensory data. We then design a scheme to search nearby actors from the event side inspired by a searching behavior of desert ants. By both perform search actions from sensor side and actor side, the proposed algorithm can achieve fast event detecting with neglect-able additional energy cost on sensors side. Extensive simulation results demonstrate the efficiency of RENDEZVOUS.

Privacy Leakage of Location Sharing in Mobile Social Networks: Attacks and Defense

Along with the popularity of mobile social networks (MSNs) is the increasing danger of privacy breaches due to user location exposures. In this work, we take an initial step towards quantifying location privacy leakage from MSNs by matching the users’ shared locations with their real mobility traces. We conduct a three-week real-world experiment with 30 participants and discover that both direct location sharing (e.g., Weibo or Renren) and indirect location sharing (e.g., Wechat or Skout) can reveal a small percentage of users’ real points of interests (POIs). We further propose a novel attack to allow an external adversary to infer the demographics (e.g., age, gender, education) after observing users’ exposed location profiles. We implement such an attack in a large real-world dataset involving 22,843 mobile users. The experimental results show that the attacker can effectively predict demographic attributes about users with some shared locations. To resist such attacks, we propose SmartMask, a context-based system-level privacy protection solution, designed to automatically learn users’ privacy preferences under different contexts and provide a transparent privacy control for MSN users. The effectiveness and efficiency of SmartMask have been well validated by extensive experiments.

A Novel Attack Tree Based Risk Assessment Approach for Location Privacy Preservation in the VANETs

Even though emerging as a promising approach to increase road safety, efficiency and convenience, Vehicular Ad hoc Networks (VANETs) pose many new research challenges, especially on the aspect of location privacy. The existing literatures focus on preventive techniques to achieve location privacy protection, however the location privacy risk assessment receives less attention. In this paper, we introduce a novel risk assessment method to evaluate the security risk of VANETs privacy based on attack tree. The proposed scheme provides a general analysis framework to estimate the degree that a certain threat might bring to the VANETs. We also use the constructed attack tree to identify possible attack scenarios that an attacker may launch towards the privacy preserving system in VANETs, which is expected to further improve the system security.

MixZone in Motion: Achieving Dynamically Cooperative Location Privacy Protection in Delay-Tolerant Networks

Delay-tolerant networks (DTNs) are typically sparse ad hoc networks where node density is low and contacts between nodes in the network do not occur very frequently. The existing location privacy protection methods, which require mobile nodes to collectively change their pseudonyms in special regions called mix zones, may not work well in DTNs due to their unique characteristics, including low network density and limited contact duration. In this paper, we propose a novel cooperative location privacy protection scheme, which is called AVATAR, for sparse DTNs. The main idea of AVATAR is to generate a certain number of virtual nodes in the proximity of a node and allow both virtual and real nodes to make a coordinated pseudonym change in an enlarged region, which are named virtual mix zones. Each AVATAR participant benefits from increased location privacy protection at the cost of generating a series of signed position messages, which are named footprint signatures. To stimulate each node to contribute more footprint signatures to the virtual mix zones, AVATAR proposes a reward mechanism, which is modeled as a multiunit discriminatory auction game. Extensive simulations and analysis have been provided to demonstrate the effectiveness and efficiency of the proposed scheme.

SAS: A Secure Data Aggregation Scheme in Vehicular Sensing Networks

Vehicular ad hoc networks support a wide range of promising applications including vehicular sensing networks, which enable vehicles to cooperatively collect and transmit the aggregated traffic data for the purpose of traffic monitoring. The reported literatures mainly focus on how to achieve the data aggregation in dynamic vehicular environment while the security issue especially on the authenticity and integrity of aggregation results receive less attention. In this study, we introduce a secure probabilistic data aggregation scheme based on Flajolet-Martin sketch and \emph{sketch proof} technique. We also discuss the tradeoff between the bandwidth efficiency and the estimation accuracy. Extensive simulations and analysis demonstrate the efficiency and effectiveness of the proposed scheme.

An attack-and-defence game for security assessment in vehicular ad hoc networks

Recently, there is an increasing interest in Security and Privacy issues in Vehicular ad hoc networks (or VANETs). However, the existing security solutions mainly focus on the preventive solutions while lack a comprehensive security analysis. The existing risk analysis solutions may not work well to evaluate the security threats in vehicular networks since they fail to consider the attack and defense costs and gains, and thus cannot appropriately model the mutual interaction between the attacker and defender. In this study, we consider both of the rational attacker and defender who decide whether to launch an attack or adopt a countermeasure based on its adversary’s strategy to maximize its own attack and defense benefits. To achieve this goal, we firstly adopt the attack-defense tree to model the attacker’s potential attack strategies and the defender’s corresponding countermeasures. To take the attack and defense costs into consideration, we introduce Return On Attack and Return on Investment to represent the potential gain from launching an attack or adopting a countermeasure in vehicular networks. We further investigate the potential strategies of the defender and the attacker by modeling it as an attack-defense game. We then give a detailed analysis on its Nash Equilibrium. The rationality of the proposed game-theoretical model is well illustrated and demonstrated by extensive analysis in a detailed case study.