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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
A Novel Framework for DDoS Detectionin Huge Scale Network...
Hamed Rezaei, Nima Ghazanfari motlagha, Yaghoub Farjamib, Mohamm · 2018-01-08 · via cs.CR updates on arXiv.org

It is not been a long time since the advent of cloud-based technology. However, in this short period of timeseveral advantages and disadvantages have been emerged. This is a problem solving technology with some threats as well. These threats and potential damages are not only limited to the cloud-based technologies, but they have always been against computer network infrastructures. One of these examples is Distributed Denial-of-Service (DDoS) intrusion which is of course one of the most complex and the most dangerous types of attacks. The impact of this type of attack, due to its powerful nature, is much higher on cloud systems since in case of occurrence, the service providers lose their services completely as well as their reputationand loyal customers. This, apparently,can even lead to the collapse of the stock and other destructive consequences. On the other hand, due to the properties of cloud service providers including large-scale infrastructures, DDoS intrusion detection algorithms need high sensitivity, innovation, and general improvements. Traditional structures of DDoS attack detection algorithms are designed for small-scale networks or at most for application camps. Lack of efficient algorithm is seemingly apparentfor the large-scale networks. Therefore, in this context we utilize standardmethods as well as a proposed hybrid protocol which is more appropriate in connection with cloud structures in order to detect DDoS attacks