pwc-archive/papers-with-abstracts huggingface dataset:
AdaPhish: AI-Powered Adaptive Defense and Education Resource Against Deceptive Emails
Authors: Rei Meguro, Ng S. T. Chong
Date: 2025-02-05 00:00:00

Phishing attacks remain a significant threat in the digital age, yet organizations lack effective methods to tackle phishing attacks without leaking sensitive information. Phish bowl initiatives are a vital part of cybersecurity efforts against these attacks. However, traditional phish bowls require manual anonymization and are often limited to internal use. To overcome these limitations, we introduce AdaPhish, an AI-powered phish bowl platform that automatically anonymizes and analyzes phishing emails using large language models (LLMs) and vector databases. AdaPhish achieves real-time detection and adaptation to new phishing tactics while enabling long-term tracking of phishing trends. Through automated reporting, adaptive analysis, and real-time alerts, AdaPhish presents a scalable, collaborative solution for phishing detection and cybersecurity education.
A Novel Zero-Touch, Zero-Trust, AI/ML Enablement Framework for IoT Network Security
Authors: Sushil Shakya, Robert Abbas, Sasa Maric
Date: 2025-02-05 00:00:00

The IoT facilitates a connected, intelligent, and sustainable society; therefore, it is imperative to protect the IoT ecosystem. The IoT-based 5G and 6G will leverage the use of machine learning and artificial intelligence (ML/AI) more to pave the way for autonomous and collaborative secure IoT networks. Zero-touch, zero-trust IoT security with AI and machine learning (ML) enablement frameworks offers a powerful approach to securing the expanding landscape of Internet of Things (IoT) devices. This paper presents a novel framework based on the integration of Zero Trust, Zero Touch, and AI/ML powered for the detection, mitigation, and prevention of DDoS attacks in modern IoT ecosystems. The focus will be on the new integrated framework by establishing zero trust for all IoT traffic, fixed and mobile 5G/6G IoT network traffic, and data security (quarantine-zero touch and dynamic policy enforcement). We perform a comparative analysis of five machine learning models, namely, XGBoost, Random Forest, K-Nearest Neighbors, Stochastic Gradient Descent, and Native Bayes, by comparing these models based on accuracy, precision, recall, F1-score, and ROC-AUC. Results show that the best performance in detecting and mitigating different DDoS vectors comes from the ensemble-based approaches.
Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks
Authors: Sarah Al-Shareeda, Fusun Ozguner, Keith Redmill, Trung Q. Duong, Berk Canberk
Date: 2025-02-05 00:00:00
Tasks: Deep Reinforcement Learning

Task offloading management in 6G vehicular networks is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50% due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].
A Match Made in Heaven? Matching Test Cases and Vulnerabilities With the VUTECO Approach
Authors: Emanuele Iannone, Quang-Cuong Bui, Riccardo Scandariato
Date: 2025-02-05 00:00:00

Software vulnerabilities are commonly detected via static analysis, penetration testing, and fuzzing. They can also be found by running unit tests - so-called vulnerability-witnessing tests - that stimulate the security-sensitive behavior with crafted inputs. Developing such tests is difficult and time-consuming; thus, automated data-driven approaches could help developers intercept vulnerabilities earlier. However, training and validating such approaches require a lot of data, which is currently scarce. This paper introduces VUTECO, a deep learning-based approach for collecting instances of vulnerability-witnessing tests from Java repositories. VUTECO carries out two tasks: (1) the "Finding" task to determine whether a test case is security-related, and (2) the "Matching" task to relate a test case to the exact vulnerability it is witnessing. VUTECO successfully addresses the Finding task, achieving perfect precision and 0.83 F0.5 score on validated test cases in VUL4J and returning 102 out of 145 (70%) correct security-related test cases from 244 open-source Java projects. Despite showing sufficiently good performance for the Matching task - i.e., 0.86 precision and 0.68 F0.5 score - VUTECO failed to retrieve any valid match in the wild. Nevertheless, we observed that in almost all of the matches, the test case was still security-related despite being matched to the wrong vulnerability. In the end, VUTECO can help find vulnerability-witnessing tests, though the matching with the right vulnerability is yet to be solved; the findings obtained lay the stepping stone for future research on the matter.
Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security Research
Authors: Othmane Belarbi, Theodoros Spyridopoulos, Eirini Anthi, Omer Rana, Pietro Carnelli, Aftab Khan
Date: 2025-02-05 00:00:00
Tasks: Intrusion Detection

In this paper, a dataset of IoT network traffic is presented. Our dataset was generated by utilising the Gotham testbed, an emulated large-scale Internet of Things (IoT) network designed to provide a realistic and heterogeneous environment for network security research. The testbed includes 78 emulated IoT devices operating on various protocols, including MQTT, CoAP, and RTSP. Network traffic was captured in Packet Capture (PCAP) format using tcpdump, and both benign and malicious traffic were recorded. Malicious traffic was generated through scripted attacks, covering a variety of attack types, such as Denial of Service (DoS), Telnet Brute Force, Network Scanning, CoAP Amplification, and various stages of Command and Control (C&C) communication. The data were subsequently processed in Python for feature extraction using the Tshark tool, and the resulting data was converted to Comma Separated Values (CSV) format and labelled. The data repository includes the raw network traffic in PCAP format and the processed labelled data in CSV format. Our dataset was collected in a distributed manner, where network traffic was captured separately for each IoT device at the interface between the IoT gateway and the device. Our dataset was collected in a distributed manner, where network traffic was separately captured for each IoT device at the interface between the IoT gateway and the device. With its diverse traffic patterns and attack scenarios, this dataset provides a valuable resource for developing Intrusion Detection Systems and security mechanisms tailored to complex, large-scale IoT environments. The dataset is publicly available at Zenodo.
Understanding and Enhancing the Transferability of Jailbreaking Attacks
Authors: Runqi Lin, Bo Han, Fengwang Li, Tongling Liu
Date: 2025-02-05 00:00:00
Tasks: Intent Recognition, Red Teaming

Jailbreaking attacks can effectively manipulate open-source large language models (LLMs) to produce harmful responses. However, these attacks exhibit limited transferability, failing to disrupt proprietary LLMs consistently. To reliably identify vulnerabilities in proprietary LLMs, this work investigates the transferability of jailbreaking attacks by analysing their impact on the model's intent perception. By incorporating adversarial sequences, these attacks can redirect the source LLM's focus away from malicious-intent tokens in the original input, thereby obstructing the model's intent recognition and eliciting harmful responses. Nevertheless, these adversarial sequences fail to mislead the target LLM's intent perception, allowing the target LLM to refocus on malicious-intent tokens and abstain from responding. Our analysis further reveals the inherent distributional dependency within the generated adversarial sequences, whose effectiveness stems from overfitting the source LLM's parameters, resulting in limited transferability to target LLMs. To this end, we propose the Perceived-importance Flatten (PiF) method, which uniformly disperses the model's focus across neutral-intent tokens in the original input, thus obscuring malicious-intent tokens without relying on overfitted adversarial sequences. Extensive experiments demonstrate that PiF provides an effective and efficient red-teaming evaluation for proprietary LLMs.
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
Authors: Sunwoo Lee, Jaebak Hwang, Yonghyeon Jo, Seungyul Han
Date: 2025-02-05 00:00:00
Tasks: Adversarial Attack, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Traditional robust methods in multi-agent reinforcement learning (MARL) often struggle against coordinated adversarial attacks in cooperative scenarios. To address this limitation, we propose the Wolfpack Adversarial Attack framework, inspired by wolf hunting strategies, which targets an initial agent and its assisting agents to disrupt cooperation. Additionally, we introduce the Wolfpack-Adversarial Learning for MARL (WALL) framework, which trains robust MARL policies to defend against the proposed Wolfpack attack by fostering systemwide collaboration. Experimental results underscore the devastating impact of the Wolfpack attack and the significant robustness improvements achieved by WALL. Our code is available at https://github.com/sunwoolee0504/WALL.
SimMark: A Robust Sentence-Level Similarity-Based Watermarking Algorithm for Large Language Models
Authors: Amirhossein Dabiriaghdam, Lele Wang
Date: 2025-02-05 00:00:00
Tasks: Sentence, Sentence Embeddings

The rapid proliferation of large language models (LLMs) has created an urgent need for reliable methods to detect whether a text is generated by such models. In this paper, we propose SimMark, a posthoc watermarking algorithm that makes LLMs' outputs traceable without requiring access to the model's internal logits, enabling compatibility with a wide range of LLMs, including API-only models. By leveraging the similarity of semantic sentence embeddings and rejection sampling to impose detectable statistical patterns imperceptible to humans, and employing a soft counting mechanism, SimMark achieves robustness against paraphrasing attacks. Experimental results demonstrate that SimMark sets a new benchmark for robust watermarking of LLM-generated content, surpassing prior sentence-level watermarking techniques in robustness, sampling efficiency, and applicability across diverse domains, all while preserving the text quality.
MARAGE: Transferable Multi-Model Adversarial Attack for Retrieval-Augmented Generation Data Extraction
Authors: Xiao Hu, Eric Liu, Weizhou Wang, Xiangyu Guo, David Lie
Date: 2025-02-05 00:00:00
Tasks: Adversarial Attack, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-Augmented Generation (RAG) offers a solution to mitigate hallucinations in Large Language Models (LLMs) by grounding their outputs to knowledge retrieved from external sources. The use of private resources and data in constructing these external data stores can expose them to risks of extraction attacks, in which attackers attempt to steal data from these private databases. Existing RAG extraction attacks often rely on manually crafted prompts, which limit their effectiveness. In this paper, we introduce a framework called MARAGE for optimizing an adversarial string that, when appended to user queries submitted to a target RAG system, causes outputs containing the retrieved RAG data verbatim. MARAGE leverages a continuous optimization scheme that integrates gradients from multiple models with different architectures simultaneously to enhance the transferability of the optimized string to unseen models. Additionally, we propose a strategy that emphasizes the initial tokens in the target RAG data, further improving the attack's generalizability. Evaluations show that MARAGE consistently outperforms both manual and optimization-based baselines across multiple LLMs and RAG datasets, while maintaining robust transferability to previously unseen models. Moreover, we conduct probing tasks to shed light on the reasons why MARAGE is more effective compared to the baselines and to analyze the impact of our approach on the model's internal state.
PatchPilot: A Cost-Efficient Software Engineering Agent with Early Attempts on Formal Verification
Authors: Hongwei Li, Yuheng Tang, Shiqi Wang, Wenbo Guo
Date: 2025-02-04 00:00:00

Recent research builds various patching agents that combine large language models (LLMs) with non-ML tools and achieve promising results on the state-of-the-art (SOTA) software patching benchmark, SWE-bench. Based on how to determine the patching workflows, existing patching agents can be categorized as agent-based planning methods, which rely on LLMs for planning, and rule-based planning methods, which follow a pre-defined workflow. At a high level, agent-based planning methods achieve high patching performance but with a high cost and limited stability. Rule-based planning methods, on the other hand, are more stable and efficient but have key workflow limitations that compromise their patching performance. In this paper, we propose PatchPilot, an agentic patcher that strikes a balance between patching efficacy, stability, and cost-efficiency. PatchPilot proposes a novel rule-based planning workflow with five components: reproduction, localization, generation, validation, and refinement (where refinement is unique to PatchPilot). We introduce novel and customized designs to each component to optimize their effectiveness and efficiency. Through extensive experiments on the SWE-bench benchmarks, PatchPilot shows a superior performance than existing open-source methods while maintaining low cost (less than 1$ per instance) and ensuring higher stability. We also conduct a detailed ablation study to validate the key designs in each component. Our code is available at https://github.com/ucsb-mlsec/PatchPilot.
Blind Visible Watermark Removal with Morphological Dilation
Authors: Preston K. Robinette, Taylor T. Johnson
Date: 2025-02-04 00:00:00
Tasks: Image Restoration

Visible watermarks pose significant challenges for image restoration techniques, especially when the target background is unknown. Toward this end, we present MorphoMod, a novel method for automated visible watermark removal that operates in a blind setting -- without requiring target images. Unlike existing methods, MorphoMod effectively removes opaque and transparent watermarks while preserving semantic content, making it well-suited for real-world applications. Evaluations on benchmark datasets, including the Colored Large-scale Watermark Dataset (CLWD), LOGO-series, and the newly introduced Alpha1 datasets, demonstrate that MorphoMod achieves up to a 50.8% improvement in watermark removal effectiveness compared to state-of-the-art methods. Ablation studies highlight the impact of prompts used for inpainting, pre-removal filling strategies, and inpainting model performance on watermark removal. Additionally, a case study on steganographic disorientation reveals broader applications for watermark removal in disrupting high-level hidden messages. MorphoMod offers a robust, adaptable solution for watermark removal and opens avenues for further advancements in image restoration and adversarial manipulation.
An Annotated Reading of 'The Singer of Tales' in the LLM Era
Authors: Kush R. Varshney
Date: 2025-02-07 00:00:00

The Parry-Lord oral-formulaic theory was a breakthrough in understanding how oral narrative poetry is learned, composed, and transmitted by illiterate bards. In this paper, we provide an annotated reading of the mechanism underlying this theory from the lens of large language models (LLMs) and generative artificial intelligence (AI). We point out the the similarities and differences between oral composition and LLM generation, and comment on the implications to society and AI policy.
ApplE: An Applied Ethics Ontology with Event Context
Authors: Aisha Aijaz, Raghava Mutharaju, Manohar Kumar
Date: 2025-02-07 00:00:00
Tasks: Ethics

Applied ethics is ubiquitous in most domains, requiring much deliberation due to its philosophical nature. Varying views often lead to conflicting courses of action where ethical dilemmas become challenging to resolve. Although many factors contribute to such a decision, the major driving forces can be discretized and thus simplified to provide an indicative answer. Knowledge representation and reasoning offer a way to explicitly translate abstract ethical concepts into applicable principles within the context of an event. To achieve this, we propose ApplE, an Applied Ethics ontology that captures philosophical theory and event context to holistically describe the morality of an action. The development process adheres to a modified version of the Simplified Agile Methodology for Ontology Development (SAMOD) and utilizes standard design and publication practices. Using ApplE, we model a use case from the bioethics domain that demonstrates our ontology's social and scientific value. Apart from the ontological reasoning and quality checks, ApplE is also evaluated using the three-fold testing process of SAMOD. ApplE follows FAIR principles and aims to be a viable resource for applied ethicists and ontology engineers.
Digital Gatekeeping: An Audit of Search Engine Results shows tailoring of queries on the Israel-Palestine Conflict
Authors: Íris Damião, José M. Reis, Paulo Almeida, Nuno Santos, Joana Gonçalves-Sá
Date: 2025-02-06 00:00:00

Search engines, often viewed as reliable gateways to information, tailor search results using customization algorithms based on user preferences, location, and more. While this can be useful for routine queries, it raises concerns when the topics are sensitive or contentious, possibly limiting exposure to diverse viewpoints and increasing polarization. To examine the extent of this tailoring, we focused on the Israel-Palestine conflict and developed a privacy-protecting tool to audit the behavior of three search engines: DuckDuckGo, Google and Yahoo. Our study focused on two main questions: (1) How do search results for the same query about the conflict vary among different users? and (2) Are these results influenced by the user's location and browsing history? Our findings revealed significant customization based on location and browsing preferences, unlike previous studies that found only mild personalization for general topics. Moreover, queries related to the conflict were more customized than unrelated queries, and the results were not neutral concerning the conflict's portrayal.
The "negative end" of change in grammar: terminology, concepts and causes
Authors: Karolina Rudnicka
Date: 2025-02-07 00:00:00

The topic of "negative end" of change is, contrary to the fields of innovation and emergence, largely under-researched. Yet, it has lately started to gain an increasing attention from language scholars worldwide. The main focus of this article is threefold, namely to discuss the i) terminology; ii) concepts and iii) causes associated with the "negative end" of change in grammar. The article starts with an overview of research conducted on the topic. It then moves to situating phenomena referred to as loss, decline or obsolescence among processes of language change, before elaborating on the terminology and concepts behind it. The last part looks at possible causes for constructions to display a (gradual or rapid, but very consistent) decrease in the frequency of use over time, which continues until the construction disappears or there are only residual or fossilised forms left. Keywords: loss, obsolescence, decline, competition, higher
"In order that" -- a data driven study of symptoms and causes of obsolescence
Authors: Karolina Rudnicka
Date: 2025-02-06 00:00:00

The paper is an empirical case study of grammatical obsolescence in progress. The main studied variable is the purpose subordinator in order that, which is shown to be steadily decreasing in the frequency of use starting from the beginning of the twentieth century. This work applies a data-driven approach for the investigation and description of obsolescence, recently developed by the Rudnicka (2019). The methodology combines philological analysis with statistical methods used on data acquired from mega-corpora. Moving from the description of possible symptoms of obsolescence to different causes for it, the paper aims at presenting a comprehensive account of the studied phenomenon. Interestingly, a very significant role in the decline of in order that can be ascribed to the so-called higher-order processes, understood as processes influencing the constructional level from above. Two kinds of higher-order processes are shown to play an important role, namely i) an externally-motivated higher-order process exemplified by the drastic socio-cultural changes of the 19th and 20th centuries; ii) an internally-motivated higher-order processes instantiated by the rise of the to-infinitive (rise of infinite clauses).
Can Grammarly and ChatGPT accelerate language change? AI-powered technologies and their impact on the English language: wordiness vs. conciseness
Authors: Karolina Rudnicka
Date: 2025-02-06 00:00:00
Tasks: Text Generation

The proliferation of NLP-powered language technologies, AI-based natural language generation models, and English as a mainstream means of communication among both native and non-native speakers make the output of AI-powered tools especially intriguing to linguists. This paper investigates how Grammarly and ChatGPT affect the English language regarding wordiness vs. conciseness. A case study focusing on the purpose subordinator in order to is presented to illustrate the way in which Grammarly and ChatGPT recommend shorter grammatical structures instead of longer and more elaborate ones. Although the analysed sentences were produced by native speakers, are perfectly correct, and were extracted from a language corpus of contemporary English, both Grammarly and ChatGPT suggest more conciseness and less verbosity, even for relatively short sentences. The present article argues that technologies such as Grammarly not only mirror language change but also have the potential to facilitate or accelerate it.