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Advanced Persistent Threats (APTs) represent a complex method of attack aimed at specific targets.<n>I will investigate both the attack life cycle and cutting-edge detection and defense strategies proposed in recent academic research.<n>I aim to highlight the strengths and limitations of each approach and propose more adaptive APT mitigation strategies.
arXiv Detail & Related papers (2026-01-19T22:15:25Z)
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Researchers from the NSF-supported Secure Software Supply Chain Center (S3C2) and the Software Innovation Campus Paderborn (SICP) conducted a Vulnerability Response Summit.<n>The goal of the Summit is to enable sharing between industry practitioners having practical experiences and challenges with software supply chain security.
arXiv Detail & Related papers (2025-12-02T10:05:41Z)
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We study the risks of collective financial fraud in large-scale multi-agent systems powered by large language model (LLM) agents.<n>We present MultiAgentFraudBench, a large-scale benchmark for simulating financial fraud scenarios.
arXiv Detail & Related papers (2025-11-09T16:30:44Z)
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We identify user difficulties including information overload, technical comprehension, and balancing security awareness with comfort. Our findings reveal consistent communication paradoxes: users require technical details for credibility yet struggle with jargon and need risk awareness without experiencing anxiety. This work contributes to more effective security communication practices that enable users to recognize and respond to cybersecurity threats appropriately.
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This study outlines an approach to cybersecurity, including proactive threat anticipation, forensic investigations, and compliance with regulations like CCPA. Key threats such as social engineering, insider risks, phishing, and ransomware are examined, along with mitigation strategies leveraging AI and machine learning. The findings emphasize the importance of continuous monitoring, policy enforcement, and adaptive security measures to protect sensitive data.
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ABI Approach: Automatic Bias Identification in Decision-Making Under Risk based in an Ontology of Behavioral Economics [46.57327530703435]
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arXiv Detail & Related papers (2024-05-22T23:53:46Z)
QBER: Quantifying Cyber Risks for Strategic Decisions [0.0]
We introduce QBER approach to offer decision-makers measurable risk metrics. The QBER evaluates losses from cyberattacks, performs detailed risk analyses based on existing cybersecurity measures, and provides thorough cost assessments. Our contributions involve outlining cyberattack probabilities and risks, identifying Technical, Economic, and Legal (TEL) impacts, creating a model to gauge impacts, suggesting risk mitigation strategies, and examining trends and challenges in implementing widespread Cyber Risk Quantification (CRQ)
arXiv Detail & Related papers (2024-05-06T14:25:58Z)
QuantTM: Business-Centric Threat Quantification for Risk Management and Cyber Resilience [0.259990372084357]
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arXiv Detail & Related papers (2024-02-21T21:34:06Z)
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arXiv Detail & Related papers (2023-10-01T19:28:03Z)
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arXiv Detail & Related papers (2023-05-03T18:47:42Z)
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arXiv Detail & Related papers (2023-04-16T22:31:50Z)
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The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
arXiv Detail & Related papers (2023-04-02T08:43:03Z)
Probabilistic Categorical Adversarial Attack & Adversarial Training [45.458028977108256]
The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. How to generate adversarial examples with categorical data is an important problem but lack of extensive exploration. We propose Probabilistic Categorical Adversarial Attack (PCAA), which transfers the discrete optimization problem to a continuous problem that can be solved efficiently by Projected Gradient Descent.
arXiv Detail & Related papers (2022-10-17T19:04:16Z)
An Uncommon Task: Participatory Design in Legal AI [64.54460979588075]
We examine a notable yet understudied AI design process in the legal domain that took place over a decade ago. We show how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers.
arXiv Detail & Related papers (2022-03-08T15:46:52Z)
A framework for effective corporate communication after cyber security incidents [2.66512000865131]
Major cyber security incidents can represent a cyber crisis for an organisation, in particular because of the associated risk of reputational damage. This research seeks to tackle this problem through a critical, multi-faceted investigation into the efficacy of crisis communication and public relations following a data breach. It does so by drawing on academic literature, obtained through a systematic literature review, and real-world case studies. The validity of this framework is demonstrated by its evaluation through interviews with senior industry professionals, as well as a critical assessment against relevant practice and research.
arXiv Detail & Related papers (2020-09-19T11:08:53Z)
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation [34.08068963253976]
This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats.<n>After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders.
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