Security Chaos Engineering (SCE) is a proactive approach to identify vulnerabilities and enhance security of systems. It embraces continuous security experimentation to build confidence in the capability of systems to withstand malicious conditions. Different SCE techniques are proposed for enhancing the resilience of software systems. The diversity of SCE techniques indicates the need for their collective analysis to uncover valuable practices and potential research opportunities. To fulfill this need, we consolidate and unify the knowledge on SCE practices through a systematic literature review. The results show that there has been limited and unsystematic investigation of SCE by the community, highlighting the importance of creating and promoting guidelines for SCE practices. Therefore, we create SCENE, a comprehensive set of guidelines for systematically reporting SCE. The goal is to support the clarity, consistency, and reproducibility of SCE practices. SCENE guidelines are evaluated by cybersecurity practitioners and active researchers in the field, and is mapped to established methodological guidelines. The results indicates that SCENE is perceived positive in terms of usefulness, understandability, practicality, and completeness. SCENE is also found to complement established experimental reporting guidelines and bridge the gap between academic studies and industrial use.

Color fidelity is often overlooked in simulation-based validation for autonomous vehicles, yet even minor color mismatches can undermine the reliability of AI-driven perception systems. In this paper, we systematically examine how controlled deviations in color reproduction-quantified by$\Delta E$-affect object detection accuracy across 32 variants of YOLO. Using a Macbeth ColorChecker, we derive calibrations for key color transforms (brightness, contrast, hue, gamma, saturation and color bias) and apply these to the COCO validation set. Our evaluations demonstrate that increasing$\Delta E$yields significant drops in detection metrics, especially for safety-critical categories such as pedestrians and cyclists. Based on these findings, we propose$\Delta E$thresholds that define acceptable color fidelity in camera simulations (e.g.,$\Delta E\leq 3$for$\Delta \mathbf{mAP}\leq 1\%)$. Further-more, we contribute these transformed datasets and scripts as a publicly available benchmark, enabling reproducible comparisons and guiding future research on color-based vulnerabilities in automated driving and other safety-critical domains.

Autonomous perception in forest environments requires accurate detection and segmentation of complex natural objects such as trees, rocks, and terrain features. However, the scarcity of large-scale, annotated forest datasets, especially those with depth and instance segmentation labels, hinders progress in deploying robust deep learning models for forestry applications. In this paper, we present SimForest, a 4K-resolution synthetic RGBD dataset generated using a photorealistic forestry simulator built on Unreal Engine 5. SimForest comprises 5,000 images, each annotated with aligned RGB data, depth maps, instance segmentation masks, and detailed metadata including object poses, terrain depth, camera parameters, and environmental conditions such as season, time, and cloudiness. The virtual scenes are geo-located and seasonally matched to a real forest near Umea, Sweden. To demonstrate the utility of SimForest, we conduct an experimental study involving the detection and segmentation of tree trunks using YOLOv11-based models trained on SimForest data. The evaluation shows strong detection accuracy (mAP@50 of 0.92) and solid segmentation performance (mAP@50 of 0.74). These findings highlight the potential of SimForest as a valuable resource for near-field RGBD perception in forestry and related outdoor robotics applications.

Non-intrusive load monitoring (NILM) is the process of disaggregating total electricity consumption measured by a smart meter into individual appliances’ contributions. In this paper, we present a privacy control strategy that selectively filters appliances’ consumption from the smart meter measurements to hinder NILM disaggregation performance. The privacy controller uses charging and discharging operations of an energy storage to achieve desired smart meter measurements. We model the household consumption using both additive and difference factorial hidden Markov models and design a control strategy to minimize privacy leakage measured in terms of Bayesian risk due to maximum a posteriori detection. Due to the high computational complexity of the optimal control strategy, we propose a computationally efficient sub-optimal strategy. We evaluate the proposed approaches using the ECO data set and show their privacy improvements against the Viterbi disaggregation algorithm.

There is a growing interest in analysing freshness of data in networked systems. Age of Information (AoI) has emerged as a relevant metric to quantify this freshness at a receiver, and minimizing this metric for different system models has received significant research attention. However, a fundamental question remains: what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? We address this question for the average peak AoI (PAoI) statistic by considering generate-at-will source model, service preemptions, and request delays. Our main result is on the characterization of the minimum achievable average PAoI, and we show that it is achieved by a fixed-threshold policy among the set of all causal policies. We use the characterization to provide necessary and sufficient condition for preemptions to be beneficial for a given service-time distribution. Our numerical results, obtained using well-known distributions, demonstrate that the heavier the tail of a distribution the higher the performance gains of using preemptions.

We study the optimal control problem of the maximum a posteriori (MAP) state sequence detection of an adversary using smart meter data. The privacy leakage is measured using the Bayesian risk and the privacy-enhancing control is achieved in real-time using an energy storage system. The control strategy is designed to minimize the expected performance of a non-causal adversary at each time instant. With a discrete-state Markov model, we study two detection problems: when the adversary is unaware or aware of the control. We show that the adversary in the former case can be controlled optimally. In the latter case, where the optimal control problem is shown to be non-convex, we propose an adaptive-grid approximation algorithm to obtain a sub-optimal strategy with reduced complexity. Although this work focuses on privacy in smart meters, it can be generalized to other sensor networks.

There is a growing interest in analysing the freshness of data in networked systems. Age of Information (AoI) has emerged as a popular metric to quantify this freshness at a given destination. There has been a significant research effort in optimizing this metric in communication and networking systems under different settings. In contrast to previous works, we are interested in a fundamental question, what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? To address this question, we study a problem of optimizing AoI under service preemptions. Our main result is on the characterization of the minimum achievable average peak AoI (PAoI). We obtain this result by showing that a fixed-threshold policy is optimal in the set of all randomized-threshold causal policies. We use the characterization to provide necessary and sufficient conditions for the service-time distributions under which preemptions are beneficial.