arXiv subjects

Zeeshan Afzal

Publications and source records attributed to Zeeshan Afzal.

5 recordsLinked to original sources

arXiv2025

The Dark Side of Flexibility: How Aggregated Cyberattacks Threaten the Power Grid

Flexible energy resources are increasingly becoming common in smart grids. These resources are typically managed and controlled by aggregators that coordinate many resources to provide flexibility services. However, these aggregators and flexible energy resources are vulnerable, which could allow attackers to remotely control flexible energy resources to launch large-scale attacks on the grid. This paper investigates and evaluates the potential attack strategies that can be used to manipulate flexible energy resources to challenge the effectiveness of traditional grid stability measures and disrupt the first-swing stability of the power grid. Our work shows that although a large amount of power is required, the current flexibility capacities could potentially be sufficient to disrupt the grid on a national level.

cs.CR↗

arXiv2025

Vision transformer-based multi-camera multi-object tracking framework for dairy cow monitoring

Activity and behaviour correlate with dairy cow health and welfare, making continual and accurate monitoring crucial for disease identification and farm productivity. Manual observation and frequent assessments are laborious and inconsistent for activity monitoring. In this study, we developed a unique multi-camera, real-time tracking system for indoor-housed Holstein Friesian dairy cows. This technology uses cutting-edge computer vision techniques, including instance segmentation and tracking algorithms to monitor cow activity seamlessly and accurately. An integrated top-down barn panorama was created by geometrically aligning six camera feeds using homographic transformations. The detection phase used a refined YOLO11-m model trained on an overhead cow dataset, obtaining high accuracy (mAP\@0.50 = 0.97, F1 = 0.95). SAMURAI, an upgraded Segment Anything Model 2.1, generated pixel-precise cow masks for instance segmentation utilizing zero-shot learning and motion-aware memory. Even with occlusion and fluctuating posture, a motion-aware Linear Kalman filter and IoU-based data association reliably identified cows over time for object tracking. The proposed system significantly outperformed Deep SORT Realtime. Multi-Object Tracking Accuracy (MOTA) was 98.7% and 99.3% in two benchmark video sequences, with IDF1 scores above 99% and near-zero identity switches. This unified multi-camera system can track dairy cows in complex interior surroundings in real time, according to our data. The system reduces redundant detections across overlapping cameras, maintains continuity as cows move between viewpoints, with the aim of improving early sickness prediction through activity quantification and behavioural classification.

cs.CV↗

arXiv2025

Towards Privacy-Preserving Anomaly-Based Intrusion Detection in Energy Communities

Energy communities consist of decentralized energy production, storage, consumption, and distribution and are gaining traction in modern power systems. However, these communities may increase the vulnerability of the grid to cyber threats. We propose an anomaly-based intrusion detection system to enhance the security of energy communities. The system leverages deep autoencoders to detect deviations from normal operational patterns in order to identify anomalies induced by malicious activities and attacks. Operational data for training and evaluation are derived from a Simulink model of an energy community. The results show that the autoencoder-based intrusion detection system achieves good detection performance across multiple attack scenarios. We also demonstrate potential for real-world application of the system by training a federated model that enables distributed intrusion detection while preserving data privacy.

cs.CR↗

arXiv2025

From Balance to Breach: Cyber Threats to Battery Energy Storage Systems

Battery energy storage systems are an important part of modern power systems as a solution to maintain grid balance. However, such systems are often remotely managed using cloud-based control systems. This exposes them to cyberattacks that could result in catastrophic consequences for the electrical grid and the connected infrastructure. This paper takes a step towards advancing understanding of these systems and investigates the effects of cyberattacks targeting them. We propose a reference model for an electrical grid cloud-controlled load-balancing system connected to remote battery energy storage systems. The reference model is evaluated from a cybersecurity perspective by implementing and simulating various cyberattacks. The results reveal the system's attack surface and demonstrate the impact of cyberattacks that can criticaly threaten the security and stability of the electrical grid.

cs.CR↗

arXiv2023

Threat Scenarios and Monitoring Requirements for Cyber-Physical Systems of Flexibility Markets

The ongoing integration of renewable generation and distributed energy resources introduces new challenges to distribution network operation. Due to the increasing volatility and uncertainty, distribution system operators (DSOs) are seeking concepts to enable more active management and control. Flexibility markets (FMs) offer a platform for economically efficient trading of electricity flexibility between DSOs and other participants. The integration of cyber, physical and market domains of multiple participants makes FMs a system of cyber-physical systems (CPSs). While cross-domain integration sets the foundation for efficient deployment of flexibility, it introduces new physical and cyber vulnerabilities to participants. This work systematically formulates threat scenarios for the CPSs of FMs, revealing several remaining security challenges across all domains. Based on the threat scenarios, unresolved monitoring requirements for secure participation of DSOs in FMs are identified, providing the basis for future works that address these gaps with new technical concepts.

eess.SY↗