Yan Lindsay Sun

Defending online reputation systems against collaborative unfair raters through signal modeling and trust

Online feedback-based rating systems are gaining popularity. Dealing with collaborative unfair ratings in such systems has been recognized as an important but difficult problem. This problem is challenging especially when the number of honest ratings is relatively small and unfair ratings can contribute to a significant portion of the overall ratings. In addition, the lack of unfair rating data from real human users is another obstacle toward realistic evaluation of defense mechanisms. In this paper, we propose a set of methods that jointly detect smart and collaborative unfair ratings based on signal modeling. Based on the detection, a framework of trust-assisted rating aggregation system is developed. Furthermore, we design and launch a Rating Challenge to collect unfair rating data from real human users. The proposed system is evaluated through simulations as well as experiments using real attack data. Compared with existing schemes, the proposed system can significantly reduce the impact from collaborative unfair ratings.

Securing collaborative spectrum sensing against untrustworthy secondary users in cognitive radio networks

Cognitive radio is a revolutionary paradigm to migrate the spectrum scarcity problem in wireless networks. In cognitive radio networks, collaborative spectrum sensing is considered as an effective method to improve the performance of primary user detection. For current collaborative spectrum sensing schemes, secondary users are usually assumed to report their sensing information honestly. However, compromised nodes can send false sensing information to mislead the system. In this paper, we study the detection of untrustworthy secondary users in cognitive radio networks. We first analyze the case when there is only one compromised node in collaborative spectrum sensing schemes. Then we investigate the scenario that there are multiple compromised nodes. Defense schemes are proposed to detect malicious nodes according to their reporting histories. We calculate the suspicious level of all nodes based on their reports. The reports from nodes with high suspicious levels will be excluded in decision-making. Compared with existing defense methods, the proposed scheme can effectively differentiate malicious nodes and honest nodes. As a result, it can significantly improve the performance of collaborative sensing. For example, when there are 10 secondary users, with the primary user detection rate being equal to 0.99, one malicious user can make the false alarm rate increase to 72%. The proposed scheme can reduce it to 5%. Two malicious users can make increase to 85% and the proposed scheme reduces it to 8%.

Trust Establishment in Distributed Networks: Analysis and Modeling

Recently, trust establishment is recognized as an important approach to defend distributed networks, such as mobile ad hoc networks and sensor networks, against malicious attacks. Trust establishment mechanisms can stimulate collaboration among distributed computing and communication entities, facilitate the detection of untrustworthy entities, and assist decision-making in various protocols. In the current literature, the methods proposed for trust establishment are always evaluated through simulation, but theoretical analysis is extremely rare. In this paper, we present a suite of approaches to analyze trust establishment process. These analysis approaches are used to provide in-depth understanding of trust establishment process and quantitative comparison among trust establishment methods. The proposed analysis methods are validated through simulations.

RepTrap: a novel attack on feedback-based reputation systems

Reputation systems are playing critical roles in securing todays distributed computing and communication systems. Similar to other security mechanisms, reputation systems can be under attack. In this paper, we report the discovery of a new attack, named RepTrap(Reputation Trap), against feedback-based reputation systems, such as those used in P2P file-sharing systems and E-commerce websites(e.g. Amazon.com). We conduct an in-depth investigation on this new attack, including analysis, case study, and performance evaluation based on real data and realistic user behavior models. We discover that the RepTrap is a strong and destructive attack that can manipulate the reputation scores of users, objects, and even undermine the entire reputation system. Compared with other known attacks that achieve the similar goals, the RepTrap requires less effort from the attackers and causes multi-dimensional damage to the reputation systems.

Anomaly Detection in Feedback-based Reputation Systems through Temporal and Correlation Analysis

As the value of reputation systems is widely recognized, the incentive to manipulate such systems is rapidly growing. We propose TAUCA, a scheme that identifies malicious users and recovers reputation scores from a novel angle: combination of temporal analysis and user correlation analysis. Benefiting from the rich information in the time-domain, TAUCA identifies the products under attack, the time when attacks occur, and malicious users who insert dishonest ratings. TAUCA and two other representative schemes are tested against real user attack data collected through a cyber competition. TAUCA demonstrates significant advantages. It largely improves the detection rate and reduces the false alarm rate in the detection of malicious users. It also effectively reduces the bias in the recovered reputation scores.

Resilience Analysis of Power Grids Under the Sequential Attack

The modern society increasingly relies on electrical service, which also brings risks of catastrophic consequences, e.g., large-scale blackouts. In the current literature, researchers reveal the vulnerability of power grids under the assumption that substations/transmission lines are removed or attacked synchronously. In reality, however, it is highly possible that such removals can be conducted sequentially. Motivated by this idea, we discover a new attack scenario, called the sequential attack, which assumes that substations/transmission lines can be removed sequentially, not synchronously. In particular, we find that the sequential attack can discover many combinations of substation whose failures can cause large blackout size. Previously, these combinations are ignored by the synchronous attack. In addition, we propose a new metric, called the sequential attack graph (SAG), and a practical attack strategy based on SAG. In simulations, we adopt three test benchmarks and five comparison schemes. Referring to simulation results and complexity analysis, we find that the proposed scheme has strong performance and low complexity.

Reliability-Oriented Single-Path Routing Protocols in Wireless Sensor Networks

Wireless sensor networks (WSNs) bring significant advantages over traditional communications in todays applications, such as environmental monitoring, homeland security, and health care. However, harsh and complex environments pose great challenges in the reliability of WSN communications. To achieve reliable wireless communications within WSNs, it is essential to have a reliable routing protocol and to have a means to evaluate the reliability performance of different routing protocols. In this paper, we first model the reliability of two different types of sensor nodes: 1) energy harvesting sensor nodes and 2) battery-powered sensor nodes. We then present wireless link reliability models for each type of sensor nodes, where effects of different parameters, such as battery life-time, shadowing, noise, and location uncertainty, are considered for analyzing the wireless link reliability. Based on the sensor node and wireless link reliability models, we compare the performance of different routing algorithms in terms of end-to-end path reliability and number of hops. A dynamic routing approach is then proposed to achieve the most reliable end-to-end path in WSNs. Furthermore, to facilitate a fair and comprehensive comparison among different routing algorithms, a cost function approach that integrates the end-to-end path reliability and number of hops is proposed, providing an indicator of quality of service of applications running on WSNs.

Integrating neuromuscular and cyber systems for neural control of artificial legs

This paper presents a design and implementation of a cyber-physical system (CPS) for neurally controlled artificial legs. The key to the new CPS system is the neural-machine interface (NMI) that uses an embedded computer to collect and interpret electromyographic (EMG) signals from a physical system that is a leg amputee. A new deciphering algorithm, composed of an EMG pattern classifier and finite state machine (FSM), was developed to identify the users intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Our preliminary experiment on a human subject demonstrated the feasibility of our designed real-time neural-machine interface for artificial legs.

Detection of collusion behaviors in online reputation systems

Online reputation systems are gaining popularity. Dealing with collaborative unfair ratings in such systems has been recognized as an important but difficult problem. The current defense mechanisms focus on analyzing rating values for individual products. In this paper, we propose a scheme that detects collaborative unfair raters based on similarity in their rating behaviors. The proposed scheme integrates abnormal detection in both rating-value domain and the user-domain. To evaluate the proposed scheme in realistic scenarios, we design and launch a cyber competition, in which attack data from real human users are collected. The proposed system is evaluated through experiments using real attack data. The proposed scheme can accurately detect collusion behaviors and therefore significantly reduce the damage caused by collaborative dishonest users.

Reliability and lifetime modeling of wireless sensor nodes

Abstract The accuracy of system reliability analysis depends not only on system-level model construction, but also on realistic estimation of failure parameters at the component-level. In this paper, we model and evaluate the reliability and lifetime of a wireless sensor node under three typical working scenarios, contributing toward the accurate reliability analysis of wireless sensor network systems. According to the medium access control (MAC) protocols, the three working scenarios are defined based on the sensor node modes (sleep and active) and the mechanism of alternating between the modes. Reliability and lifetime of wireless sensor nodes under these three scenarios are illustrated and compared through numerical examples.