Collaborating Authors


Log-based Anomaly Detection Without Log Parsing Artificial Intelligence

Software systems often record important runtime information in system logs for troubleshooting purposes. There have been many studies that use log data to construct machine learning models for detecting system anomalies. Through our empirical study, we find that existing log-based anomaly detection approaches are significantly affected by log parsing errors that are introduced by 1) OOV (out-of-vocabulary) words, and 2) semantic misunderstandings. The log parsing errors could cause the loss of important information for anomaly detection. To address the limitations of existing methods, we propose NeuralLog, a novel log-based anomaly detection approach that does not require log parsing. NeuralLog extracts the semantic meaning of raw log messages and represents them as semantic vectors. These representation vectors are then used to detect anomalies through a Transformer-based classification model, which can capture the contextual information from log sequences. Our experimental results show that the proposed approach can effectively understand the semantic meaning of log messages and achieve accurate anomaly detection results. Overall, NeuralLog achieves F1-scores greater than 0.95 on four public datasets, outperforming the existing approaches.

Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection Artificial Intelligence

Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.

Extending Isolation Forest for Anomaly Detection in Big Data via K-Means Artificial Intelligence

Industrial Information Technology (IT) infrastructures are often vulnerable to cyberattacks. To ensure security to the computer systems in an industrial environment, it is required to build effective intrusion detection systems to monitor the cyber-physical systems (e.g., computer networks) in the industry for malicious activities. This paper aims to build such intrusion detection systems to protect the computer networks from cyberattacks. More specifically, we propose a novel unsupervised machine learning approach that combines the K-Means algorithm with the Isolation Forest for anomaly detection in industrial big data scenarios. Since our objective is to build the intrusion detection system for the big data scenario in the industrial domain, we utilize the Apache Spark framework to implement our proposed model which was trained in large network traffic data (about 123 million instances of network traffic) stored in Elasticsearch. Moreover, we evaluate our proposed model on the live streaming data and find that our proposed system can be used for real-time anomaly detection in the industrial setup. In addition, we address different challenges that we face while training our model on large datasets and explicitly describe how these issues were resolved. Based on our empirical evaluation in different use-cases for anomaly detection in real-world network traffic data, we observe that our proposed system is effective to detect anomalies in big data scenarios. Finally, we evaluate our proposed model on several academic datasets to compare with other models and find that it provides comparable performance with other state-of-the-art approaches.

A Unifying Review of Deep and Shallow Anomaly Detection Artificial Intelligence

Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text. These results have sparked a renewed interest in the anomaly detection problem and led to the introduction of a great variety of new methods. With the emergence of numerous such methods, including approaches based on generative models, one-class classification, and reconstruction, there is a growing need to bring methods of this field into a systematic and unified perspective. In this review we aim to identify the common underlying principles as well as the assumptions that are often made implicitly by various methods. In particular, we draw connections between classic 'shallow' and novel deep approaches and show how this relation might cross-fertilize or extend both directions. We further provide an empirical assessment of major existing methods that is enriched by the use of recent explainability techniques, and present specific worked-through examples together with practical advice. Finally, we outline critical open challenges and identify specific paths for future research in anomaly detection.

Real-time Anomaly Detection and Classification in Streaming PMU Data Machine Learning

--Ensuring secure and reliable operations of the power grid is a primary concern of system operators. Phasor measurement units (PMUs) are rapidly being deployed in the grid to provide fast-sampled operational data that should enable quicker decision-making. This work presents a general interpretable framework for analyzing real-time PMU data, and thus enabling grid operators to understand the current state and to identify anomalies on the fly. Applying statistical learning tools on the streaming data, we first learn an effective dynamical model to describe the current behavior of the system. Next, we use the probabilistic predictions of our learned model to define in a principled way an efficient anomaly detection tool. Finally, the last module of our framework produces on-the-fly classification of the detected anomalies into common occurrence classes using features that grid operators are familiar with. We demonstrate the efficacy of our interpretable approach through extensive numerical experiments on real PMU data collected from a transmission operator in the USA. Traditional supervisory control and data acquisition (SCADA) systems provide information regarding the system state at the order of seconds to the operator. However, such fidelity, considered appropriate in prior decades, is not sufficient to observe or predict disturbances at faster timescales that are increasingly being observed in today's stochastic grid [1]. To provide more rapid measurement data, phasor measurement units (PMUs) have gained widespread deployment. PMUs [2] are time-synchronized by GPS timestamps and collect measurements of system states (Eg.

Online Topology Identification from Vector Autoregressive Time Series Machine Learning

Due to their capacity to condense the spatiotemporal structure of a data set in a format amenable for human interpretation, forecasting, and anomaly detection, causality graphs are routinely estimated in social sciences, natural sciences, and engineering. A popular approach to mathematically formalize causality is based on vector autoregressive (VAR) models, which constitutes an alternative to the well-known but usually intractable Granger causality. Relying on such a VAR causality notion, this paper develops two algorithms with complementary benefits to track time-varying causality graphs in an online fashion. Despite using data in a sequential fashion, both algorithms are shown to asymptotically attain the same average performance as a batch estimator with all data available at once. Moreover, their constant complexity per update renders these algorithms appealing for big-data scenarios. Theoretical and experimental performance analysis support the merits of the proposed algorithms. Remarkably, no probabilistic models or stationarity assumptions need to be introduced, which endows the developed algorithms with considerable generality