LLM-Driven APT Detection for 6G Wireless Networks: a Systematic Review and Taxonomy

Sixth Generation 6G wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and...

Modeling Interdependent Privacy Threats

The rise of online social networks, user-gene-rated content, and third-party apps made data sharing an inevitable trend, driven by both user behavior and the commercial value of personal information. As service providers amass vast amounts of data, safeguarding individual privacy has become...

ACSE-Eval: Can LLMs Threat Model Real-World Cloud Infrastructure?

While Large Language Models have shown promise in cybersecurity applications, their effectiveness in identifying security threats within cloud deployments remains unexplored. This paper introduces AWS Cloud Security Engineering Eval, a novel dataset for evaluating LLMs cloud security threat...

Ghosted by a cybercriminal

Welcome to this week's edition of the Threat Source newsletter. Talos recently published research into how threat actors are increasingly teaming up across the attack chain. Each group handles a slice of the operation, passing the breach along like a relay baton. It's a concerning trend -- one th...

SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning

Large Reasoning Models LRMs introduce a new generation paradigm of explicitly reasoning before answering, leading to remarkable improvements in complex tasks. However, they pose great safety risks against harmful queries and adversarial attacks. While recent mainstream safety efforts on LRMs,...

VulCPE: Context-Aware Cybersecurity Vulnerability Retrieval and Management

The dynamic landscape of cybersecurity demands precise and scalable solutions for vulnerability management in heterogeneous systems, where configuration-specific vulnerabilities are often misidentified due to inconsistent data in databases like the National Vulnerability Database NVD. Inaccurate...

Redefining IABs: Impacts of compartmentalization on threat tracking and modeling

Cisco Talos has observed a growing trend of attack kill chains being split into two stages -- initial compromise and subsequent exploitation -- executed by separate threat actors. This compartmentalization increases the complexity and difficulty of performing threat modeling and actor profiling...

Securing WiFi Fingerprint-Based Indoor Localization Systems from Malicious Access Points

WiFi fingerprint-based indoor localization schemes deliver highly accurate location data by matching the received signal strength indicator RSSI with an offline database using machine learning ML or deep learning DL models. However, over time, RSSI values degrade due to the malicious behavior of...

Optimizing Mouse Dynamics for User Authentication by Machine Learning: Addressing Data Sufficiency, Accuracy-Practicality Trade-Off, and Model Performance Challenges

User authentication is essential to ensure secure access to computer systems, yet traditional methods face limitations in usability, cost, and security. Mouse dynamics authentication, based on the analysis of users' natural interaction behaviors with mouse devices, offers a cost-effective,...

ThreatLens: LLM-Guided Threat Modeling and Test Plan Generation for Hardware Security Verification

Current hardware security verification processes predominantly rely on manual threat modeling and test plan generation, which are labor-intensive, error-prone, and struggle to scale with increasing design complexity and evolving attack methodologies. To address these challenges, we propose...

Privacy-Preserving Transformers: SwiftKey'S Differential Privacy Implementation

In this paper we train a transformer using differential privacy DP for language modeling in SwiftKey. We run multiple experiments to balance the trade-off between the model size, run-time speed and accuracy. We show that we get small and consistent gains in the next-word-prediction and accuracy...

LLMs' Suitability for Network Security: a Case Study of STRIDE Threat Modeling

Artificial Intelligence AI is expected to be an integral part of next-generation AI-native 6G networks. With the prevalence of AI, researchers have identified numerous use cases of AI in network security. However, there are almost nonexistent studies that analyze the suitability of Large Language...

Risk Assessment and Threat Modeling for Safe Autonomous Driving Technology

This research paper delves into the field of autonomous vehicle technology, examining the vulnerabilities inherent in each component of these transformative vehicles. Autonomous vehicles AVs are revolutionizing transportation by seamlessly integrating advanced functionalities such as sensing,...

Modeling Behavioral Preferences of Cyber Adversaries Using Inverse Reinforcement Learning

This paper presents a holistic approach to attacker preference modeling from system-level audit logs using inverse reinforcement learning IRL. Adversary modeling is an important capability in cybersecurity that lets defenders characterize behaviors of potential attackers, which enables attributio...

Securing the Future of IVR: AI-Driven Innovation with Agile Security, Data Regulation, and Ethical AI Integration

The rapid digitalization of communication systems has elevated Interactive Voice Response IVR technologies to become critical interfaces for customer engagement. With Artificial Intelligence AI now driving these platforms, ensuring secure, compliant, and ethically designed development practices i...

Enhancing the Cloud Security through Topic Modelling

Protecting cloud applications is crucial in an age where security constantly threatens the digital world. The inevitable cyber-attacks throughout the CI/CD pipeline make cloud security innovations necessary. This research is motivated by applying Natural Language Processing NLP methodologies, suc...

14 secure coding tips: Learn from the experts at Microsoft Build

Hey friends! If you are a developer, you know that writing clean and efficient code is just the starting point. Now, with AI playing a bigger role, secure coding isn't just a 'nice-to-have'—it's a must. Whether you're building web apps, working on cloud services, or adding AI to your projects,...

14 secure coding tips: Learn from the experts at Microsoft Build

Hey friends! If you are a developer, you know that writing clean and efficient code is just the starting point. Now, with AI playing a bigger role, secure coding isn't just a 'nice-to-have'—it's a must. Whether you're building web apps, working on cloud services, or adding AI to your projects,...

CISA: Roadmap to Innovation in the Dams Sector

The Roadmap to Innovation in the Dams Sector outlines Research and Development Focus Areas for the next 3-5 years to enhance the security and resilience of the sector and ensure that dams and related infrastructure can withstand current and emerging risks. The R+D Focus Areas identified in this...

ThreMoLIA: Threat Modeling of Large Language Model-Integrated Applications

Large Language Models LLMs are currently being integrated into industrial software applications to help users perform more complex tasks in less time. However, these LLM-Integrated Applications LIA expand the attack surface and introduce new kinds of threats. Threat modeling is commonly used to...