Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach Muhammad Akbar Husnoo a,, Adnan Anwar a, Md Enamul Haque b and Abdun Naser Mahmood c a Centre for Cyber Resilience and Trust (CREST), Deakin University, 75 Pigdons Rd, Waurn Ponds, 3216, Victoria, Australia b Centre for Smart Power and Energy Research (CSPER)), Deakin University, 75 Pigdons Rd, Waurn Ponds, 3216, Victoria, Australia c Department of Computer Science & IT, Latrobe University, Plenty Rd, Bundoora, 3086, Victoria, AustraliaA R T I C L E I N F OKeywords: Anomaly Detection Decentralized Federated Learning (DFL) Cyberattack Internet of Things (Io T) Smart Grid A B S T R A C T Amidst escalating concerns regarding security and privacy within the Smart Grid domain, the need for robust intrusion detection mechanisms in critical energy infrastructure has surged in recent times. To address the challenges posed by privacy preservation and decentralized power zones with distinct data ownership, Federated Learning (FL) has emerged as a promising privacy-preserving solution which facilitates collaborative training of attack detection models without necessitating the sharing of raw data. However, FL presents several implementation limitations in the power system domain due to its heavy reliance on a centralized aggregator and the risks of privacy leakage during model update transmission. In response to the technical bottlenecks, this paper introduces a novel decentralized federated anomaly detection scheme based on two main gossip protocols namely Random Walk and Epidemic. Our findings indicate that the Random Walk protocol exhibits superior performance compared to the Epidemic protocol, highlighting its efficacy in decentralized federated learning environments. Experimental validation of the proposed framework utilizing publicly available industrial control systems datasets demonstrates superior attack detection accuracy while safeguarding data confidentiality and mitigating the impact of communication latency and stragglers. Moreover, a notable 35% improvement in training time against conventional FL highlights the efficacy and robustness of our decentralized learning approach.1.

In this research, an effort is made to address microgrid systems' operational challenges, characterized by power oscillations that eventually contribute to grid instability. An integrated strategy is proposed, leveraging the strengths of convolutional and Gated Recurrent Unit (GRU) layers. This approach is aimed at effectively extracting temporal data from energy datasets to improve the precision of microgrid behavior forecasts. Additionally, an attention layer is employed to underscore significant features within the time-series data, optimizing the forecasting process. The framework is anchored by a Multi-Layer Perceptron (MLP) model, which is tasked with comprehensive load forecasting and the identification of abnormal grid behaviors. Our methodology underwent rigorous evaluation using the Micro-grid Tariff Assessment Tool dataset, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (r2-score) serving as the primary metrics. The approach demonstrated exemplary performance, evidenced by a MAE of 0.39, RMSE of 0.28, and an r2-score of 98.89\% in load forecasting, along with near-perfect zero state prediction accuracy (approximately 99.9\%). Significantly outperforming conventional machine learning models such as support vector regression and random forest regression, our model's streamlined architecture is particularly suitable for real-time applications, thereby facilitating more effective and reliable microgrid management.

In multivariate time-series forecasting (MTSF), extracting the temporal correlations of the input sequences is crucial. While popular Transformer-based predictive models can perform well, their quadratic computational complexity results in inefficiency and high overhead. The recently emerged Mamba, a selective state space model, has shown promising results in many fields due to its strong temporal feature extraction capabilities and linear computational complexity. However, due to the unilateral nature of Mamba, channel-independent predictive models based on Mamba cannot attend to the relationships among all variables in the manner of Transformer-based models. To address this issue, we combine fast-attention with Mamba to introduce a novel framework named FMamba for MTSF. Technically, we first extract the temporal features of the input variables through an embedding layer, then compute the dependencies among input variables via the fast-attention module. Subsequently, we use Mamba to selectively deal with the input features and further extract the temporal dependencies of the variables through the multi-layer perceptron block (MLP-block). Finally, FMamba obtains the predictive results through the projector, a linear layer. Experimental results on eight public datasets demonstrate that FMamba can achieve state-of-the-art performance while maintaining low computational overhead.

In recent years, as computer vision tasks have increasingly relied on high-quality image inputs, the task of image dehazing has received significant attention. Previously, many methods based on priors and deep learning have been proposed to address the task of image dehazing. Ignoring the domain gap between different data, former de-hazing methods usually adopt multiple datasets for explicit training, which often makes the methods themselves be violated. To address this problem, we propose a novel method of internal and external data augmentation to improve the existing dehazing methodology. By using cross-data external augmentor. The dataset inherits samples from different domains that are firmly aligned, making the model learn more robust and generalizable features. By using the internal data augmentation method, the model can fully exploit local information within the images, thereby obtaining more image details. To demonstrate the effectiveness of our proposed method, we conduct training on both the Natural Image Dataset (NID) and the Remote Sensing Image Dataset (RSID). Experimental results show that our method clearly resolves the domain gap in different dehazing datasets and presents a new pipeline for joint training in the dehazing task. Our approach significantly outperforms other advanced methods in dehazing and produces dehazed images that are closest to real haze-free images. The code will be available at: https://github.com/wengzp1/ScaleUpDehazing

Industrial time-series, as a structural data responds to production process information, can be utilized to perform data-driven decision-making for effective monitoring of industrial production process. However, there are some challenges for time-series forecasting in industry, e.g., predicting few-shot caused by data shortage, and decision-confusing caused by unknown treatment policy. To cope with the problems, we propose a novel causal domain adaptation framework, Causal Domain Adaptation (CDA) forecaster to improve the performance on the interested domain with limited data (target). Firstly, we analyze the causality existing along with treatments, and thus ensure the shared causality over time. Subsequently, we propose an answer-based attention mechanism to achieve domain-invariant representation by the shared causality in both domains. Then, a novel domain-adaptation is built to model treatments and outcomes jointly training on source and target domain. The main insights are that our designed answer-based attention mechanism allows the target domain to leverage the existed causality in source time-series even with different treatments, and our forecaster can predict the counterfactual outcome of industrial time-series, meaning a guidance in production process. Compared with commonly baselines, our method on real-world and synthetic oilfield datasets demonstrates the effectiveness in across-domain prediction and the practicality in guiding production process

In the context of satellite monitoring of the earth, we can assume that the surface of the earth is divided into a set of regions. We assume that the impact of a big social/environmental event spills into neighboring regions. Using Identifying Code Sets (ICSes), we can deploy sensors in such a way that the region in which an event takes place can be uniquely identified, even with fewer sensors than regions. As Earth is almost a sphere, we use a soccer ball as a model. We construct a Soccer Ball Graph (SBG), and provide human-oriented, analytical proofs that 1) the SBG has at least 26 ICSes of cardinality ten, implying that there are at least 26 different ways to deploy ten satellites to monitor the Earth and 2) that the cardinality of the minimum Identifying Code Set (MICS) for the SBG is at least nine. We then provide a machine-oriented formal proof that the cardinality of the MICS for the SBG is in fact ten, meaning that one must deploy at least ten satellites to monitor the Earth in the SBG model. We also provide machine-oriented proof that there are exactly 26 ICSes of cardinality ten for the SBG.

In the dynamic landscape of technology, the convergence of Artificial Intelligence (AI) and Operating Systems (OS) has emerged as a pivotal arena for innovation. Our exploration focuses on the symbiotic relationship between AI and OS, emphasizing how AI-driven tools enhance OS performance, security, and efficiency, while OS advancements facilitate more sophisticated AI applications. We delve into various AI techniques employed to optimize OS functionalities, including memory management, process scheduling, and intrusion detection. Simultaneously, we analyze the role of OS in providing essential services and infrastructure that enable effective AI application execution, from resource allocation to data processing. The article also addresses challenges and future directions in this domain, emphasizing the imperative of secure and efficient AI integration within OS frameworks. By examining case studies and recent developments, our review provides a comprehensive overview of the current state of AI-OS integration, underscoring its significance in shaping the next generation of computing technologies. Finally, we explore the promising prospects of Intelligent OSes, considering not only how innovative OS architectures will pave the way for groundbreaking opportunities but also how AI will significantly contribute to advancing these next-generation OSs.

As the complexities of Dynamic Data Driven Applications Systems increase, preserving their resilience becomes more challenging. For instance, maintaining power grid resilience is becoming increasingly complicated due to the growing number of stochastic variables (such as renewable outputs) and extreme weather events that add uncertainty to the grid. Current optimization methods have struggled to accommodate this rise in complexity. This has fueled the growing interest in data-driven methods used to operate the grid, leading to more vulnerability to cyberattacks. One such disruption that is commonly discussed is the adversarial disruption, where the intruder attempts to add a small perturbation to input data in order to "manipulate" the system operation. During the last few years, work on adversarial training and disruptions on the power system has gained popularity. In this paper, we will first review these applications, specifically on the most common types of adversarial disruptions: evasion and poisoning disruptions. Through this review, we highlight the gap between poisoning and evasion research when applied to the power grid. This is due to the underlying assumption that model training is secure, leading to evasion disruptions being the primary type of studied disruption. Finally, we will examine the impacts of data poisoning interventions and showcase how they can endanger power grid resilience.

The spectral risk has wide applications in machine learning, especially in real-world decision-making, where people are not only concerned with models' average performance. By assigning different weights to the losses of different sample points, rather than the same weights as in the empirical risk, it allows the model's performance to lie between the average performance and the worst-case performance. In this paper, we propose SOREL, the first stochastic gradient-based algorithm with convergence guarantees for the spectral risk minimization. Previous algorithms often consider adding a strongly concave function to smooth the spectral risk, thus lacking convergence guarantees for the original spectral risk. We theoretically prove that our algorithm achieves a near-optimal rate of $\widetilde{O}(1/\sqrt{\epsilon})$ in terms of $\epsilon$. Experiments on real datasets show that our algorithm outperforms existing algorithms in most cases, both in terms of runtime and sample complexity.

The transition to sustainable Open Radio Access Network (O-RAN) architectures brings new challenges for resource management, especially in predicting the utilization of Physical Resource Block (PRB)s. In this paper, we propose a novel approach to characterize the PRB load using probabilistic forecasting techniques. First, we provide background information on the O-RAN architecture and components and emphasize the importance of energy/power consumption models for sustainable implementations. The problem statement highlights the need for accurate PRB load prediction to optimize resource allocation and power efficiency. We then investigate probabilistic forecasting techniques, including Simple-Feed-Forward (SFF), DeepAR, and Transformers, and discuss their likelihood model assumptions. The simulation results show that DeepAR estimators predict the PRBs with less uncertainty and effectively capture the temporal dependencies in the dataset compared to SFF- and Transformer-based models, leading to power savings. Different percentile selections can also increase power savings, but at the cost of over-/under provisioning. At the same time, the performance of the Long-Short Term Memory (LSTM) is shown to be inferior to the probabilistic estimators with respect to all error metrics. Finally, we outline the importance of probabilistic, prediction-based characterization for sustainable O-RAN implementations and highlight avenues for future research.