Bharat Bhargava

KMA Solaiman, Shafkat Islam, Ruy de Oliveira et al.

Edge AI systems increasingly rely on federated learning to train perception models in distributed, privacy-preserving, and resource-constrained environments. Yet, before training begins, practitioners often lack practical tools to estimate how difficult a federated learning task will be in terms of achievable accuracy and communication cost. This paper presents a classifier-agnostic, pre-deployment framework for estimating learning complexity in federated perception systems by jointly modeling intrinsic properties of the data and characteristics of the distributed environment. The proposed complexity metric integrates dataset attributes such as dimensionality, sparsity, and heterogeneity with factors related to the composition of participating clients. Using federated learning as a representative distributed training setting, we examine how learning difficulty varies across different federated configurations. Experiments on multiple variants of the MNIST dataset and CIFAR dataset show that the proposed metric strongly correlates with federated learning performance and the communication effort required to reach fixed accuracy targets. These findings suggest that complexity estimation can serve as a practical diagnostic tool for resource planning, dataset assessment, and feasibility evaluation in edge-deployed perception systems.

Feras Benchellal andLotfi Ben Othmane, Yasaswini Konapalli, Cihan Tunc et al.

This paper investigates control-aware attacks against ArduPilot-based Unmanned Aerial Vehicles (UAVs), inwhich an adversary exploits the sensitivity of flight-controller dynamics to parameter changes to cause loss of control and crashes. It describes six attacks that exploit interactions among multi-layer controllers by modifying Proportional-Integral-Derivative (PID) gains, altering Extended Kalman Filter (EKF) estimation configuration, and violating failsafe assumptions, thereby forcing ArduPilot into unsafe operating conditions. We evaluate the attacks in Software-in-the-Loop (SITL) simulation and validate them on a Pixhawk 2.4.8 hardware platform. The results show that short sequences of well-formed MAVLink messages can exploit controller sensitivity to parameter values and updates frequency, affecting controller states and degrading attitude stability, angular-rate behavior, trajectory tracking, and estimator health. We demonstrate that when multiple effects are combined, the vehicle can enter an unsafe state and crashes. These findings show that security gaps in input-parameter handling, command trust, and controller-state validation can be exploited to cause loss of control and crashes in UAVs.

Yibo Wang, Riteng Zhang, Yinghao He et al.

Reliable analytics and machine-learning pipelines depend on clean tabular data, yet production tables often contain missing values, typographical errors, inconsistent formats, violated dependencies, unit mismatches, and ambiguous categorical values. Existing cleaning systems make different trade-offs. Constraint-based systems need experts to specify rules. Learning-based systems need labels or retraining. Recent LLM-based cleaners reduce setup effort, but many call an LLM on rows, cells, or repeated workflow steps, so their cost grows with table size and with every recurring batch. We present TabClean, a model-training-free system that compiles LLM reasoning into reusable guarded cleaning programs. Given a dirty table and a small annotated development set, TabClean profiles table evidence, diagnoses repair mechanisms, synthesizes executable Python transformations, validates candidates with cell-level feedback, and commits the best program for reuse on schema-compatible batches. The key abstraction is an evidence-backed guarded repair clause. A deterministic transformation may fire only when its dirty pattern, target-negative condition, evidence support, and scope constraints are satisfied. Across six benchmarks, TabClean achieves high precision, improves F1 over representative rule-based, learning-based, and LLM-based baselines on five datasets, and substantially reduces recurring runtime and API cost by replacing repeated LLM inference with deterministic program execution.

Sripath Mishra, Bharat Bhargava, Zizheng Liu et al.

Intrusion detection systems (IDS) trained on wired-network benchmarks degrade sharply in real-world unmanned aerial vehicle (UAV) swarms, where mobility, fluctuating link quality, and decentralized routing reshape traffic distributions. Existing UAV-specific datasets also do not systematically vary these conditions, leaving no way to train or test an IDS against the very shift that defeats it. We present UAV-CAS, a large-scale labeled flow dataset for UAV-network intrusion detection, generated by a Containernet digital twin that is systematically calibrated against AERPAW testbed measurements. We have a four-layer calibration pipeline spanning altitude-dependent path loss, mission-specific mobility, the link-level performance chain, and end-to-end trace fidelity. UAV-CAS comprises 99,492 flows drawn from 1,024 configurations that span five attack families (DoS, DDoS, blackhole, wormhole, replay) and nine collaborative attack compositions. A diversity analysis shows that high-rate attacks separate from benign traffic up to an order of magnitude more strongly than in any prior benchmark, while stealth attacks deliberately blend with benign traffic. Across ten baseline IDS, binary attack detection saturates above $0.98$, confirming the dataset is learnable, whereas full attack-class identification remains hard -- per-class $F_1$ ranges from near zero to $0.82$ and falls into the single digits for stealth attacks. We release the dataset, simulator, and calibration data to support reproducible UAV intrusion-detection research.