Anomaly detection is vital for enhancing the safety of Industrial Control Systems (ICS). However, the complicated structure of ICS creates complex temporal correlations among devices with many parameters. Current methods often ignore these correlations and poorly select parameters, missing valuable insights. Additionally, they lack interpretability, operating efficiently with limited resources, and root cause identification. This study proposes an explainable correlation-based anomaly detection method for ICS. The optimal window size of the data is determined using Long Short-Term Memory Networks—Autoencoder (LSTM-AE) and the correlation parameter set is extracted using the Pearson correlation. A Latent Correlation Matrix (LCM) is created from the correlation parameter set and a Latent Correlation Vector (LCV) is derived from LCM. Based on the LCV, the method utilizes a Multivariate Gaussian Distribution (MGD) to identify anomalies. This is achieved through an anomaly detection module that incorporates a threshold mechanism, utilizing alpha and epsilon values. The proposed method utilizes a novel set of input features extracted using the Shapley Additive explanation (SHAP) framework to train and evaluate the MGD model. The method is evaluated on the Secure Water Treatment (SWaT), Hardware-in-the-loop-based augmented ICS security (HIL-HAI), and Internet of Things Modbus dataset using precision, recall, and F-1 score metrics. Additionally, SHAP is used to gain insights into the anomalies and identify their root causes. Comparative experiments demonstrate the method's effectiveness, achieving a better 0.96% precision and 0.84% F1-score. This enhanced performance aids ICS engineers and decision-makers in identifying the root causes of anomalies. Our code is publicly available at a GitHub repository: https://github.com/Ermiyas21/Explainable-correlation-AD.

Money laundering hides illegal money’s origin by making it seem legal. Detecting suspicious activity quickly in financial data is key to stopping fraud and money laundering. Real-time detection is popular approach for its speed and efficiently detecting illegal activities in financial institutes system. However, handling massive and distributed data streams have challenges in achieving real-time efficiency and effectiveness. Therefore proposing and developing data stream framework is needed to handle these challenges efficiently. The main goal of this study is to propose real-time suspicious detection framework for financial institutions to effectively combat money laundering. The proposed model comprises two approaches: a distributed computing architecture based on Docker container to enhance flexibility, migration capabilities, and customization, and a suspicious detection module employing the autoencoder method. To determine whether there is any suspicious activity in the system, the proposed model uses the reconstruction error. The reconstruction error is the difference between the original input data and the data reconstructed by the proposed model. To evaluate the proposed model, we used real-world data from a financial institution and synthetic data generated from the real-world data. The study demonstrates the better performance of the proposed real-time detection framework compared to traditional methods in identifying anomalous transactions. It also explores the importance and limitations of using both real-world and generated data. Our code is publicly available: https://github.com/Ermiyas21/Real-Time-Suspicious-Detection-Framework-for-financial-data.

In recent years, wireless sensor networks (WSN) have received considerable attention in environmental, industrial monitoring, and control applications. It is used to monitor, collect, maintain, and analyse environmental data from a remote place with the help of remote sensing and monitoring technologies. Identification of suitable algorithms and protocol WSNs for water quality monitoring is a challenge due to the limitation of energy, computation capability, and storage resources of the sensor nodes. In this paper, we designed a model for a WSN-based water quality monitoring system that employs a machine learning technique to analyse water data and facilitates decision-making. The suggested model can gather water quality parameters from a water body and send them to a distant data centre, where they will be processed using a machine learning (ML) algorithm. The experiment shown that by adopting WSN and ML approach, we can obtain state-of-the-art 0.0723 RMSE and 0.0052 MAE, indicating that the proposed methodology is appropriate for usage in intelligence water quality monitoring.