arXiv subjects

Christopher Kiekintveld

Publications and source records attributed to Christopher Kiekintveld.

At least 19 recordsLinked to original sources

arXiv2026

Beyond Best Response: Quantal Stackelberg Deception as Insurance Against Attacker Misspecification

Stackelberg Security Games (SSG) assume that an attacker observes the defender's strategy and chooses the target that maximizes their expected utility perfectly. In most realistic applications this is not plausible, and in the case of cyber deception (e.g., using decoys) the purpose of the game is to induce uncertainty and mistakes. Quantal response is a common way to represent noise and mistakes in decision-making; here it replaces perfect best-response with a logit choice with rationality parameter $λ$ and results in a generalized Quantal Stackelberg Equilibrium (QSE), which recovers the classical solution exactly as $λ\rightarrow \infty$. We conduct a deeper analysis of how QSE can function as a generalized form of insurance against a variety of forms of model specification error/uncertainty; our analysis shows that QSE provides a practical way to address the important role of tie-breaking rules and model uncertainty in SSG from both a theoretical and practical perspective. We conduct an empirical evaluation in a cybersecurity case study with two networks and real vulnerabilities drawn from CVE and scored using the Common Vulnerability Scoring System (CVSS). QSE beats Stackelberg in realized defender utility spanning 144 scenarios with specification errors and 25 parameter configurations, with gains of 46\% to 175\% showing a substantial advantage in a wide variety of realistic cases.

cs.CR↗

arXiv2026

Evaluating Generalization Mechanisms in Autonomous Cyber Attack Agents

Autonomous offensive agents often fail to transfer beyond the networks on which they are trained. We isolate a minimal but fundamental shift -- unseen host/subnet IP reassignment in an otherwise fixed enterprise scenario -- and evaluate attacker generalization in the NetSecGame environment. Agents are trained on five IP-range variants and tested on a sixth unseen variant; only the meta-learning agent may adapt at test time. We compare three agent families (traditional RL, adaptation agents, and LLM-based agents) and use action-distribution-based behavioral/XAI analyses to localize failure modes. Some adaptation methods show partial transfer but significant degradation under unseen reassignment, indicating that even address-space changes can break long-horizon attack policies. Under our evaluation protocol and agent-specific assumptions, prompt-driven pretrained LLM agents achieve the highest success on the held-out reassignment, but at the cost of increased inference-time compute, reduced transparency, and practical failure modes such as repetition/invalid-action loops.

cs.CR↗

arXiv2026

Coordinated Multi-Domain Deception: A Stackelberg Game Approach

This paper explores coordinated deception strategies by synchronizing defenses across coupled cyber and physical systems to mislead attackers and strengthen defense mechanisms. We introduce a Stackelberg game framework to model the strategic interaction between defenders and attackers, where the defender leverages CVSS-based exploit probabilities and real-world vulnerability data from the National Vulnerability Database (NVD) to guide the deployment of deception. Cyber and physical replicas are used to disrupt attacker reconnaissance and enhance defensive effectiveness. We propose a CVE-based utility function to identify the most critical vulnerabilities and demonstrate that coordinated multilayer deception outperforms single-layer and baseline strategies in improving defender utility across both CVSS versions.

cs.CR↗

arXiv2025

MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection and Classification

Out of distribution (OOD) detection remains a critical challenge in malware classification due to the substantial intra family variability introduced by polymorphic and metamorphic malware variants. Most existing deep learning based malware detectors rely on closed world assumptions and fail to adequately model this intra class variation, resulting in degraded performance when confronted with previously unseen malware families. This paper presents MADOOD, a novel two stage, cluster driven deep learning framework for robust OOD malware detection and classification. In the first stage, malware family embeddings are modeled using class conditional spherical decision boundaries derived from Gaussian Discriminant Analysis (GDA), enabling statistically grounded separation of indistribution and OOD samples without requiring OOD data during training. Z score based distance analysis across multiple class centroids is employed to reliably identify anomalous samples in the latent space. In the second stage, a deep neural network integrates cluster based predictions, refined embeddings, and supervised classifier outputs to enhance final classification accuracy. Extensive evaluations on benchmark malware datasets comprising 25 known families and multiple novel OOD variants demonstrate that MADOOD significantly outperforms state of the art OOD detection methods, achieving an AUC of up to 0.911 on unseen malware families. The proposed framework provides a scalable, interpretable, and statistically principled solution for real world malware detection and anomaly identification in evolving cybersecurity environments.

cs.CR↗

arXiv2024

An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their vulnerabilities and future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their corresponding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model

cs.CR↗

arXiv2024

Fine-tuning Large Language Models for DGA and DNS Exfiltration Detection

Domain Generation Algorithms (DGAs) are malicious techniques used by malware to dynamically generate seemingly random domain names for communication with Command & Control (C&C) servers. Due to the fast and simple generation of DGA domains, detection methods must be highly efficient and precise to be effective. Large Language Models (LLMs) have demonstrated their proficiency in real-time detection tasks, making them ideal candidates for detecting DGAs. Our work validates the effectiveness of fine-tuned LLMs for detecting DGAs and DNS exfiltration attacks. We developed LLM models and conducted comprehensive evaluation using a diverse dataset comprising 59 distinct real-world DGA malware families and normal domain data. Our LLM model significantly outperformed traditional natural language processing techniques, especially in detecting unknown DGAs. We also evaluated its performance on DNS exfiltration datasets, demonstrating its effectiveness in enhancing cybersecurity measures. To the best of our knowledge, this is the first work that empirically applies LLMs for DGA and DNS exfiltration detection.

cs.CR↗

arXiv2024

An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey

In this research, we analyzed the suitability of each of the current state-of-the-art machine learning models for various cyberattack detection from the past 5 years with a major emphasis on the most recent works for comparative study to identify the knowledge gap where work is still needed to be done with regard to detection of each category of cyberattack. We also reviewed the suitability, effeciency and limitations of recent research on state-of-the-art classifiers and novel frameworks in the detection of differnet cyberattacks. Our result shows the need for; further research and exploration on machine learning approach for the detection of drive-by download attacks, an investigation into the mix performance of Naive Bayes to identify possible research direction on improvement to existing state-of-the-art Naive Bayes classifier, we also identify that current machine learning approach to the detection of SQLi attack cannot detect an already compromised database with SQLi attack signifying another possible future research direction.

cs.CR↗

arXiv2024

Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework

The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection.

cs.CR↗

arXiv2023

Generation of Games for Opponent Model Differentiation

Protecting against adversarial attacks is a common multiagent problem. Attackers in the real world are predominantly human actors, and the protection methods often incorporate opponent models to improve the performance when facing humans. Previous results show that modeling human behavior can significantly improve the performance of the algorithms. However, modeling humans correctly is a complex problem, and the models are often simplified and assume humans make mistakes according to some distribution or train parameters for the whole population from which they sample. In this work, we use data gathered by psychologists who identified personality types that increase the likelihood of performing malicious acts. However, in the previous work, the tests on a handmade game could not show strategic differences between the models. We created a novel model that links its parameters to psychological traits. We optimized over parametrized games and created games in which the differences are profound. Our work can help with automatic game generation when we need a game in which some models will behave differently and to identify situations in which the models do not align.

cs.AI↗

arXiv2023

Honeypot Allocation for Cyber Deception in Dynamic Tactical Networks: A Game Theoretic Approach

Honeypots play a crucial role in implementing various cyber deception techniques as they possess the capability to divert attackers away from valuable assets. Careful strategic placement of honeypots in networks should consider not only network aspects but also attackers' preferences. The allocation of honeypots in tactical networks under network mobility is of great interest. To achieve this objective, we present a game-theoretic approach that generates optimal honeypot allocation strategies within an attack/defense scenario. Our proposed approach takes into consideration the changes in network connectivity. In particular, we introduce a two-player dynamic game model that explicitly incorporates the future state evolution resulting from changes in network connectivity. The defender's objective is twofold: to maximize the likelihood of the attacker hitting a honeypot and to minimize the cost associated with deception and reconfiguration due to changes in network topology. We present an iterative algorithm to find Nash equilibrium strategies and analyze the scalability of the algorithm. Finally, we validate our approach and present numerical results based on simulations, demonstrating that our game model successfully enhances network security. Additionally, we have proposed additional enhancements to improve the scalability of the proposed approach.

cs.GT↗

arXiv2023

Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection

Bayesian classifiers perform well when each of the features is completely independent of the other which is not always valid in real world application. The aim of this study is to implement and compare the performances of each variant of Bayesian classifier (Multinomial, Bernoulli, and Gaussian) on anomaly detection in network intrusion, and to investigate whether there is any association between each variant assumption and their performance. Our investigation showed that each variant of Bayesian algorithm blindly follows its assumption regardless of feature property, and that the assumption is the single most important factor that influences their accuracy. Experimental results show that Bernoulli has accuracy of 69.9% test (71% train), Multinomial has accuracy of 31.2% test (31.2% train), while Gaussian has accuracy of 81.69% test (82.84% train). Going deeper, we investigated and found that each Naive Bayes variants performances and accuracy is largely due to each classifier assumption, Gaussian classifier performed best on anomaly detection due to its assumption that features follow normal distributions which are continuous, while multinomial classifier have a dismal performance as it simply assumes discreet and multinomial distribution.

cs.LG↗

arXiv2023

Cyber Deception against Zero-day Attacks: A Game Theoretic Approach

Reconnaissance activities precedent other attack steps in the cyber kill chain. Zero-day attacks exploit unknown vulnerabilities and give attackers the upper hand against conventional defenses. Honeypots have been used to deceive attackers by misrepresenting the true state of the network. Existing work on cyber deception does not model zero-day attacks. In this paper, we address the question of "How to allocate honeypots over the network?" to protect its most valuable assets. To this end, we develop a two-player zero-sum game theoretic approach to study the potential reconnaissance tracks and attack paths that attackers may use. However, zero-day attacks allow attackers to avoid placed honeypots by creating new attack paths. Therefore, we introduce a sensitivity analysis to investigate the impact of different zero-day vulnerabilities on the performance of the proposed deception technique. Next, we propose several mitigating strategies to defend the network against zero-day attacks based on this analysis. Finally, our numerical results validate our findings and illustrate the effectiveness of the proposed defense approach.

cs.GT↗

arXiv2023

Adversarial Reconnaissance Mitigation and Modeling

Adversarial reconnaissance is a crucial step in sophisticated cyber-attacks as it enables threat actors to find the weakest points of otherwise well-defended systems. To thwart reconnaissance, defenders can employ cyber deception techniques, such as deploying honeypots. In recent years, researchers have made great strides in developing game-theoretic models to find optimal deception strategies. However, most of these game-theoretic models build on relatively simple models of adversarial reconnaissance -- even though reconnaissance should be a focus point as the very purpose of deception is to thwart reconnaissance. In this paper, we first discuss effective cyber reconnaissance mitigation techniques including deception strategies and beyond. Then we provide a review of the literature on deception games from the perspective of modeling adversarial reconnaissance, highlighting key aspects of reconnaissance that have not been adequately captured in prior work. We then describe a probability-theory based model of the adversaries' belief formation and illustrate using numerical examples that this model can capture key aspects of adversarial reconnaissance. We believe that our review and belief model can serve as a stepping stone for developing more realistic and practical deception games.

cs.CR↗

arXiv2022

Survey and Taxonomy of Adversarial Reconnaissance Techniques

Adversaries are often able to penetrate networks and compromise systems by exploiting vulnerabilities in people and systems. The key to the success of these attacks is information that adversaries collect throughout the phases of the cyber kill chain. We summarize and analyze the methods, tactics, and tools that adversaries use to conduct reconnaissance activities throughout the attack process. First, we discuss what types of information adversaries seek, and how and when they can obtain this information. Then, we provide a taxonomy and detailed overview of adversarial reconnaissance techniques. The taxonomy introduces a categorization of reconnaissance techniques based on the source as third-party, human-, and system-based information gathering. This paper provides a comprehensive view of adversarial reconnaissance that can help in understanding and modeling this complex but vital aspect of cyber attacks as well as insights that can improve defensive strategies, such as cyber deception.

cs.CR↗

arXiv2020

Solving Zero-Sum One-Sided Partially Observable Stochastic Games

Many security and other real-world situations are dynamic in nature and can be modelled as strictly competitive (or zero-sum) dynamic games. In these domains, agents perform actions to affect the environment and receive observations -- possibly imperfect -- about the situation and the effects of the opponent's actions. Moreover, there is no limitation on the total number of actions an agent can perform -- that is, there is no fixed horizon. These settings can be modelled as partially observable stochastic games (POSGs). However, solving general POSGs is computationally intractable, so we focus on a broad subclass of POSGs called one-sided POSGs. In these games, only one agent has imperfect information while their opponent has full knowledge of the current situation. We provide a full picture for solving one-sided POSGs: we (1) give a theoretical analysis of one-sided POSGs and their value functions, (2) show that a variant of a value-iteration algorithm converges in this setting, (3) adapt the heuristic search value-iteration algorithm for solving one-sided POSGs, (4) describe how to use approximate value functions to derive strategies in the game, and (5) demonstrate that our algorithm can solve one-sided POSGs of non-trivial sizes and analyze the scalability of our algorithm in three different domains: pursuit-evasion, patrolling, and search games.

cs.GT↗

arXiv2020

Local Context Normalization: Revisiting Local Normalization

Normalization layers have been shown to improve convergence in deep neural networks, and even add useful inductive biases. In many vision applications the local spatial context of the features is important, but most common normalization schemes including Group Normalization (GN), Instance Normalization (IN), and Layer Normalization (LN) normalize over the entire spatial dimension of a feature. This can wash out important signals and degrade performance. For example, in applications that use satellite imagery, input images can be arbitrarily large; consequently, it is nonsensical to normalize over the entire area. Positional Normalization (PN), on the other hand, only normalizes over a single spatial position at a time. A natural compromise is to normalize features by local context, while also taking into account group level information. In this paper, we propose Local Context Normalization (LCN): a normalization layer where every feature is normalized based on a window around it and the filters in its group. We propose an algorithmic solution to make LCN efficient for arbitrary window sizes, even if every point in the image has a unique window. LCN outperforms its Batch Normalization (BN), GN, IN, and LN counterparts for object detection, semantic segmentation, and instance segmentation applications in several benchmark datasets, while keeping performance independent of the batch size and facilitating transfer learning.

cs.CV↗

arXiv2020

Optimizing Vulnerability-Driven Honey Traffic Using Game Theory

Enterprises are increasingly concerned about adversaries that slowly and deliberately exploit resources over the course of months or even years. A key step in this kill chain is network reconnaissance, which has historically been active (e.g., network scans) and therefore detectable. However, new networking technology increases the possibility of passive network reconnaissance, which will be largely undetectable by defenders. In this paper, we propose Snaz, a technique that uses deceptively crafted honey traffic to confound the knowledge gained through passive network reconnaissance. We present a two-player non-zero-sum Stackelberg game model that characterizes how a defender should deploy honey traffic in the presence of an adversary who is aware of Snaz. In doing so, we demonstrate the existence of optimal defender strategies that will either dissuade an adversary from acting on the existence of real vulnerabilities observed within network traffic, or reveal the adversary's presence when it attempts to unknowingly attack an intrusion detection node.

cs.CR↗

arXiv2019

Compact Representation of Value Function in Partially Observable Stochastic Games

Value methods for solving stochastic games with partial observability model the uncertainty about states of the game as a probability distribution over possible states. The dimension of this belief space is the number of states. For many practical problems, for example in security, there are exponentially many possible states which causes an insufficient scalability of algorithms for real-world problems. To this end, we propose an abstraction technique that addresses this issue of the curse of dimensionality by projecting high-dimensional beliefs to characteristic vectors of significantly lower dimension (e.g., marginal probabilities). Our two main contributions are (1) novel compact representation of the uncertainty in partially observable stochastic games and (2) novel algorithm based on this compact representation that is based on existing state-of-the-art algorithms for solving stochastic games with partial observability. Experimental evaluation confirms that the new algorithm over the compact representation dramatically increases the scalability compared to the state of the art.

cs.GT↗