39 seconds. That is the timelapse between two consecutive cyber attacks as of 2023. Meaning that by the time you are done reading this abstract, about 1 or 2 additional cyber attacks would have occurred somewhere in the world. In this context of highly increased frequency of cyber threats, Security Operation Centers (SOC) and Computer Emergency Response Teams (CERT) can be overwhelmed. In order to relieve the cybersecurity teams in their investigative effort and help them focus on more added-value tasks, machine learning approaches and methods started to emerge. This paper introduces a novel method, IsoEx, for detecting anomalous and potentially problematic command lines during the investigation of contaminated devices. IsoEx is built around a set of features that leverages the log structure of the command line, as well as its parent/child relationship, to achieve a greater accuracy than traditional methods. To detect anomalies, IsoEx resorts to an unsupervised anomaly detection technique that is both highly sensitive and lightweight. A key contribution of the paper is its emphasis on interpretability, achieved through the features themselves and the application of eXplainable Artificial Intelligence (XAI) techniques and visualizations. This is critical to ensure the adoption of the method by SOC and CERT teams, as the paper argues that the current literature on machine learning for log investigation has not adequately addressed the issue of explainability. This method was proven efficient in a real-life environment as it was built to support a companyś SOC and CERT