arXiv subjects

Sidra Ghayour Bhatti

Publications and source records attributed to Sidra Ghayour Bhatti.

8 recordsLinked to original sources

arXiv2026

When Stealth Requires Memory: Budgeted Attack Scheduling under a Whiteness Constraint

We pose stealthy attack scheduling on a sensor-to-estimator link under a resource constraint with a budget \barΓ on the fraction of corrupted transmissions and a model-free whiteness constraint on the received innovations. For a discrete-time linear plant with non-Gaussian noise, the worst attack, innovation sign flip, preserves the innovation magnitude and is exactly stealthy against every magnitude-measurable detector, the damage-optimal schedule being a memoryless threshold; but the tail concentration that maximizes damage also manufactures serial correlation, where an innovation-whiteness monitor gains power. We dualize the whiteness constraint and show the optimum is a threshold rule on corrected innovation energy using memory of recent magnitudes and the previous decision. We further trace that correction to a second degree of freedom and set the damage by the location of the firing set in magnitude space and set exposure by the boundary density of its run structure. We realize it as a hysteresis set by one split-conformal order statistic without any plant model, using one counter and two comparisons per step. It keeps 80--96\% of the memoryless damage at 2.3 to 71 times lower lag-one whiteness power, validated on a real truck CAN record.

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arXiv2026

Distribution-Free Budgeted Stealthy Attack Scheduling for Remote State Estimation

This letter addresses budgeted stealthy false-data-injection (FDI) scheduling against remote state estimation, where a resource-constrained adversary may corrupt at most a fraction $\barΓ$ of transmissions. Existing event-triggered schedulers invert a Gaussian innovation tail to set the firing threshold and certify stealth by covariance matching; both are exact only under Gaussianity, which real cyber-physical residuals routinely violate. We propose a distribution-free scheduler pairing the worst-case FDI action with a split-conformal calibrated trigger, requiring neither the plant matrices nor any distributional model. We establish exact pathwise stealth against every magnitude-measurable detector, for any firing rule and innovation law; a finite-sample distribution-free bound on the mean firing rate, with almost-sure budget attainment under stationarity and ergodicity; and a steady-state degradation identity linear in a single scalar energy capture $ψ$, maximized by the same order statistic that delivers the budget guarantee. A conditional sign-symmetry condition delimits when the certificate extends to sign-sensitive detectors, the residual exposure being governed by a fourth cumulant that vanishes under Gaussian noise. Monte-Carlo studies and a heavy-duty truck CAN record confirm the bounds and quantify what the Gaussian assumption costs outside its regime.

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arXiv2026

Ranking-Augmented On-Policy Optimization with Adaptive Advantage-Normalization for Constrained Control

This paper analyzes the boundedness and feasibility properties of Advantage-Ranked Group Relative Policy Optimization (A-GRPO), a ranking-augmented, critic-free policy gradient method employing a Transformer-encoder actor for fixed-horizon control with terminal constraints. When feasibility is evaluated only at the final step, the resulting sparse feedback destabilizes critic-based advantage estimation and weakens standard Lagrangian approaches. A trajectory-level ranking mechanism that augments group-relative policy updates by reweighting advantages according to constraint satisfaction is formalized, and three results are established: (i) a scale-adaptive per-timestep normalization bounds advantage variance at every timestep independently, (ii) the ranked advantage strictly separates feasible from violating trajectories under a verifiable ranking-weight condition, biasing the policy gradient toward constraint satisfaction, and (iii) the adaptive dual variables remain bounded and exhibit a drift-balance property that acts as a feedback mechanism for feasibility. These results are validated on a 3,605-step series-hybrid powertrain energy management task with a terminal state-of-charge constraint, where A-GRPO achieves 75.4% mean sustained feasibility with return within 3.7% of the dynamic programming optimum, outperforming a Proximal Policy Optimization with Lagrangian penalties (PPO-Lag) baseline (27.4% sustained), and ablation experiments confirm that both the ranking and Lagrangian components are necessary for this performance.

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arXiv2026

Model-Free Budgeted Attack Scheduling for Cyber-Physical Systems

This letter studies the budgeted scheduling of stealthy false data-injection (FDI) attacks against state estimators in cyber-physical systems. Existing event-based attack schedulers require full knowledge of the plant model and assume the residual distribution is exactly Gaussian -- assumptions that fail for real-world CPS sensor streams whose residuals are heavy-tailed and whose dynamics are unknown to the adversary. We propose a model-free attack-scheduler that replaces the parametric Gaussian threshold with the empirical quantile of a learned sequence autoencoder residual, calibrated from measurements alone without any plant matrices. We prove that the realized attack rate converges almost surely to the target budget under stationary ergodic residuals. Experiments on two synthetic systems and a real heavy-duty truck dataset show that the proposed scheduler tracks the budget to within 1-2% while also preserving the residual magnitude, guaranteeing stealthiness against any residual-based detector. Comparing with the model-based baseline -- granted the true plant and innovation covariance -- mis-realizes the budget by up to 8.96% under heavy-tailed residual distribution, causing the attacker to achieve only 1.37x system degradation when 1.84x is intended.

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arXiv2025

Multi-Stage Knowledge-Distilled VGAE and GAT for Robust Controller-Area-Network Intrusion Detection

The Controller Area Network (CAN) protocol is a standard for in-vehicle communication but remains susceptible to cyber-attacks due to its lack of built-in security. This paper presents a multi-stage intrusion detection framework leveraging unsupervised anomaly detection and supervised graph learning tailored for automotive CAN traffic. Our architecture combines a Variational Graph Autoencoder (VGAE) for structural anomaly detection with a Knowledge-Distilled Graph Attention Network (KD-GAT) for robust attack classification. CAN bus activity is encoded as graph sequences to model temporal and relational dependencies. The pipeline applies VGAE-based selective undersampling to address class imbalance, followed by GAT classification with optional score-level fusion. The compact student GAT achieves 96% parameter reduction compared to the teacher model while maintaining strong predictive performance. Experiments on six public CAN intrusion datasets--Car-Hacking, Car-Survival, and can-train-and-test--demonstrate competitive accuracy and efficiency, with average improvements of 16.2% in F1-score over existing methods, particularly excelling on highly imbalanced datasets with up to 55% F1-score improvements.

cs.LG↗

arXiv2025

Sequence Aware SAC Control for Engine Fuel Consumption Optimization in Electrified Powertrain

As hybrid electric vehicles (HEVs) gain traction in heavy-duty trucks, adaptive and efficient energy management is critical for reducing fuel consumption while maintaining battery charge for long operation times. We present a new reinforcement learning (RL) framework based on the Soft Actor-Critic (SAC) algorithm to optimize engine control in series HEVs. We reformulate the control task as a sequential decision-making problem and enhance SAC by incorporating Gated Recurrent Units (GRUs) and Decision Transformers (DTs) into both actor and critic networks to capture temporal dependencies and improve planning over time. To evaluate robustness and generalization, we train the models under diverse initial battery states, drive cycle durations, power demands, and input sequence lengths. Experiments show that the SAC agent with a DT-based actor and GRU-based critic was within 1.8% of Dynamic Programming (DP) in fuel savings on the Highway Fuel Economy Test (HFET) cycle, while the SAC agent with GRUs in both actor and critic networks, and FFN actor-critic agent were within 3.16% and 3.43%, respectively. On unseen drive cycles (US06 and Heavy Heavy-Duty Diesel Truck (HHDDT) cruise segment), generalized sequence-aware agents consistently outperformed feedforward network (FFN)-based agents, highlighting their adaptability and robustness in real-world settings.

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arXiv2025

Improving Q-Learning for Real-World Control: A Case Study in Series Hybrid Agricultural Tractors

The variable and unpredictable load demands in hybrid agricultural tractors make it difficult to design optimal rule-based energy management strategies, motivating the use of adaptive, learning-based control. However, existing approaches often rely on basic fuel-based rewards and do not leverage expert demonstrations to accelerate training. In this paper, first, the performance of Q-value-based reinforcement learning algorithms is evaluated for powertrain control in a hybrid agricultural tractor. Three algorithms, Double Q-Learning (DQL), Deep Q-Networks (DQN), and Double DQN (DDQN), are compared in terms of convergence speed and policy optimality. Second, a piecewise domain-specific reward-shaping strategy is introduced to improve learning efficiency and steer agent behavior toward engine fuel-efficient operating regions. Third, the design of the experience replay buffer is examined, with a focus on the effects of seeding the buffer with expert demonstrations and analyzing how different types of expert policies influence convergence dynamics and final performance. Experimental results demonstrate that (1) DDQN achieves 70\% faster convergence than DQN in this application domain, (2) the proposed reward shaping method effectively biases the learned policy toward fuel-efficient outcomes, and (3) initializing the replay buffer with structured expert data leads to a 33\% improvement in convergence speed.

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arXiv2025

KD-GAT: Combining Knowledge Distillation and Graph Attention Transformer for a Controller Area Network Intrusion Detection System

The Controller Area Network (CAN) protocol is widely adopted for in-vehicle communication but lacks inherent security mechanisms, making it vulnerable to cyberattacks. This paper introduces KD-GAT, an intrusion detection framework that combines Graph Attention Networks (GATs) with knowledge distillation (KD) to enhance detection accuracy while reducing computational complexity. In our approach, CAN traffic is represented as graphs using a sliding window to capture temporal and relational patterns. A multi-layer GAT with jumping knowledge aggregation acting as the teacher model, while a compact student GAT--only 6.32% the size of the teacher--is trained via a two-phase process involving supervised pretraining and knowledge distillation with both soft and hard label supervision. Experiments on three benchmark datasets--Car-Hacking, Car-Survival, and can-train-and-test demonstrate that both teacher and student models achieve strong results, with the student model attaining 99.97% and 99.31% accuracy on Car-Hacking and Car-Survival, respectively. However, significant class imbalance in can-train-and-test has led to reduced performance for both models on this dataset. Addressing this imbalance remains an important direction for future work.

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