arXiv subjects

Shouhuai Xu

Publications and source records attributed to Shouhuai Xu.

At least 19 recordsLinked to original sources

arXiv2026

Tractable Defense against Advanced Persistent Threats in Networked Settings

Recently, the theory of Boolean Dynamical Systems was proposed to study the decision theory surrounding the defense of computer networks against Advanced Persistent Threats (APTs). Boolean Dynamical Systems naturally capture four first principle primitives of APTs: the stealthy nature of attacks, limited and noisy information from automated systems like intrusion detection systems, lateral movement after the attacker penetrates into the network, and the defender's ability to secure a subset of computers at any time at the loss of resources such as system uptime. Currently, doing optimal/heuristic control in a computationally tractable manner is not possible because the emergent value function is computationally intractable (with respect to the network size). To resolve this, we propose a mean-field analysis inspired heuristic value function. We prove that our proposed heuristic is based on an exact computation of the value function under the assumption that the underlying state estimate distribution maximizes entropy. We numerically evaluate the quality of our heuristic as parameterized by the degree to which the entropy assumptions are violated.

cs.CR↗

arXiv2026

Measuring Time-Horizon Engagement Effectiveness: Persistence, Recency, and Re-Emergence

Online attention is commonly summarized using cumulative volume, peak activity, or arithmetic averages, but such measures can obscure differences between activity that is sustained over time, concentrated near the present, or renewed after dormancy. This paper introduces the Time-Horizon Engagement Effectiveness (TH-EE) framework, which constructs interpretable temporal profiles of online attention by distinguishing three related but non-equivalent properties: persistence, recency, and re-emergence. We evaluate the framework through controlled engagement traces, a proof-of-concept application to 1,850 YouTube videos across 37 topics (18 cohorts selected a priori as exemplars of persistent, acute, cyclical, and recently originating attention, and 19 cohorts corresponding to authoritatively debunked claims), and an event-level validation on eleven years of daily Wikipedia pageview series for the same topics. The controlled analyses show that the framework distinguishes distributed activity from concentrated bursts, introduces temporal-order sensitivity through recency weighting, and identifies renewed activity after a defined dormant interval. On the event-level series, the framework's reactivations co-locate with Kleinberg burst onsets, and PELT change points far more often than chance. The YouTube application shows that debunked-claim cohorts do not occupy a unique region of temporal-profile space; they exhibit heterogeneous patterns that overlap substantially with benign topics. These results support treating persistence, recency, and re-emergence as separate dimensions of online attention. TH-EE is a descriptive and comparative measurement framework, not a classifier of misinformation, coordination, intent, or content veracity.

cs.SI↗

arXiv2026

Exploring the OODA Loop as a Systematic Way of Thinking in Coping with Conflicts

When conflicts emerge, we need systematic ways of thinking to deal with them. This paper revisits Boyd's Observe, Orient, Decide, Act (OODA) loop and explores its usefulness as a systematic way of thinking for reasoning about conflicts in dynamic environments characterized by uncertainty, adaptation, and adversarial interference. We explore the OODA loop beyond its origin in air warfare where it focuses attention on the relationship between information, understanding, choice, and action. Our exploration is conducted in two application domains: cyber conflicts and cognitive conflicts. Across both domains, we emphasize situational awareness as a critical mechanism of orientation, while noting that observations become useful only when they are perceived, comprehended, projected into possible futures, and integrated with mental models, objectives, doctrine, trust, and experience. Our exploration suggests that conflict is not merely a contest of actions or effects, but a contest over the ability to generate, protect, and leverage one's own superior observation, orientation, and decision, while degrading and exploiting adversary's observation, orientation, and decision.

cs.CR↗

arXiv2026

ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning

LLM-based agents can recover from individual execution errors, yet they repeatedly fail on the same fault when the underlying process knowledge--operator schemas, preconditions, and constraints--remains unrepaired. Existing self-evolving approaches address this gap by updating prompts, memory, or model weights, but none directly repair the symbolic structures that encode how tasks are executed, and few provide the governance guarantees required for safe deployment. We introduce ANNEAL, a neuro-symbolic agent that converts recurring failures into governed symbolic edits of a process knowledge graph without modifying foundation model weights. Its core mechanism, Failure-Driven Knowledge Acquisition (FDKA), localizes the responsible operator, synthesizes a typed patch through constrained LLM generation, and validates the proposal via multi-dimensional scoring, symbolic guardrails, and canary testing before commit. Every accepted edit carries full provenance and deterministic rollback capability. Across four domains and 27 multi-seed runs, ANNEAL is the only evaluated system that commits persistent structural repairs--strong baselines such as ReAct and Reflexion achieve high episodic recovery yet retain 72--100% holdout failure rates on recurring faults, whereas ANNEAL reduces these to 0% in the tested recurring-failure settings. Ablation confirms that removing FDKA eliminates all structural repairs and drops success rate by up to 26.7 percentage points. These results suggest that governed symbolic repair offers a complementary paradigm to weight-level and prompt-level adaptation for persistent fault elimination.

cs.AI↗

arXiv2026

Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities

Cybersecurity demands both rapid pattern recognition and deliberative reasoning, yet purely neural or purely symbolic approaches each address only one side of this duality. Neuro-Symbolic (NeSy) AI bridges this gap by integrating learning and logic within a unified framework. This systematic review analyzes 103 publications across the neural-symbolic integration spectrum in cybersecurity through April 2026, organizing them via a three-tier taxonomy -- deep integration, structured interaction, and contextual baselines -- and a Grounding-Instructibility-Alignment (G-I-A) analytical lens. We find that multi-agent and structured-integration architectures across the surveyed spectrum substantially outperform single-agent approaches in complex scenarios, causal reasoning enables proactive defense beyond correlation-based detection, and knowledge-guided learning improves both data efficiency and explainability. These findings span intrusion detection, malware analysis, vulnerability discovery, and autonomous penetration testing, revealing that integration depth often correlates with capability gains across domains. A first-of-its-kind dual-use analysis further shows that autonomous offensive systems in the broader survey corpus are already achieving notable zero-day exploitation success at significantly reduced cost, fundamentally reshaping threat landscapes. However, critical barriers persist: evaluation standardization remains nascent, computational costs constrain deployment, and effective human-AI collaboration is underexplored. We distill these findings into a prioritized research roadmap emphasizing community-driven benchmarks, responsible development practices, and defensive alignment to guide the next generation of NeSy cybersecurity systems.

cs.CR↗

arXiv2026

Short Message Service (SMS) Phishing Attacks and Defenses: A Systematic Review

SMS Phishing (also known as 'smishing') is a growing deceptive social engineering (SE) attack that leverages mobile SMS to conduct cybercrimes such as stealing sensitive information or spreading malware by tricking users into interacting with attackers' messages (e.g., responding to or clicking URLs). This threat has increased rapidly in recent years, causing $470M in financial losses for United States users in 2024 alone. This threat is also evolving rapidly, meaning that attackers continually adapt their tactics, reshaping the landscape. There is a significant body of literature on investigating smishing attacks and defenses. However, there is no systematic review that reflects the current attack and defense landscape along with available resources (i.e., relevant datasets). This motivates us to systematize the current smishing research efforts, including the following four research pillars: (a) user perception and susceptibility, (b) attack characterization, (c) defense landscape, and (d) smishing datasets. This leads us to propose novel future research directions towards effectively mitigating smishing attacks.

cs.CR↗

arXiv2026

Towards a Systematic Taxonomy of Attacks against Space Infrastructures

Space infrastructures represent an emerging domain that is critical to the global economy and society. However, this domain is vulnerable to attacks, including cyber attacks and other kinds of attacks. To enhance the resilience of this domain, we must understand these attacks that can be waged against it and the defenses that can be employed to mitigate these attacks. The status quo is that there is neither a systematic understanding of these attacks against, nor defenses for, space infrastructures, despite their clear importance in guiding systematic analysis of space security and future research. In this paper, we fill the void by proposing the first systematic taxonomy of attacks against, and defenses for, space infrastructures. We hope this paper will inspire a community effort at refining the taxonomy towards a widely used one.

cs.CR↗

arXiv2026

Cognitive Warfare: Definition, Framework, and Case Study

Cognitive warfare has emerged as a central feature of modern conflict, yet it remains inconsistently defined and difficult to evaluate. Existing approaches often treat cognitive operations as a subset of information operations, limiting the ability to assess cognitive attacker-defender interactions or determine when advantage has been achieved. This article proposes a unified definition of cognitive warfare, introduces an interaction framework grounded in the OODA loop, and identifies measurable attributes associated with cognitive superiority. To illustrate the use of the framework, a notional case study demonstrates how these concepts can be applied to assess cognitive attacks and defenses in a contested environment. Thus, the framework provides joint force leaders and analysts with a practical foundation for understanding, comparing, and evaluating cognitive warfare campaigns.

cs.SI↗

arXiv2025

Semantically-Equivalent Transformations-Based Backdoor Attacks against Neural Code Models: Characterization and Mitigation

Neural code models have been increasingly incorporated into software development processes. However, their susceptibility to backdoor attacks presents a significant security risk. The state-of-the-art understanding focuses on injection-based attacks, which insert anomalous patterns into software code. These attacks can be neutralized by standard sanitization techniques. This status quo may lead to a false sense of security regarding backdoor attacks. In this paper, we introduce a new kind of backdoor attacks, dubbed Semantically-Equivalent Transformation (SET)-based backdoor attacks, which use semantics-preserving low-prevalence code transformations to generate stealthy triggers. We propose a framework to guide the generation of such triggers. Our experiments across five tasks, six languages, and models like CodeBERT, CodeT5, and StarCoder show that SET-based attacks achieve high success rates (often >90%) while preserving model utility. The attack proves highly stealthy, evading state-of-the-art defenses with detection rates on average over 25.13% lower than injection-based counterparts. We evaluate normalization-based countermeasures and find they offer only partial mitigation, confirming the attack's robustness. These results motivate further investigation into scalable defenses tailored to SET-based attacks.

cs.SE↗

arXiv2025

Characterizing Cyber Attacks against Space Infrastructures with Missing Data: Framework and Case Study

Cybersecurity of space infrastructures is an emerging topic, despite space-related cybersecurity incidents occurring as early as 1977 (i.e., hijacking of a satellite transmission signal). There is no single dataset that documents cyber attacks against space infrastructures that have occurred in the past; instead, these incidents are often scattered in media reports while missing many details, which we dub the missing-data problem. Nevertheless, even ``low-quality'' datasets containing such reports would be extremely valuable because of the dearth of space cybersecurity data and the sensitivity of space infrastructures which are often restricted from disclosure by governments. This prompts a research question: How can we characterize real-world cyber attacks against space infrastructures? In this paper, we address the problem by proposing a framework, including metrics, while also addressing the missing-data problem by leveraging methodologies such as the Space Attack Research and Tactic Analysis (SPARTA) and the Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) to ``extrapolate'' the missing data in a principled fashion. We show how the extrapolated data can be used to reconstruct ``hypothetical but plausible'' space cyber kill chains and space cyber attack campaigns that have occurred in practice. To show the usefulness of the framework, we extract data for 108 cyber attacks against space infrastructures and show how to extrapolate this ``low-quality'' dataset containing missing information to derive 6,206 attack technique-level space cyber kill chains. Our findings include: cyber attacks against space infrastructures are getting increasingly sophisticated; successful protection of the link segment between the space and user segments could have thwarted nearly half of the 108 attacks. We will make our dataset available.

cs.CR↗

arXiv2025

Towards Proactive Defense Against Cyber Cognitive Attacks

Cyber cognitive attacks leverage disruptive innovations (DIs) to exploit psychological biases and manipulate decision-making processes. Emerging technologies, such as AI-driven disinformation and synthetic media, have accelerated the scale and sophistication of these threats. Prior studies primarily categorize current cognitive attack tactics, lacking predictive mechanisms to anticipate future DIs and their malicious use in cognitive attacks. This paper addresses these gaps by introducing a novel predictive methodology for forecasting the emergence of DIs and their malicious uses in cognitive attacks. We identify trends in adversarial tactics and propose proactive defense strategies.

cs.CR↗

arXiv2025

Quantifying the Engagement Effectiveness of Cyber Cognitive Attacks: A Behavioral Metric for Disinformation Campaigns

As disinformation-driven cognitive attacks become increasingly sophisticated, the ability to quantify their impact is essential for advancing cybersecurity defense strategies. This paper presents a novel framework for measuring the engagement effectiveness of cognitive attacks by introducing a weighted interaction metric that accounts for both the type and volume of user engagement relative to the number of attacker-generated transmissions. Applying this model to real-world disinformation campaigns across social media platforms, we demonstrate how the metric captures not just reach but the behavioral depth of user engagement. Our findings provide new insights into the behavioral dynamics of cognitive warfare and offer actionable tools for researchers and practitioners seeking to assess and counter the spread of malicious influence online.

cs.CY↗

arXiv2025

Characterizing Event-themed Malicious Web Campaigns: A Case Study on War-themed Websites

Cybercrimes such as online scams and fraud have become prevalent. Cybercriminals often abuse various global or regional events as themes of their fraudulent activities to breach user trust and attain a higher attack success rate. These attacks attempt to manipulate and deceive innocent people into interacting with meticulously crafted websites with malicious payloads, phishing, or fraudulent transactions. To deepen our understanding of the problem, this paper investigates how to characterize event-themed malicious website-based campaigns, with a case study on war-themed websites. We find that attackers tailor their attacks by exploiting the unique aspects of events, as evidenced by activities such as fundraising, providing aid, collecting essential supplies, or seeking updated news. We use explainable unsupervised clustering methods to draw further insights, which could guide the design of effective early defenses against various event-themed malicious web campaigns.

cs.CR↗

arXiv2025

Optimizing Preventive and Reactive Defense Resource Allocation with Uncertain Sensor Signals

Cyber attacks continue to be a cause of concern despite advances in cyber defense techniques. Although cyber attacks cannot be fully prevented, standard decision-making frameworks typically focus on how to prevent them from succeeding, without considering the cost of cleaning up the damages incurred by successful attacks. This motivates us to investigate a new resource allocation problem formulated in this paper: The defender must decide how to split its investment between preventive defenses, which aim to harden nodes from attacks, and reactive defenses, which aim to quickly clean up the compromised nodes. This encounters a challenge imposed by the uncertainty associated with the observation, or sensor signal, whether a node is truly compromised or not; this uncertainty is real because attack detectors are not perfect. We investigate how the quality of sensor signals impacts the defender's strategic investment in the two types of defense, and ultimately the level of security that can be achieved. In particular, we show that the optimal investment in preventive resources increases, and thus reactive resource investment decreases, with higher sensor quality. We also show that the defender's performance improvement, relative to a baseline of no sensors employed, is maximal when the attacker can only achieve low attack success probabilities.

eess.SY↗

arXiv2025

Efficient State Estimation of a Networked FlipIt Model

The Boolean Kalman Filter and associated Boolean Dynamical System Theory have been proposed to study the spread of infection on computer networks. Such models feature a network where attacks propagate through, an intrusion detection system that provides noisy signals of the true state of the network, and the capability of the defender to clean a subset of computers at any time. The Boolean Kalman Filter has been used to solve the optimal estimation problem, by estimating the hidden true state given the attack-defense dynamics and noisy observations. However, this algorithm is intractable because it runs in exponential time and space with respect to the network size. We address this feasibility problem by proposing a mean-field estimation approach, which is inspired by the epidemic modeling literature. Although our approach is heuristic, we prove that our estimator exactly matches the optimal estimator in certain non-trivial cases. We conclude by using simulations to show both the run-time improvement and estimation accuracy of our approach.

cs.CR↗

arXiv2025

Space Cybersecurity Testbed: Fidelity Framework, Example Implementation, and Characterization

Cyber threats against space infrastructures, including satellites and systems on the ground, have not been adequately understood. Testbeds are important to deepen our understanding and validate space cybersecurity studies. The state of the art is that there are very few studies on building testbeds, and there are few characterizations of testbeds. In this paper, we propose a framework for characterizing the fidelity of space cybersecurity testbeds. The framework includes 7 attributes for characterizing the system models, threat models, and defenses that can be accommodated by a testbed. We use the framework to guide us in building and characterizing a concrete testbed we have implemented, which includes space, ground, user, and link segments. In particular, we show how the testbed can accommodate some space cyber attack scenarios that have occurred in the real world, and discuss future research directions.

cs.CR↗

arXiv2025

SoK: Automated Vulnerability Repair: Methods, Tools, and Assessments

The increasing complexity of software has led to the steady growth of vulnerabilities. Vulnerability repair investigates how to fix software vulnerabilities. Manual vulnerability repair is labor-intensive and time-consuming because it relies on human experts, highlighting the importance of Automated Vulnerability Repair (AVR). In this SoK, we present the systematization of AVR methods through the three steps of AVR workflow: vulnerability analysis, patch generation, and patch validation. We assess AVR tools for C/C++ and Java programs as they have been widely studied by the community. Since existing AVR tools for C/C++ programs are evaluated with different datasets, which often consist of a few vulnerabilities, we construct the first C/C++ vulnerability repair benchmark dataset, dubbed Vul4C, which contains 144 vulnerabilities as well as their exploits and patches. We use Vul4C to evaluate seven AVR tools for C/C++ programs and use the third-party Vul4J dataset to evaluate two AVR tools for Java programs. We also discuss future research directions.

cs.SE↗

arXiv2025

Game-Theoretic Cybersecurity: the Good, the Bad and the Ugly

Given the scale of consequences attributable to cyber attacks, the field of cybersecurity has long outgrown ad-hoc decision-making. A popular choice to provide disciplined decision-making in cybersecurity is Game Theory, which seeks to mathematically understand strategic interaction. In practice though, game-theoretic approaches are scarcely utilized (to our knowledge), highlighting the need to understand the deficit between the existing state-of-the-art and the needs of cybersecurity practitioners. Therefore, we develop a framework to characterize the function and assumptions of existing works as applied to cybersecurity and leverage it to characterize 80 unique technical papers. Then, we leverage this information to analyze the capabilities of the proposed models in comparison to the application-specific needs they are meant to serve, as well as the practicality of implementing the proposed solution. Our main finding is that Game Theory largely fails to incorporate notions of uncertainty critical to the application being considered. To remedy this, we provide guidance in terms of how to incorporate uncertainty in a model, what forms of uncertainty are critical to consider in each application area, and how to model the information that is available in each application area.

cs.GT↗