Predictive-CSM: Lightweight Fragment Security for 6LoWPAN IoT Networks

• URL: http://arxiv.org/abs/2506.01767v1
• Date: Mon, 02 Jun 2025 15:15:18 GMT
• Title: Predictive-CSM: Lightweight Fragment Security for 6LoWPAN IoT Networks
• Abstract summary: This work explores a defense strategy that takes a more adaptive, behavior-aware approach to this problem.<n>Our system, called Predictive-CSM, introduces a combination of two lightweight mechanisms.<n>We put this system to the test using a set of targeted attack simulations, including early fragment injection, replayed headers, and flooding with fake data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fragmentation is a routine part of communication in 6LoWPAN-based IoT networks, designed to accommodate small frame sizes on constrained wireless links. However, this process introduces a critical vulnerability fragments are typically stored and processed before their legitimacy is confirmed, allowing attackers to exploit this gap with minimal effort. In this work, we explore a defense strategy that takes a more adaptive, behavior-aware approach to this problem. Our system, called Predictive-CSM, introduces a combination of two lightweight mechanisms. The first tracks how each node behaves over time, rewarding consistent and successful interactions while quickly penalizing suspicious or failing patterns. The second checks the integrity of packet fragments using a chained hash, allowing incomplete or manipulated sequences to be caught early, before they can occupy memory or waste processing time. We put this system to the test using a set of targeted attack simulations, including early fragment injection, replayed headers, and flooding with fake data. Across all scenarios, Predictive CSM preserved network delivery and maintained energy efficiency, even under pressure. Rather than relying on heavyweight cryptography or rigid filters, this approach allows constrained de vices to adapt their defenses in real time based on what they observe, not just what they're told. In that way, it offers a step forward for securing fragmented communication in real world IoT systems

Related papers

• SafeCA: Safe Cross-Attention Localization and Regulation for Text-to-Video Jailbreak Defense [77.70598852234149]
  Text-to-Video (T2V) generative models are vulnerable to jailbreak attacks in real-world deployment.<n>We propose SafeCA, a feature-level defense mechanism for safe cross-attention localization and regularization.<n> Experimental results show that SafeCA reduces the jailbreak success rate by about 20% on mainstream T2V models.
  arXiv  Detail & Related papers  (2026-08-11T14:01:24Z)
• Agent-Assisted Side-Channel Attacks on Non-Prefix KV Cache in RAG [17.045113249152234]
  We introduce the first end-to-end side-channel attack targeting non- KV cache fusion.<n>SpliceLeak executes a systematic two-phase privacy breach.<n>SpliceDefense effectively flattens the side-channel signal (Delta TTFT 0) with negligible throughput overhead.
  arXiv  Detail & Related papers  (2026-06-20T02:28:28Z)
• MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents [74.64265314956441]
  We propose a unified black-box attack framework that poisons graph-structured external memory with coordinated text-image evidence.<n>MemVenom achieves strong end-to-end attack success with minimal impact on benign performance, reaching up to 99.15% on GPT-5-family web agents.
  arXiv  Detail & Related papers  (2026-06-09T11:53:25Z)
• TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic via Asymmetric Contrastive Learning [60.68349524623048]
  Decompositional jailbreaks pose a critical threat to large language models.<n>We present TwinGate, a stateful dual-encoder defense framework.<n>We construct a comprehensive dataset of over 3.62 million instructions spanning 8,600 distinct malicious intents.
  arXiv  Detail & Related papers  (2026-04-30T13:44:01Z)
• ShieldNet: Network-Level Guardrails against Emerging Supply-Chain Injections in Agentic Systems [56.613157564882925]
  Malicious behaviors are embedded in seemingly benign tools, silently hijacking agent execution, leaking sensitive data, or triggering unauthorized actions.<n>Despite their growing impact, there is currently no comprehensive benchmark for evaluating such threats.<n>We propose ShieldNet, a network-level guardrail framework that detects supply-chain poisoning by observing real network interactions.
  arXiv  Detail & Related papers  (2026-04-06T05:15:00Z)
• Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks [10.849171172103198]
  Adversarial examples can represent a serious threat to machine learning (ML) algorithms.<n>In this work, we aim to mitigate such risks by increasing the robustness of Network Intrusion Detection Systems.
  arXiv  Detail & Related papers  (2026-03-11T04:53:48Z)
• RedVisor: Reasoning-Aware Prompt Injection Defense via Zero-Copy KV Cache Reuse [47.85771791033142]
  We propose RedVisor, a framework that synthesizes the explainability of detection systems with the seamless integration of prevention strategies.<n>RedVisor is the first approach to leverage fine-grained reasoning paths to simultaneously detect attacks and guide the model's safe response.<n> Experiments demonstrate that RedVisor outperforms state-of-the-art defenses in detection accuracy and throughput while incurring negligible utility loss.
  arXiv  Detail & Related papers  (2026-02-02T08:26:51Z)
• Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs [26.288357188171265]
  MAMA (Multi-Agent Memory Attack) is a framework that measures how network structure shapes leakage.<n>We quantify leakage as the fraction of ground-truth PII recovered from attacking agent outputs.<n>Results provide the first systematic mapping from architectural choices to measurable privacy risk.
  arXiv  Detail & Related papers  (2025-12-04T11:00:49Z)
• Immunity memory-based jailbreak detection: multi-agent adaptive guard for large language models [12.772312329709868]
  Large language models (LLMs) have become foundational in AI systems, yet they remain vulnerable to adversarial jailbreak attacks.<n>We propose the Multi-Agent Adaptive Guard (MAAG) framework for jailbreak detection.<n>MAAG first extracts activation values from input prompts and compares them to historical activations stored in a memory bank for quick preliminary detection.
  arXiv  Detail & Related papers  (2025-12-03T01:40:40Z)
• The Trojan Knowledge: Bypassing Commercial LLM Guardrails via Harmless Prompt Weaving and Adaptive Tree Search [58.8834056209347]
  Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs.<n>We introduce the Correlated Knowledge Attack Agent (CKA-Agent), a dynamic framework that reframes jailbreaking as an adaptive, tree-structured exploration of the target model's knowledge base.
  arXiv  Detail & Related papers  (2025-12-01T07:05:23Z)
• Overlapping data in network protocols: bridging OS and NIDS reassembly gap [0.22499166814992436]
  A Network Intrusion Detection System (NIDS) that tries to reassemble a given flow data has to use the same reassembly policy as the monitored host.<n>We show that 1) OS reassembly policies evolve over time and 2) all the tested NIDSes are still vulnerable to overlap-based evasion and insertion attacks.
  arXiv  Detail & Related papers  (2025-04-30T13:15:50Z)
• A Flow is a Stream of Packets: A Stream-Structured Data Approach for DDoS Detection [32.22817720403158]
  We propose a new tree-based DDoS detection approach that operates on a flow as a stream structure. Our approach matches or exceeds existing machine learning techniques' accuracy, including state-of-the-art deep learning methods.
  arXiv  Detail & Related papers  (2024-05-12T09:29:59Z)
• Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability [61.549465258257115]
  We propose a novel and severer backdoor attack, TransTroj, which enables the backdoors embedded in PTMs to efficiently transfer in the model supply chain.<n> Experimental results show that our method significantly outperforms SOTA task-agnostic backdoor attacks.
  arXiv  Detail & Related papers  (2024-01-29T04:35:48Z)
• Carry Your Fault: A Fault Propagation Attack on Side-Channel Protected LWE-based KEM [12.164927192334748]
  We propose a new fault attack on side-channel secure masked implementation of LWE-based key-encapsulation mechanisms. We exploit the data dependency of the adder carry chain in A2B and extract sensitive information. We show key recovery attacks of Kyber, although the leakage also exists for other schemes like Saber.
  arXiv  Detail & Related papers  (2024-01-25T11:18:43Z)
• EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization [71.70414291057332]
  TTA may primarily be conducted on edge devices with limited memory. Long-term adaptation often leads to catastrophic forgetting and error accumulation. We present lightweight meta networks that can adapt the frozen original networks to the target domain.
  arXiv  Detail & Related papers  (2023-03-03T13:05:30Z)
• Intrusion Detection in Internet of Things using Convolutional Neural Networks [4.718295605140562]
  We propose a novel solution to the intrusion attacks against IoT devices using CNNs. The data is encoded as the convolutional operations to capture the patterns from the sensors data along time. The experimental results show significant improvement in both true positive rate and false positive rate compared to the baseline using LSTM.
  arXiv  Detail & Related papers  (2022-11-18T07:27:07Z)
• NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks [9.194664029847019]
  We show how to use Machine Learning for Network Intrusion Detection (NID) in a principled way. We propose NetSentry, perhaps the first of its kind NIDS that builds on Bi-ALSTM, an original ensemble of sequential neural models. We demonstrate F1 score gains above 33% over the state-of-the-art, as well as up to 3 times higher rates of detecting attacks such as XSS and web bruteforce.
  arXiv  Detail & Related papers  (2022-02-20T17:41:02Z)
• Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming [97.40955121478716]
  We propose a first-order dual SDP algorithm that requires memory only linear in the total number of network activations. We significantly improve L-inf verified robust accuracy from 1% to 88% and 6% to 40% respectively. We also demonstrate tight verification of a quadratic stability specification for the decoder of a variational autoencoder.
  arXiv  Detail & Related papers  (2020-10-22T12:32:29Z)
• Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
  adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes. We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks. These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
  arXiv  Detail & Related papers  (2020-08-17T07:16:57Z)
• FCOS: A simple and strong anchor-free object detector [111.87691210818194]
  We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free.
  arXiv  Detail & Related papers  (2020-06-14T01:03:39Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.