A survey on artificial intelligence techniques for security event correlation: models, challenges, and opportunities

D Levshun, I Kotenko - Artificial Intelligence Review, 2023 - Springer
Abstract Information systems need to process a large amount of event monitoring data. The
process of finding the relationships between events is called correlation, which creates a …

Automatic parsing and utilization of system log features in log analysis: A survey

J Ma, Y Liu, H Wan, G Sun - Applied Sciences, 2023 - mdpi.com
System logs are almost the only data that records system operation information, so they play
an important role in anomaly analysis, intrusion detection, and situational awareness …

Mannose metabolism inhibition sensitizes acute myeloid leukaemia cells to therapy by driving ferroptotic cell death

K Woodley, LS Dillingh, G Giotopoulos… - Nature …, 2023 - nature.com
Resistance to standard and novel therapies remains the main obstacle to cure in acute
myeloid leukaemia (AML) and is often driven by metabolic adaptations which are …

Failure detection using semantic analysis and attention-based classifier model for IT Infrastructure log data

DA Bhanage, AV Pawar, K Kotecha, A Abrahim - IEEE Access, 2023 - ieeexplore.ieee.org
The improvement in the reliability, availability, and maintenance of the IT infrastructure
components is paramount to ensure uninterrupted services in large-scale IT Infrastructures …

Parameter-Efficient Log Anomaly Detection based on Pre-training model and LORA

S He, Y Lei, Y Zhang, K Xie… - 2023 IEEE 34th …, 2023 - ieeexplore.ieee.org
Logs record both the normal and abnormal system operating status at any time, which are
crucial data during system operation. Log anomaly detection can help with system …

STEAMCODER: Spatial and temporal adaptive dynamic convolution autoencoder for anomaly detection

P Xu, H Gan, H Fu, Z Zhang - Knowledge-Based Systems, 2023 - Elsevier
The anomaly detection algorithm greatly improves the reliability of equipment operation.
Traditional anomaly detection algorithms are mostly designed for large data sets, making it …

Software Failure Log Analysis for Engineers

W Dobrowolski, M Nikodem, O Unold - Electronics, 2023 - mdpi.com
The use of automated methods for log analysis is unavoidable in any large company;
therefore, it has attracted attention from engineers and researchers. As a result, the number …

[PDF][PDF] Fine-grained multivariate time series anomaly detection in iot

S He, M Guo, B Yang, O Alfarraj, A Tolba… - Computers, Materials …, 2023 - aura.abdn.ac.uk
Sensors produce a large amount of multivariate time series data to record the states of
Internet of Things (IoT) systems. Multivariate time series timestamp anomaly detection …

ETCNLog: A System Log Anomaly Detection Method Based on Efficient Channel Attention and Temporal Convolutional Network

Y Chang, N Luktarhan, J Liu, Q Chen - Electronics, 2023 - mdpi.com
The scale of the system and network applications is expanding, and higher requirements are
being put forward for anomaly detection. The system log can record system states and …

An automatic anomaly application detection system in mobile devices using FL-HTR-DBN and SKLD-SED K means algorithms

R Lakshmana Kumar, S Jayanthi… - Journal of Intelligent …, 2023 - content.iospress.com
The proliferation of mobile technology has given rise to a multitude of applications, among
them those designed with malicious intent, aimed at compromising the integrity of mobile …