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On-Device Interpretable Tsetlin Machine-Based Intrusion Detection for Secure IoMT LivePI: More Realistic Benchmarking of Agents Against Indirect Prompt Injection Backdoor Channels Hidden in Latent Space: Cryptographic Undetectability in Modern Neural Networks Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety Red-Teaming Agent Execution Contexts: Open-World Security Evaluation on OpenClaw Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents From Specification to Deployment: Empirical Evidence from a W3C VC + DID Trust Infrastructure for Autonomous Agents When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models 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 StegoStylo: Squelching Stylometric Scrutiny through Steganographic Stitching CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks Feedback Lunch: Learned Feedback Codes for Secure Communications Noise Aggregation Analysis Driven by Small-Noise Injection: Efficient Membership Inference for Diffusion Models A First Look at the Security Issues in the Model Context Protocol Ecosystem Formalizing the Safety, Security, and Functional Properties of Agentic AI Systems MEASER: Malware embedding attacks on open-source LLMs ADMIT: Few-shot Knowledge Poisoning Attacks on RAG-based Fact Checking Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Models A2AS: Agentic AI Runtime Security and Self-Defense Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG) From surveillance to signalling: escalation channels as environmental controls for agentic AI Quantitative Certification of Agentic Tool Selection STAC: When Innocent Tools Form Dangerous Chains to Jailbreak LLM Agents Fingerprinting LLMs via Prompt Injection Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids Guidance Watermarking for Diffusion Models SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios xOffense: An Autonomous Multi-Agent Framework for Penetration Testing with Domain-Adapted Large Language Models Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities Tell-Tale Watermarks for Explanatory Reasoning in Synthetic Media Forensics Between a Rock and a Hard Place: The Tension Between Ethical Reasoning and Safety Alignment in LLMs A Comprehensive Guide to Differential Privacy: From Theory to User Expectations Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution Searching for Privacy Risks in LLM Agents via Simulation Exact Verification of Graph Neural Networks with Incremental Constraint Solving SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstruction Majority Bit-Aware Watermarking For Large Language Models Coward: Collision-based OOD Watermarking for Practical Proactive Federated Backdoor Detection Prompt to Pwn: Automated Exploit Generation for Smart Contracts Activation-Guided Local Editing for Jailbreaking Attacks Random Walk Learning and the Pac-Man Attack ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation White-Basilisk: A Hybrid Model for Code Vulnerability Detection Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model! 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AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents DiffMI: Breaking Face Recognition Privacy via Diffusion-Driven Training-Free Model Inversion Chronology of Multi-Agent Interactions for Provenance of Evolving Information Progent: Securing AI Agents with Privilege Control Gungnir: Exploiting Stylistic Features in Images for Backdoor Attacks on Diffusion Models DeePen: Penetration Testing for Audio Deepfake Detection Detecting Malicious Concepts without Image Generation in AI-Generated Content (AIGC) How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies Imitation Game for Adversarial Disillusion with Chain-of-Thought Reasoning in Generative AI PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis Red-Teaming Text-to-Image Models via In-Context Experience Replay and Semantic-Preserving Prompt Rewriting DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning Privacy Leakage via Output Label Space and Differentially Private Continual Learning ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs Faster-GCG: Efficient Discrete Optimization Jailbreak Attacks against Aligned Large Language Models CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment Power-Softmax: Towards Secure LLM Inference over Encrypted Data FlipAttack: Jailbreak LLMs via Flipping Hypnopaedia-Aware Machine Unlearning via Psychometrics of Artificial Mental Imagery Anomaly Detection from a Tensor Train Perspective Survival of the Cheapest: Cost-Aware Hardware Adaptation for Adversarial Robustness Convergent Differential Privacy Analysis for General Federated Learning Improving Clean Accuracy via a Tangent-Space Perspective on Adversarial Training The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence Towards Agentic Runtime Healing Verification of Machine Unlearning is Fragile Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning Whispers in the Machine: Confidentiality in Agentic Systems MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Towards Adaptive, Learning-Based Security in Decentralized Applications Approximate and Weighted Data Reconstruction Attack in Federated Learning Can Blockchains Reliably Train Machine Learning Models? Vendor-Conditioned Contrastive Learning for Predicting Organizational Cyber Threat Targets
A Lightweight and Scalable Physical Layer Attack Detection Mechanism for the Internet of Things (IoT) Using Hybrid Security Schema
Reza Fotohi, Hossein Pakdel · 2021-03-31 · via cs.CR updates on arXiv.org

The Internet of Things, also known as the IoT, refers to the billions of devices around the world that are now connected to the Internet, collecting and sharing data. The amount of data collected through IoT sensors must be completely securely controlled. To protect the information collected by IoT sensors, a lightweight method called Discover the Flooding Attack-RPL (DFA-RPL) has been proposed. The proposed DFA-RPL method identifies intrusive nodes in several steps to exclude them from continuing routing operations. Thus, in the DFA-RPL method, it first builds a cluster and selects the most appropriate node as a cluster head in DODAG, then, due to the vulnerability of the RPL protocol to Flooding attacks, it uses an ant colony algorithm (ACO) using five steps to detect attacks. Use Flooding to prevent malicious activity on the IoT network. In other words, if it detects a node as malicious, it puts that node on the detention list and quarantines it for a certain period of time. The results obtained from the simulation show the superiority of the proposed method in terms of Packet Delivery Rate, Detection Rate, False Positive Rate, and False Negative Rate compared to IRAD and REATO methods.

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