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Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection, log parsing, feature extraction, and anomaly detection,…
Logs are semi-structured text files that represent software's execution paths and states during its run-time. Therefore, detecting anomalies in software logs reflect anomalies in the software's execution path or state. So, it has become a…
The constant increase in the amount and complexity of information obtained from IT data networkelements, for its correct monitoring and management, is a reality. The same happens to data net-works in electrical systems that provide…
As Large-Scale Cloud Systems (LCS) become increasingly complex, effective anomaly detection is critical for ensuring system reliability and performance. However, there is a shortage of large-scale, real-world datasets available for…
Log anomaly detection (LAD) is essential to ensure safe and stable operation of software systems. Although current LAD methods exhibit significant potential in addressing challenges posed by unstable log events and temporal sequence…
Wireless Sensor Networks forms the backbone of modern cyber physical systems used in various applications such as environmental monitoring, healthcare monitoring, industrial automation, and smart infrastructure. Ensuring the reliability of…
The conventional wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Unfortunately, bugs, misconfigurations,…
Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs? A key part of achieving this goal is to use the network of power grid sensors to quickly detect, in real-time, when any unusual events,…
Automatic log file analysis enables early detection of relevant incidents such as system failures. In particular, self-learning anomaly detection techniques capture patterns in log data and subsequently report unexpected log event…
Anomaly detection is crucial in financial auditing, and effective detection requires large volumes of data from multiple organizations. However, journal entry data is highly sensitive, making it infeasible to share them directly across…
Anomaly detection is a crucial step for preventing malicious activities in the network and keeping resources available all the time for legitimate users. It is noticed from various studies that classical anomaly detectors work well with…
Modern real-time Structural Health Monitoring systems can generate a considerable amount of information that must be processed and evaluated for detecting early anomalies and generating prompt warnings and alarms about the civil…
Anomaly-based cyber threat detection using deep learning is on a constant growth in popularity for novel cyber-attack detection and forensics. A robust, efficient, and real-time threat detector in a large-scale operational enterprise…
Effective anomaly detection from logs is crucial for enhancing cybersecurity defenses by enabling the early identification of threats. Despite advances in anomaly detection, existing systems often fall short in areas such as post-detection…
Transforming a design into a high-quality product is a challenge in metal additive manufacturing due to rare events which can cause defects to form. Detecting these events in-situ could, however, reduce inspection costs, enable corrective…
An important tool grid operators use to safeguard against failures, whether naturally occurring or malicious, involves detecting anomalies in the power system SCADA data. In this paper, we aim to solve a real-time anomaly detection problem.…
In the research area of anomaly detection, novel and promising methods are frequently developed. However, most existing studies exclusively focus on the detection task only and ignore the interpretability of the underlying models as well as…
Anomaly detection systems aim to detect and report attacks or unexpected behavior in networked systems. Previous work has shown that anomalies have an impact on system performance, and that performance signatures can be effectively used for…
In this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network.…
Online unsupervised detection of anomalies is crucial to guarantee the correct operation of cyber-physical systems and the safety of humans interacting with them. State-of-the-art approaches based on deep learning via neural networks…