Industrial Internet of Things (IIoT) is becoming an attack target of advanced persistent threat (APT). Currently, IIoT logs have not been effectively used for anomaly detection. In this paper, we use blockchain to prevent logs from being tampered with and propose a pvalue-guided anomaly detection approach. This approach uses statistical pvalues to combine multiple heterogeneous log parser algorithms. The weighted edit distance is selected as a score function to calculate the nonconformity score between a log and a predefined event. The pvalue is calculated based on the non-conformity scores which indicate how well a log matches an event. This approach is tested on a large number of real-world HDFS logs and IIoT logs. The experiment results show that abnormal events could be effectively recognized by our pvalue-guided approach.
. As an important branch of log analysis, anomaly detection is widely used in large-scaled distributed systems. It has also formed a complete analysis process including log collection, log parsing, feature extraction and machine learning. Based on different design techniques, anomaly detection models are mainly divided into supervised models and unsupervised models. In order to select an efficient and accurate anomaly detection in practical applications, @cite_18 provide a detailed review and evaluation of 6 state-of-art log anomaly detection methods.
@cite_18
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Anomaly detection plays an important role in managementof modern large-scale distributed systems. Logs, whichrecord system runtime information, are widely used for anomalydetection. Traditionally, developers (or operators) often inspectthe logs manually with keyword search and rule matching. Theincreasing scale and complexity of modern systems, however, make the volume of logs explode, which renders the infeasibilityof manual inspection. To reduce manual effort, many anomalydetection methods based on automated log analysis are proposed. However, developers may still have no idea which anomalydetection methods they should adopt, because there is a lackof a review and comparison among these anomaly detectionmethods. Moreover, even if developers decide to employ ananomaly detection method, re-implementation requires a nontrivialeffort. To address these problems, we provide a detailedreview and evaluation of six state-of-the-art log-based anomalydetection methods, including three supervised methods and threeunsupervised methods, and also release an open-source toolkitallowing ease of reuse. These methods have been evaluated ontwo publicly-available production log datasets, with a total of15,923,592 log messages and 365,298 anomaly instances. Webelieve that our work, with the evaluation results as well asthe corresponding findings, can provide guidelines for adoptionof these methods and provide references for future development. Anomaly detection is a critical step towards building a secure and trustworthy system. The primary purpose of a system log is to record system states and significant events at various critical points to help debug system failures and perform root cause analysis. Such log data is universally available in nearly all computer systems. Log data is an important and valuable resource for understanding system status and performance issues; therefore, the various system logs are naturally excellent source of information for online monitoring and anomaly detection. We propose DeepLog, a deep neural network model utilizing Long Short-Term Memory (LSTM), to model a system log as a natural language sequence. This allows DeepLog to automatically learn log patterns from normal execution, and detect anomalies when log patterns deviate from the model trained from log data under normal execution. In addition, we demonstrate how to incrementally update the DeepLog model in an online fashion so that it can adapt to new log patterns over time. Furthermore, DeepLog constructs workflows from the underlying system log so that once an anomaly is detected, users can diagnose the detected anomaly and perform root cause analysis effectively. Extensive experimental evaluations over large log data have shown that DeepLog has outperformed other existing log-based anomaly detection methods based on traditional data mining methodologies. Detection of execution anomalies is very important for the maintenance, development, and performance refinement of large scale distributed systems. Execution anomalies include both work flow errors and low performance problems. People often use system logs produced by distributed systems for troubleshooting and problem diagnosis. However, manually inspecting system logs to detect anomalies is unfeasible due to the increasing scale and complexity of distributed systems. Therefore, there is a great demand for automatic anomalies detection techniques based on log analysis. In this paper, we propose an unstructured log analysis technique for anomalies detection. In the technique, we propose a novel algorithm to convert free form text messages in log files to log keys without heavily relying on application specific knowledge. The log keys correspond to the log-print statements in the source code which can provide cues of system execution behavior. After converting log messages to log keys, we learn a Finite State Automaton (FSA) from training log sequences to present the normal work flow for each system component. At the same time, a performance measurement model is learned to characterize the normal execution performance based on the log mes-sages’ timing information. With these learned models, we can automatically detect anomalies in newly input log files. Experiments on Hadoop and SILK show that the technique can effectively detect running anomalies. High-dimensional problem domains pose significant challenges for anomaly detection. The presence of irrelevant features can conceal the presence of anomalies. This problem, known as the 'curse of dimensionality', is an obstacle for many anomaly detection techniques. Building a robust anomaly detection model for use in high-dimensional spaces requires the combination of an unsupervised feature extractor and an anomaly detector. While one-class support vector machines are effective at producing decision surfaces from well-behaved feature vectors, they can be inefficient at modelling the variation in large, high-dimensional datasets. Architectures such as deep belief networks (DBNs) are a promising technique for learning robust features. We present a hybrid model where an unsupervised DBN is trained to extract generic underlying features, and a one-class SVM is trained from the features learned by the DBN. Since a linear kernel can be substituted for nonlinear ones in our hybrid model without loss of accuracy, our model is scalable and computationally efficient. The experimental results show that our proposed model yields comparable anomaly detection performance with a deep autoencoder, while reducing its training and testing time by a factor of 3 and 1000, respectively. HighlightsWe use a combination of a one-class SVM and deep learning.In our model linear kernels can be used rather than nonlinear ones.Our model delivers a comparable accuracy with a deep autoencoder.Our model executes 3times faster in training and 1000 faster than a deep autoencoder.