Design of a Chatbot Social Engineering Victim

Social engineering is an ever-growing problem in online and offline communication. Companies invest time and resources to train employees not to fall victim to attacks. The concept of adversarial thinking encourages people to learn the ways of the attacker to better defend themselves. This research introduces the design features of a chatbot that plays the role of a social engineering victim to allow people to perform the role of an attacker in a training exercise. By attacking this chatbot, people can learn better how to defend themselves

Facilitating Natural Conversational Agent Interactions: Lessons from a Deception Experiment

This study reports the results of a laboratory experiment exploring interactions between humans and a conversational agent. Using the ChatScript language, we created a chat bot that asked participants to describe a series of images. The two objectives of this study were (1) to analyze the impact of dynamic responses on participants’ perceptions of the conversational agent, and (2) to explore behavioral changes in interactions with the chat bot (i.e. response latency and pauses) when participants engaged in deception. We discovered that a chat bot that provides adaptive responses based on the participant’s input dramatically increases the perceived humanness and engagement of the conversational agent. Deceivers interacting with a dynamic chat bot exhibited consistent response latencies and pause lengths while deceivers with a static chat bot exhibited longer response latencies and pause lengths. These results give new insights on social interactions with computer agents during truthful and deceptive interactions

The effect of conversational agent skill on user behavior during deception

Conversational agents (CAs) are an integral component of many personal and business interactions. Many recent advancements in CA technology have attempted to make these interactions more natural and human-like. However, it is currently unclear how human-like traits in a CA impact the way users respond to questions from the CA. In some applications where CAs may be used, detecting deception is important. Design elements that make CA interactions more human-like may induce undesired strategic behaviors from human deceivers to mask their deception. To better understand this interaction, this research investigates the effect of conversational skill—that is, the ability of the CA to mimic human conversation—from CAs on behavioral indicators of deception. Our results show that cues of deception vary depending on CA conversational skill, and that increased conversational skill leads to users engaging in strategic behaviors that are detrimental to deception detection. This finding suggests that for applications in which it is desirable to detect when individuals are lying, the pursuit of more human-like interactions may be counter-productive

Developing a measure of adversarial thinking in social engineering scenarios

Social engineering is a major issue for organizations. In this paper, we propose that increasing adversarial thinking can improve individual resistance to social engineering attacks. We formalize our understanding of adversarial thinking using Utility Theory. Next a measure of adversarial thinking in a text-based context. Lastly the paper reports on two studies that demonstrate the effectiveness of the newly developed measure. We show that the measure of adversarial thinking has variability, can be manipulated with training, and that it is not influenced significantly by priming. The paper also shows that social engineering training has an influence on adversarial thinking and that practicing against an adversarial conversational agent has a positive influence on adversarial thinking

Characterization of Aerospike Nozzle Flows

Aerospike nozzles possess many qualities that make them more desirable and efficient than conventional bell-shaped rocket nozzles. Aerospike nozzles have been studied since the 1960s, but problems and limitations with experimentation often led to abandoning further efforts on aerospike nozzles and implementing much more familiar bell-shaped nozzles. In fact, aerospike nozzles have yet to be used in flight—they have only undergone ground testing. The goal of our research is to develop multiple additively manufactured aerospike nozzles and characterize the flow experimentally, numerically, and computationally. Schlieren photography and Particle Image Velocimetry (PIV) are used to experimentally characterize the flow, ANSYS CFD software and SolidWorks Flow Simulation are used to computationally analyze the nozzle flows, and hand calculations with the assistance of Matlab and Microsoft Excel are performed to analyze the nozzle flows numerically. Using these methods, we will study and compare the flows present in aerospike nozzles with a singular annular entrance as well as multiple orifice entries. To date, we have developed an experimental setup and procedure to study the nozzles we produce. Furthermore, using this setup we\u27ve successfully designed, manufactured, and analyzed a converging-diverging nozzle for our setup that produces supersonic flow—a necessary property of flow to accurately characterize nozzles use for aerospace applications. We hope that our research helps to develop a better understanding of aerospike nozzles and their many advantages over the bell nozzle, and motivates further research and eventually the implementation of aerospike nozzles in both aircraft and spacecraft.https://engagedscholarship.csuohio.edu/u_poster_2016/1052/thumbnail.jp

Facilitating Natural Conversational Agent Interactions: Lessons from a Deception Experiment

This study reports the results of a laboratory experiment exploring interactions between humans and a conversational agent. Using the ChatScript language, we created a chat bot that asked participants to describe a series of images. The two objectives of this study were (1) to analyze the impact of dynamic responses on participants’ perceptions of the conversational agent, and (2) to explore behavioral changes in interactions with the chat bot (i.e. response latency and pauses) when participants engaged in deception. We discovered that a chat bot that provides adaptive responses based on the participant’s input dramatically increases the perceived humanness and engagement of the conversational agent. Deceivers interacting with a dynamic chat bot exhibited consistent response latencies and pause lengths while deceivers with a static chat bot exhibited longer response latencies and pause lengths. These results give new insights on social interactions with computer agents during truthful and deceptive interactions

The Influence of Conversational Agents on Socially Desirable Responding

Conversational agents (CAs) are becoming an increasingly common component in many information systems. The ubiquity of CAs in cell phones, entertainment systems, and messaging applications has led to a growing need to understand how design choices made when developing CAs influence user interactions. In this study, we explore the use case of CAs that gather potentially sensitive information from people-”for example, in a medical interview. Using a laboratory experiment, we examine the influence of CA responsiveness and embodiment on the answers people give in response to sensitive and non-sensitive questions. The results show that for sensitive questions, the responsiveness of the CA increased the social desirability of the responses given by participants

The influence of conversational agent embodiment and conversational relevance on socially desirable responding

Conversational agents (CAs) are becoming an increasingly common component in a wide range of information systems. A great deal of research to date has focused on enhancing traits that make CAs more humanlike. However, few studies have examined the influence such traits have on information disclosure. This research builds on self-disclosure, social desirability, and social presence theories to explain how CA anthropomorphism affects disclosure of personally sensitive information. Taken together, these theories suggest that as CAs become more humanlike, the social desirability of user responses will increase. In this study, we use a laboratory experiment to examine the influence of two elements of CA design—conversational relevance and embodiment—on the answers people give in response to sensitive and non-sensitive questions. We compare the responses given to various CAs to those given in a face-to-face interview and an online survey. The results show that for sensitive questions, CAs with better conversational abilities elicit more socially desirable responses from participants, with a less significant effect found for embodiment. These results suggest that for applications where eliciting honest answers to sensitive questions is important, CAs that are “better” in terms of humanlike realism may not be better for eliciting truthful responses to sensitive questions

ERK1/2 signaling induces skeletal muscle slow fiber-type switching and reduces muscular dystrophy disease severity

© 2019 American Society for Clinical Investigation. MAPK signaling consists of an array of successively acting kinases. ERK1 and -2 (ERK1/2) are major components of the greater MAPK cascade that transduce growth factor signaling at the cell membrane. Here, we investigated ERK1/2 signaling in skeletal muscle homeostasis and disease. Using mouse genetics, we observed that the muscle-specifc expression of a constitutively active MEK1 mutant promotes greater ERK1/2 signaling that mediates fber-type switching to a slow, oxidative phenotype with type I myosin heavy chain expression. Using a conditional and temporally regulated Cre strategy, as well as Mapk1 (ERK2) and Mapk3 (ERK1) genetically targeted mice, MEK1-ERK2 signaling was shown to underlie this fast-to-slow fber-type switching in adult skeletal muscle as well as during development. Physiologic assessment of these activated MEK1-ERK1/2 mice showed enhanced metabolic activity and oxygen consumption with greater muscle fatigue resistance. In addition, induction of MEK1-ERK1/2 signaling increased dystrophin and utrophin protein expression in a mouse model of limb-girdle muscle dystrophy and protected myofbers from damage. In summary, sustained MEK1-ERK1/2 activity in skeletal muscle produces a fast-to-slow fber-type switch that protects from muscular dystrophy, suggesting a therapeutic approach to enhance the metabolic effectiveness of muscle and protect from dystrophic disease

Multi-modal biomarkers of low back pain: A machine learning approach

Chronic low back pain (LBP) is a very common health problem worldwide and a major cause of disability. Yet, the lack of quantifiable metrics on which to base clinical decisions leads to imprecise treatments, unnecessary surgery and reduced patient outcomes. Although, the focus of LBP has largely focused on the spine, the literature demonstrates a robust reorganization of the human brain in the setting of LBP. Brain neuroimaging holds promise for the discovery of biomarkers that will improve the treatment of chronic LBP. In this study, we report on morphological changes in cerebral cortical thickness (CT) and resting-state functional connectivity (rsFC) measures as potential brain biomarkers for LBP. Structural MRI scans, resting state functional MRI scans and self-reported clinical scores were collected from 24 LBP patients and 27 age-matched healthy controls (HC). The results suggest widespread differences in CT in LBP patients relative to HC. These differences in CT are correlated with self-reported clinical summary scores, the Physical Component Summary and Mental Component Summary scores. The primary visual, secondary visual and default mode networks showed significant age-corrected increases in connectivity with multiple networks in LBP patients. Cortical regions classified as hubs based on their eigenvector centrality (EC) showed differences in their topology within motor and visual processing regions. Finally, a support vector machine trained using CT to classify LBP subjects from HC achieved an average classification accuracy of 74.51%, AUC = 0.787 (95% CI: 0.66-0.91). The findings from this study suggest widespread changes in CT and rsFC in patients with LBP while a machine learning algorithm trained using CT can predict patient group. Taken together, these findings suggest that CT and rsFC may act as potential biomarkers for LBP to guide therapy