arXiv Machine Learning
Jun 19
Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids
arXiv:2606.
Benchmarking Cyberattack Detection in Electric Vehicle Charging Infrastructure with Benign User Updates
arXiv:2608. 11286v1 Announce Type: cross Abstract: Cyberattack detection in electric vehicle charging infrastructure is complicated by legitimate post-activation revisions to requested energy and departure time.
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arXiv Machine Learning
Aug 19
Digital Twin-Based Intrusion Detection for Vehicle Powertrain CAN Bus Systems
The paper presents a digital‑twin (DT) based intrusion detection system (IDS) for vehicle powertrain CAN bus traffic, modeling physical relationships among decoded signals to detect payload‑manipulation attacks that preserve normal timing and sequencing. Using a shared‑encoder LSTM trained on 17 Hyundai/Kia CAN signals, the DT flags anomalies when residuals exceed a threshold, achieving high detection rates (up to 94.6%) for stealthy attacks such as continuous drift and masquerade, while a range‑and‑plausibility baseline fails to detect them. The study demonstrates that learning coupled vehicle dynamics enables detection of payload‑level attacks that evade traditional timing‑based IDSs, though false positives remain a challenge.
arXiv Machine Learning
Sep 23
Learning Defensive Policies against Diverse Inference Attacks for Smart Meter Privacy
The paper introduces a black-box defense strategy for smart meter data that uses a proxy-guided hierarchical reinforcement learning framework to generate battery-based load-shaping policies. These policies inject realistic yet misleading appliance-level signatures into aggregate power signals, disrupting non-intrusive load monitoring attacks. Experiments on UK-DALE and REDD datasets show significant increases in appliance-level reconstruction error and reductions in attacker F1 scores across multiple unseen NILM models.
Hugging Face Trending Papers
Aug 17
Digital Twin-Based Intrusion Detection for Vehicle Powertrain CAN Bus Systems
Existing automotive intrusion detection systems (IDSs) for the Controller Area Network (CAN) largely target discrepancies in message timing, frequency, or sequencing and cannot detect attacks that pre...
arXiv AI
Jun 9
Model Multiplicity for Adversarial Detection in Small Language Model Training on Edge Devices
arXiv:2606. 07857v1 Announce Type: cross Abstract: The rise of edge-based machine learning has enabled distributed adaptation of language models across mobile and IoT devices, offering privacy preservation and real-time responsiveness.
arXiv Machine Learning
Jun 15
Dynamic Free-Rider Detection in Federated Learning via Simulated Attack Patterns
arXiv:2604. 04611v2 Announce Type: replace Abstract: Federated learning (FL) enables multiple clients to collaboratively train a global model by aggregating local updates without sharing private data.
Hugging Face Trending Papers
Jun 2
RogueMerge: Robust and Unified Attacks against LLM Model Merging
Model merging composes specialized capabilities into a single LLM by aggregating task vectors sourced from unverified public platforms, exposing a critical supply-chain attack surface: Because any malicious behavior can be encoded into a task vector, and merging grants third-party vectors direct write access to model weights, an attacker-provided task vector can enable or amplify diverse downstream threats. Prior work studies only backdoor attacks against model merging for classifiers using static arithmetic heuristics, which fail to effectively handle diverse attacks on generative LLMs for three reasons.
arXiv AI
Jun 3
Black-box, Adaptive, Efficient, Transferable, Harmful, Applicable... Attacks Are All You Need to Break LLMs
arXiv:2606. 03647v1 Announce Type: cross Abstract: Accurately evaluating adversarial robustness is a longstanding challenge.
arXiv AI
Jun 18
MIDS: Detecting Stealthy Masquerade and Tampering Attacks on CAN Bus via Bidirectional Mamba
arXiv:2606. 18599v1 Announce Type: cross Abstract: The Controller Area Network (CAN) protocol is the primary communication standard for Electronic Control Units (ECUs) in modern vehicles, but its lack of encryption and authentication exposes it to a range of security threats.
arXiv Machine Learning
Jun 3
RogueMerge: Robust and Unified Attacks against LLM Model Merging
arXiv:2606. 03344v1 Announce Type: cross Abstract: Model merging composes specialized capabilities into a single LLM by aggregating task vectors sourced from unverified public platforms, exposing a critical supply-chain attack surface: Because any malicious behavior can be encoded into a task vector, and merging grants third-party vectors direct write access to model weights, an attacker-provided task vector can enable or amplify diverse downstream threats.
Hugging Face Trending Papers
Sep 2
SPADE: SPaT Attack Detection from the Connected Vehicle's Perspective
SPADE is a labelled, multi‑modal, simulation‑based dataset for detecting attacks on Signal Phase and Timing (SPaT) messages from the perspective of connected vehicles. It contains 1.89 million timestep records generated by injecting six classes of application‑layer attacks and one benign class into the SAE J2735 SPaT protocol, across multiple intersection geometries, operating conditions, and random seeds. Each record fuses SPaT fields, onboard camera confidence scores, and cooperative V2V peer data into 40 features, enabling deep‑learning intrusion detection systems to distinguish deliberate attacks from environmental noise.
arXiv AI
Jul 14
A Low-Latency Fraud Detection Layer for Detecting Adversarial Interaction Patterns in LLM-Powered Agents
arXiv:2605. 01143v2 Announce Type: replace Abstract: Large Language Model (LLM)-powered agents demonstrate strong capabilities in autonomous task execution, tool use, and multi-step reasoning.