惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

C
Check Point Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
GbyAI
GbyAI
WordPress大学
WordPress大学
月光博客
月光博客
V
Visual Studio Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Google DeepMind News
Google DeepMind News
H
Help Net Security
MongoDB | Blog
MongoDB | Blog
P
Proofpoint News Feed
博客园 - 司徒正美
S
SegmentFault 最新的问题
Apple Machine Learning Research
Apple Machine Learning Research
Blog — PlanetScale
Blog — PlanetScale
B
Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Microsoft Azure Blog
Microsoft Azure Blog
V
V2EX
L
LangChain Blog
腾讯CDC
T
The Blog of Author Tim Ferriss
量子位

cs.CR updates on arXiv.org

Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
WedgeTail: An Intrusion Prevention System for the Data Pl...
Arash Shaghaghi, Mohamed Ali Kaafar, Sanjay Jha · 2017-08-18 · via cs.CR updates on arXiv.org

Networks are vulnerable to disruptions caused by malicious forwarding devices. The situation is likely to worsen in Software Defined Networks (SDNs) with the incompatibility of existing solutions, use of programmable soft switches and the potential of bringing down an entire network through compromised forwarding devices. In this paper, we present WedgeTail, an Intrusion Prevention System (IPS) designed to secure the SDN data plane. WedgeTail regards forwarding devices as points within a geometric space and stores the path packets take when traversing the network as trajectories. To be efficient, it prioritizes forwarding devices before inspection using an unsupervised trajectory-based sampling mechanism. For each of the forwarding device, WedgeTail computes the expected and actual trajectories of packets and `hunts' for any forwarding device not processing packets as expected. Compared to related work, WedgeTail is also capable of distinguishing between malicious actions such as packet drop and generation. Moreover, WedgeTail employs a radically different methodology that enables detecting threats autonomously. In fact, it has no reliance on pre-defined rules by an administrator and may be easily imported to protect SDN networks with different setups, forwarding devices, and controllers. We have evaluated WedgeTail in simulated environments, and it has been capable of detecting and responding to all implanted malicious forwarding devices within a reasonable time-frame. We report on the design, implementation, and evaluation of WedgeTail in this manuscript.