Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning

ArXiv.org · 2026-03-21

Unmanned Aerial Vehicles (UAVs) are valuable for mission-critical systems like surveillance, rescue, or delivery. Not surprisingly, such systems attract cyberattacks, including Denial-of-Service (DoS) attacks to overwhelm the resources of mission drones (MDs). How can we defend UAV mission systems against DoS attacks? We adopt cyber deception as a defense strategy, in which honey drones (HDs) are proposed to bait and divert attacks. The attack and deceptive defense hinge upon radio signal strength: The attacker selects victim MDs based on their signals, and HDs attract the attacker from afar by emitting stronger signals, despite this reducing battery life. We formulate an optimization problem for the attacker and defender to identify their respective strategies for maximizing mission performance while minimizing energy consumption. To address this problem, we propose a novel approach, called HT-DRL. HT-DRL identifies optimal solutions without a long learning convergence time by taking the solutions of hypergame theory into the neural network of deep reinforcement learning. This achieves a systematic way to intelligently deceive attackers. We analyze the performance of diverse defense mechanisms under different attack strategies. Further, the HT-DRL-based HD approach outperforms existing non-HD counterparts up to two times better in mission performance while incurring low energy consumption.

WITHDRAWN 2/4 email from Jiaqing Yuan jiaqing111yuan@gmail.com -- Right vs. Right: Evaluating How LLMs Handle Ethical Dilemmas

2026-05-24

Consent Violations and Ambivalence: Analyzing Reddit Stories Around Sexual Assault

SSRN Electronic Journal · 2026-01-01

Understanding Narratives of Trauma on Social Media

2025-05-19 · 1 citations

Background: Victims of domestic and sexual violence often share their narratives on social media. Doing so helps them access validation, solidarity, and support from external sources, which has been shown to enhance resilience and facilitate healing. Problem Statement: We address two aspects of such narratives of trauma: (1) identifying causal relationships between narrative elements and (2) analyzing the effect of such elements on social support received. Method: We retrieved 5561 such narratives from Reddit, a popular online platform. We applied Large Language Models to extract features from these narratives and analyzed them computationally. Findings: Our analysis reveals that prolonged abuse increases selfblame and reduces the intent to seek legal advice; the presence of support increases the likelihood of a victim adopting coping strategies; night-time abuse and intoxication are strongly associated with higher rates of violence; victims experiencing nightmares are more likely to provide detailed descriptions of their abusers; suffering economic and familial abuse increases the support received online. Our research thus corroborates leading psychological theories of narrative, social support, and resilience in online stories and contributes to understanding trauma narratives. In this way, our research can facilitate enhanced social support for victims.

Analyzing Reddit Stories of Sexual Violence: Incidents, Effects, and Requests for Advice

Proceedings of the International AAAI Conference on Web and Social Media · 2025-06-07

Warning: This paper may contain triggering language for some readers, especially survivors of sexual violence. Survivors of sexual violence sometimes share their experiences on social media, revealing their feelings and emotions and seeking advice. On platforms such as Reddit, some stories can be long---up to 40,000 characters. We posit that such long stories are demanding for helpers to read and respond to. Prior research has indicated that parts of these stories describing the incident, the effects on the poster, and advice requested by the poster are important. Highlighting those parts can draw helpers' attention toward key information and assist them in reading and responding to long stories. We first examine the stories posted on Reddit for the prevalence of these parts. Second, we develop a computational model to highlight these parts of a story. On ten-fold cross-validation of a dataset, our model achieves a macro F1 score of 0.82. In addition, we contribute METHREE, a dataset comprising 8,947 labeled sentences for these parts from Reddit stories. A survey of users who are helpers on some relevant subreddits shows that the parts highlighted by our tool represent important information and assist them while reading and responding to long stories. We find that these tool-generated highlights statistically significantly reduce the demandingness of long stories. Moreover, almost all helpers felt that highlighted stories are helpful and easier to read, understand, and respond to than nonhighlighted ones. In particular, on a 4-point Likert scale, there is about 0.7 point reduction in demandingess when stories were presented with highlights.

Reasoner Outperforms: Generative Stance Detection with Rationalization for Social Media

2025-08-26

MEAL: Model of Empathy Augmented Logistics for Food Security

IEEE Internet Computing · 2025-05-01

Millions globally lack reliable access to nutritious food. Efforts to address food insecurity seek to provide consumers food that may be rescued (i.e., what warehouses or grocers would otherwise soon discard as unusable), directly donated, or acquired using governmental funds. Current approaches produce allocations that optimize global objectives to store and move food efficiently in the network. However, they largely overlook consumer preferences and constraints. As a result, the resulting allocations lead to consumers either using foods they don’t care for or discarding such foods, leading to food waste. This article presents a new model, evaluated via human study and agent-based simulation, that shows how to combine the consumer and provider perspectives. We find that persuasive messages that include individual circumstances and the social context can promote prosociality and empathy, leading to improved outcomes overall.

Theory of Mind in Action: The Instruction Inference Task in Dynamic Human-Agent Collaboration

ArXiv.org · 2025-06-26

Successful human-agent teaming relies on an agent being able to understand instructions given by a (human) principal. In many cases, an instruction may be incomplete or ambiguous. In such cases, the agent must infer the unspoken intentions from their shared context, that is, it must exercise the principal's Theory of Mind (ToM) and infer the mental states of its principal. We consider the prospects of effective human-agent collaboration using large language models (LLMs). To assess ToM in a dynamic, goal-oriented, and collaborative environment, we introduce a novel task, Instruction Inference, in which an agent assists a principal in reaching a goal by interpreting incomplete or ambiguous instructions. We present Tomcat, an LLM-based agent, designed to exhibit ToM reasoning in interpreting and responding to the principal's instructions. We implemented two variants of Tomcat. One, dubbed Fs-CoT (Fs for few-shot, CoT for chain-of-thought), is based on a small number of examples demonstrating the requisite structured reasoning. One, dubbed CP (commonsense prompt), relies on commonsense knowledge and information about the problem. We realized both variants of Tomcat on three leading LLMs, namely, GPT-4o, DeepSeek-R1, and Gemma-3-27B. To evaluate the effectiveness of Tomcat, we conducted a study with 52 human participants in which we provided participants with the same information as the CP variant. We computed intent accuracy, action optimality, and planning optimality to measure the ToM capabilities of Tomcat and our study participants. We found that Tomcat with Fs-CoT, particularly with GPT-4o and DeepSeek-R1, achieves performance comparable to the human participants, underscoring its ToM potential for human-agent collaboration.

Moral Sparks in Social Media Narratives

2025-08-26 · 1 citations

Comparative Analysis of Different Cloud Computing Security Services offered By Microsoft Azure, Amazon Web Services and Google Cloud Platform

International Journal of Advanced Research in Science Communication and Technology · 2025-04-04

This paper presents a novel approach toward a comprehensive analysis of various simulation-based tools to test and measure the Cloud Datacenter performance, scalability, robustness and complexity. There are different Cloud Datacenter resources in cloud Computing Infrastructure like Virtual Machine, CPU, RAM, SAN, LAN and WAN topologies. The server machines need to be analyzed for their utilization in terms of energy and service to clients in cloud computing. We have analyzed various Cloud resources using CloudSim, CloudReports and Cloud Analyst tools. Resources provIsIOning, Cloud Management, Load Balancing, Robustness and Cloud Scalability are this paper's primary scope of work. In this regard, some Simulation test results and Simulations are presented to compare them with real-time scenarios to bring the performance and scalability issues to our notice for future directions