Using Twitter trust network for stock market analysis

Online social networks are now attracting a lot of attention not only from their users but also from researchers in various fields. Many researchers believe that the public mood or sentiment expressed in social media is related to financial markets. We propose to use trust among users as a filtering and amplifying mechanism for the social media to increase its correlation with financial data in the stock market. Therefore, we used the real stock market data as ground truth for our trust management system. We collected stock-related data (tweets) from Twitter, which is a very popular Micro-blogging forum, to see the correlation between the Twitter sentiment valence and abnormal stock returns for eight firms in the S&P 500. We developed a trust management framework to build a user-to-user trust network for Twitter users. Compared with existing works, in addition to analyzing and accumulating tweets’ sentiment, we take into account the source of tweets – their authors. Authors are differentiated by their power or reputation in the whole community, where power is determined by the user-to-user trust network. To validate our trust management system, we did the Pearson correlation test for an eight months period (the trading days from 01/01/2015 through 08/31/2015). Compared with treating all the authors equally important, or weighting them by their number of followers, our trust network based reputation mechanism can amplify the correlation between a specific firm’s Twitter sentiment valence and the firm’s stock abnormal returns. To further consider the possible auto-correlation property of abnormal stock returns, we constructed a linear regression model, which includes historical stock abnormal returns, to test the relation between the Twitter sentiment valence and abnormal stock returns. Again, our results showed that by using our trust network power based method to weight tweets, Twitter sentiment valence reflect abnormal stock returns better than treating all the authors equally important or weighting them by their number of followers

Toward A Mobile Agent Relay Network

Although wireless communication provides connectivity where hardwired links are difficult or impractical, it is still hindered by the environmental conditions where the communicators reside. Signal loss over large distances or because of intervening obstacles can be mitigated by increasing the user\u27s transmission power or adding repeater nodes between the users. Unfortunately, increasing the signal strength strains limited power resources and increases the likelihood of eavesdropping. Stationary repeaters are impractical for highly mobile users in dangerous environments. While mobile relay nodes might be a preferred solution, a centralized control scheme saps bandwidth from important traffic and introduces a single point of failure at the control station. An alternative solution is to create a Mobile Agent Relay Network (MARN). Each autonomous node in the MARN decides where to move to maintain the network connectivity using only locally-available information from onboard sensors and communication with in-range neighbor nodes. This is achieved by borrowing concepts from flocking behaviors that motivates our agents to maintain equal distance between its neighboring nodes. In addition, each agent maintains a filtered list of previously visited locations that provided best connection. This thesis takes the first steps toward realizing a MARN by providing mobile relay agents. Each model-based reflex agent is guided by a modified flocking behavior which considers only trustworthy neighbors and uses a Bayesian model to aggregate observations and shared reputation. The relay agents are able to build a network and maintain connectivity for their users. In this work, MARN agent algorithms are evaluated in a simulated unobstructed environment with stationary users. The system behavior is explored under both benign conditions and with varying numbers of misbehaving nodes

Reputation Propagation and Updating in Mobile Ad Hoc Networks with Byzantine Failures

International audienceIn a mobile ad hoc network we consider the problem of designing a reputation system that allows to update and to propagate the computed reputation scores while tolerating Byzantine failures. Each time a correct node uses directly a service, it can determine by itself the quality of service currently provided. This fresh and valid rating information is broadcast immediately to all its current neighbors. Then, while the mobile node moves, it can receive from other nodes other recommendations also related to the same service. Thus it updates continuously its own opinion. Meanwhile it continues to broadcast this updated information. The freshness and the validity of the received/sent information become questionable. We propose a protocol that allows a node to ignore a second hand information when this information is not fresh or not valid. In particular, fake values provided by Byzantine nodes are eliminated when they are not consistent with those gathered from correct nodes. When the quality of service stabilizes, the correct nodes are supposed to provide quite similar recommendations. In this case, we demonstrate that the proposed protocol ensures convergence to a range of possible reputation scores if a necessary condition is satisfied by the mobile nodes. Simulations are conducted in random mobility scenarios. The results show that our algorithm has a better performance than typical methods proposed in previous works

A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources

A Trust-based Message Evaluation and Propagation Framework in Vehicular Ad-Hoc Networks

In this paper, we propose a trust-based message propagation and evaluation framework to support the effective evaluation of information sent by peers and the immediate control of false information in a VANET. More specifically, our trust-based message propagation collects peers’ trust opinions about a message sent by a peer (message sender) during the propagation of the message. We improve on an existing cluster-based data routing mechanism by employing a secure and efficient identity-based aggregation scheme for the aggregation and propagation of the sender’s message and the trust opinions. These trust opinions weighted by the trustworthiness of the peers modeled using a combination of role-based and experience-based trust metrics are used by cluster leaders to compute a ma jority opinion about the sender’s message, in order to proactively detect false information. Malicious messages are dropped and controlled to a local minimum without further affecting other peers. Our trust-based message evaluation allows each peer to evaluate the trustworthiness of the message by also taking into account other peers’ trust opinions about the message and the peer-to-peer trust of these peers. The result of the evaluation derives an effective action decision for the peer. We evaluate our framework in simulations of real life traffic scenarios by employing real maps with vehicle entities following traffic rules and road limits. Some entities involved in the simulations are possibly malicious and may send false information to mislead others or spread spam messages to jam the network. Experimental results demonstrate that our framework significantly improves network scalability by reducing the utilization of wireless bandwidth caused by a large number of malicious messages. Our system is also demonstrated to be effective in mitigating against malicious messages and protecting peers from being affected. Thus, our framework is particularly valuable in the deployment of VANETs by achieving a high level of scalability and effectiveness

SECURITY, PRIVACY AND APPLICATIONS IN VEHICULAR AD HOC NETWORKS

With wireless vehicular communications, Vehicular Ad Hoc Networks (VANETs) enable numerous applications to enhance traffic safety, traffic efficiency, and driving experience. However, VANETs also impose severe security and privacy challenges which need to be thoroughly investigated. In this dissertation, we enhance the security, privacy, and applications of VANETs, by 1) designing application-driven security and privacy solutions for VANETs, and 2) designing appealing VANET applications with proper security and privacy assurance. First, the security and privacy challenges of VANETs with most application significance are identified and thoroughly investigated. With both theoretical novelty and realistic considerations, these security and privacy schemes are especially appealing to VANETs. Specifically, multi-hop communications in VANETs suffer from packet dropping, packet tampering, and communication failures which have not been satisfyingly tackled in literature. Thus, a lightweight reliable and faithful data packet relaying framework (LEAPER) is proposed to ensure reliable and trustworthy multi-hop communications by enhancing the cooperation of neighboring nodes. Message verification, including both content and signature verification, generally is computation-extensive and incurs severe scalability issues to each node. The resource-aware message verification (RAMV) scheme is proposed to ensure resource-aware, secure, and application-friendly message verification in VANETs. On the other hand, to make VANETs acceptable to the privacy-sensitive users, the identity and location privacy of each node should be properly protected. To this end, a joint privacy and reputation assurance (JPRA) scheme is proposed to synergistically support privacy protection and reputation management by reconciling their inherent conflicting requirements. Besides, the privacy implications of short-time certificates are thoroughly investigated in a short-time certificates-based privacy protection (STCP2) scheme, to make privacy protection in VANETs feasible with short-time certificates. Secondly, three novel solutions, namely VANET-based ambient ad dissemination (VAAD), general-purpose automatic survey (GPAS), and VehicleView, are proposed to support the appealing value-added applications based on VANETs. These solutions all follow practical application models, and an incentive-centered architecture is proposed for each solution to balance the conflicting requirements of the involved entities. Besides, the critical security and privacy challenges of these applications are investigated and addressed with novel solutions. Thus, with proper security and privacy assurance, these solutions show great application significance and economic potentials to VANETs. Thus, by enhancing the security, privacy, and applications of VANETs, this dissertation fills the gap between the existing theoretic research and the realistic implementation of VANETs, facilitating the realistic deployment of VANETs

Defense and traceback mechanisms in opportunistic wireless networks

 In this thesis, we have identified a novel attack in OppNets, a special type of packet dropping attack where the malicious node(s) drops one or more packets (not all the packets) and then injects new fake packets instead. We name this novel attack as the Catabolism attack and propose a novel attack detection and traceback approach against this attack referred to as the Anabolism defence. As part of the Anabolism defence approach we have proposed three techniques: time-based, Merkle tree based and Hash chain based techniques for attack detection and malicious node(s) traceback. We provide mathematical models that show our novel detection and traceback mechanisms to be very effective and detailed simulation results show our defence mechanisms to achieve a very high accuracy and detection rate