Vehicular ad hoc networks (VANET) is a class of ad hoc networks build to ensure the safety of traffic. This is important because accidents claim many lives. Trust and security remain a major concern in VANET since a simple mistake can have catastrophic consequence. A crucial point in VANET is how to trust the information transmitted when the neighboring vehicles are rapidly changing and moving in and out of range. Current reputation systems for VANET try to establish trust between entities, which might not be required for practical scenarios. Due to the ephemeral nature of VANET, reputation schemes for mobile ad hoc networks (MANETs) cannot be applied to VANET. In this thesis, we point out several limitations of reputation trust management schemes for VANET. In particular, we identify the problem of information cascading and oversampling, which commonly arise in social networks. Oversampling is a situation in which a node observing two or more nodes, takes into consideration both their opinions equally without knowing that they might have influenced each other in decision making. We show that simple voting for decision making, leads to oversampling. We propose a solution to overcome this problem in VANET. We also suggest new ways to merge reputation schemes with misbehavior detection schemes to establish a trustworthy VANET.

Vehicular ad hoc networks (VANET) is a class of ad hoc networks build to ensure the safety of traffic. This is important because accidents claim many lives. Trust and security remain a major concern in VANET since a simple mistake can have catastrophic consequence. A crucial point in VANET is how to trust the information transmitted when the neighboring vehicles are rapidly changing and moving in and out of range. Current reputation systems for VANET try to establish trust between entities, which might not be required for practical scenarios. Due to the ephemeral nature of VANET, reputation schemes for mobile ad hoc networks (MANETs) cannot be applied to VANET. In this thesis, we point out several limitations of reputation trust management schemes for VANET. In particular, we identify the problem of information cascading and oversampling, which commonly arise in social networks. Oversampling is a situation in which a node observing two or more nodes, takes into consideration both their opinions equally without knowing that they might have influenced each other in decision making. We show that simple voting for decision making, leads to oversampling. We propose a solution to overcome this problem in VANET. We also suggest new ways to merge reputation schemes with misbehavior detection schemes to establish a trustworthy VANET.

Vehicular ad hoc networks (VANET) is a class of ad hoc networks build to ensure the safety of traffic. This is important because accidents claim many lives. Trust and security remain a major concern in VANET since a simple mistake can have catastrophic consequence. A crucial point in VANET is how to trust the information transmitted when the neighboring vehicles are rapidly changing and moving in and out of range. Current reputation systems for VANET try to establish trust between entities, which might not be required for practical scenarios. Due to the ephemeral nature of VANET, reputation schemes for mobile ad hoc networks (MANETs) cannot be applied to VANET. In this thesis, we point out several limitations of reputation trust management schemes for VANET. In particular, we identify the problem of information cascading and oversampling, which commonly arise in social networks. Oversampling is a situation in which a node observing two or more nodes, takes into consideration both their opinions equally without knowing that they might have influenced each other in decision making. We show that simple voting for decision making, leads to oversampling. We propose a solution to overcome this problem in VANET. We also suggest new ways to merge reputation schemes with misbehavior detection schemes to establish a trustworthy VANET.

Vehicular ad hoc networks (VANET) is a class of ad hoc networks build to ensure the safety of traffic. This is important because accidents claim many lives. Trust and security remain a major concern in VANET since a simple mistake can have catastrophic consequence. A crucial point in VANET is how to trust the information transmitted when the neighboring vehicles are rapidly changing and moving in and out of range. Current reputation systems for VANET try to establish trust between entities, which might not be required for practical scenarios. Due to the ephemeral nature of VANET, reputation schemes for mobile ad hoc networks (MANETs) cannot be applied to VANET. In this thesis, we point out several limitations of reputation trust management schemes for VANET. In particular, we identify the problem of information cascading and oversampling, which commonly arise in social networks. Oversampling is a situation in which a node observing two or more nodes, takes into consideration both their opinions equally without knowing that they might have influenced each other in decision making. We show that simple voting for decision making, leads to oversampling. We propose a solution to overcome this problem in VANET. We also suggest new ways to merge reputation schemes with misbehavior detection schemes to establish a trustworthy VANET.

Research has found that the prevalence of community violence exposure is relatively high among suburban and urban middle school-aged children. Exposure through witnessing and victimization has been related to antisocial behavior. Active coping has been related to fewer emotional and behavioral problems, whereas avoidant coping is related to conduct disorder. This study examines effects of community violence exposure on antisocial behavior problems (in terms of school infractions) and coping as a moderator of these negative effects of violence exposure. It was hypothesized that 1) there would be a main effect for community violence exposure such that adolescents with high levels of violence exposure (either as a victim or witness) would exhibit greater school misbehavior problems than those with low levels of exposure; 2) there would be main effects for coping such that children with high (vs. low) levels of active coping would exhibit fewer school misbehavior problems, and those with high (vs. low) levels of avoidant coping would exhibit greater school misbehavior problems.; 3) there would be an interaction effect such that children with high levels of violence exposure and high (vs. low) levels of avoidant coping and low (vs. high) levels of active coping would exhibit the greatest level of school misbehavior problems relative to all other groups. Seventy-eight ninth grade male and female students from a predominantly rural setting were recruited and grouped according to high and low self-reports of community violence exposure and active and avoidant coping strategies. School misbehavior was measured through school discipline records. Results indicated high prevalence rates of community violence exposure among school children in a rural setting, replicating previous findings and acknowledging violence exposure beyond at-risk and urban populations. Fifty-five percent reported being a victim of community violence and 86% reported witnessing community violence at least once in their lifetime. A significant interaction indicated that individuals with low levels of both active and avoidant coping exhibited more school infractions as well as more severe and aggressive types of infractions. In addition, for severity and aggressiveness, this interaction effect of coping on school misbehavior was strongest only in individuals with higher levels of community violence exposure. The results of this study provide support for the position that community violence exposure exists among school-aged children in urban and rural communities and this exposure negatively impacts the behavior of children when it is combined with poor coping. Coping appears to play a role in the difference observed among children who experience community violence in relation to school misbehavior. / Master of Science

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Covert channels are becoming more popular as security risks grow in networks. One area that is promising for covert channels is wireless networks, since many use a collision avoidance scheme such as carrier sense multiple access with collision avoidance (CSMA/CA). These schemes often introduce randomness in the network, which provides good cover for a covert timing channel. In this thesis, we use the 802.11 standard as an example to demonstrate a wireless covert channel. In particular, most 802.11 configurations use a distributed coordinated function (DCF) to assist in communications. This DCF uses a random backoff to avoid collisions, which provides the cover for our covert channel. Our timing channel provides great improvements on other recent covert channels in the field of throughput, while maintaining high accuracy. We are able to achieve throughput over 8000 bps using Covert DCF, or by accepting a throughput of 1800 bps we can achieve higher covertness and 99% accuracy as well.

The IEEE 802.15.4 has attracted time-critical applications in wireless sensor networks (WSNs) because of its beacon-enabled mode and guaranteed timeslots (GTSs). However, the GTS scheme’s security still leave the 802.15.4 MAC vulnerable to attacks. Further, the existing techniques in the literature for securing 802.15.4 either focus on non beacon-enabled 802.15.4 or cannot defend against insider attacks for beacon-enabled 802.15.4. In this thesis, we illustrate this by demonstrating attacks on the availability and integrity of the beacon-enabled 802.15.4. To proof the attacks, we implement the attacks using Tmote Sky motes for a malicious node along with regular nodes. We show that the malicious node can freely exploit the beacon frames to compromise the integrity and availability of the network. For the defense, we present beacon-enabled MiniSec (BCN-MiniSec) and analyze its cost.

Cooperative systems exploit spatial diversity to improve communication quality. But system performances could be severely degraded in the existance malicious relay nodes. In this thesis, we consider a two-relay decode-and-forward (DF) cooperative network. Relay nodes adopt Orthogonal Space Time Block Code (OSTBC) to achieve spatial diversity. Assume that relay nodes may misbehave with a certain probability. If a relay is malicious, it will garble transmission signals, resulting in severe damage to system performance. In the literature, detecting malicious relays requires perfect channel state information. However, misbehavior of the relay will first lead to inaccurate channel estimation. Therefore, we propose a novel detecting misbehavior scheme to deal with the dilemma. Since misbehavior of relays influences statistical properties of the estimated channel coefficients, destination can detect misbehaving by comparing the distribution of channel estimates. The most important of all is that we don¡¦t need channel state information to enhance detecting performance. Through simulation results, we verify proposed scheme can detect misbehavior effectively without channel state information. Compared with signal-to-noise ratio, the number of received tracing symbols has more significant impact on detecting misbehavior of the relay.

The safety and efficiency of vehicular communications rely on the correctness of the data exchanged between vehicles. Location spoofing is a proven and powerful attack against Vehicle-to-everything (V2X) communication systems that can cause traffic congestion and other safety hazards. Recent work also demonstrates practical spoofing attacks that can confuse intelligent transportation systems at road intersections. In this work, we propose two self-reliant schemes at the application layer and the physical layer to detect such misbehaviors. These schemes can be run independently by each vehicle and do not rely on the assumption that the majority of vehicles is honest. We first propose a scheme that uses application-layer plausibility checks as a feature vector for machine learning models. Our results show that this scheme improves the precision of the plausibility checks by over 20% by using them as feature vectors in KNN and SVM classifiers. We also show how to classify different types of known misbehaviors, once they are detected. We then propose three novel physical layer plausibility checks that leverage the received signal strength indicator (RSSI) of basic safety messages (BSMs). These plausibility checks have multi-step mechanisms to improve not only the detection rate, but also to decrease false positives. We comprehensively evaluate the performance of these plausibility checks using the VeReMi dataset (which we enhance along the way) for several types of attacks. We show that the best performing physical layer plausibility check among the three considered achieves an overall detection rate of 83.73% and a precision of 95.91%. The proposed application-layer and physical-layer plausibility checks provide a promising framework toward the deployment of on self-reliant misbehavior detection systems.