Landscape of automated log analysis: A systematic literature review and mapping study
Ł Korzeniowski, K Goczyła - IEEE Access, 2022 - ieeexplore.ieee.org
Logging is a common practice in software engineering to provide insights into working
systems. The main uses of log files have always been failure identification and root cause …
systems. The main uses of log files have always been failure identification and root cause …
A comprehensive survey on identification and analysis of phishing website based on machine learning methods
MH Alkawaz, SJ Steven… - 2021 IEEE 11th IEEE …, 2021 - ieeexplore.ieee.org
Phishing is a cybercrime which is carried out by imitating a legal website to trick users to
steal their personal data, including usernames, passwords, account numbers, national …
steal their personal data, including usernames, passwords, account numbers, national …
Combining k-means and xgboost models for anomaly detection using log datasets
Computing and networking systems traditionally record their activity in log files, which have
been used for multiple purposes, such as troubleshooting, accounting, post-incident …
been used for multiple purposes, such as troubleshooting, accounting, post-incident …
A survey on forensics and compliance auditing for critical infrastructure protection
The broadening dependency and reliance that modern societies have on essential services
provided by Critical Infrastructures is increasing the relevance of their trustworthiness …
provided by Critical Infrastructures is increasing the relevance of their trustworthiness …
An integrated method for anomaly detection from massive system logs
Logs are generated by systems to record the detailed runtime information about system
operations, and log analysis plays an important role in anomaly detection at the host or …
operations, and log analysis plays an important role in anomaly detection at the host or …
Big-data analysis of multi-source logs for anomaly detection on network-based system
Log data are important audit basis to record routine events occurring on computer or
network system, which are also critical data source for detecting system anomalies. By …
network system, which are also critical data source for detecting system anomalies. By …
Multi-source log parsing with pre-trained domain classifier
Automated log analysis with AI technologies is commonly used in network, system, and
service operation and maintenance to ensure reliability and quality assurance. Log parsing …
service operation and maintenance to ensure reliability and quality assurance. Log parsing …
Comparison and detection analysis of network traffic datasets using K-means clustering algorithm
OI Al-Sanjary, MAB Roslan, RAA Helmi… - Journal of Information & …, 2020 - World Scientific
Anomaly detection in specific datasets involves the detection of circumstances that are
common in a homogeneous data. When looking at network traffic data, it is generally difficult …
common in a homogeneous data. When looking at network traffic data, it is generally difficult …
Anomaly Detection in Logs using Deep Learning
A Aziz, K Munir - IEEE Access, 2024 - ieeexplore.ieee.org
Detection of abnormalities is important for the security and reliability of computer systems as
they heavily rely on logs to detect anomalies. The logs provide general information, errors …
they heavily rely on logs to detect anomalies. The logs provide general information, errors …
A Federated Learning Approach for Multi-stage Threat Analysis in Advanced Persistent Threat Campaigns
F Nelles, A Yazdinejad, A Dehghantanha… - arXiv preprint arXiv …, 2024 - arxiv.org
Multi-stage threats like advanced persistent threats (APT) pose severe risks by stealing data
and destroying infrastructure, with detection being challenging. APTs use novel attack …
and destroying infrastructure, with detection being challenging. APTs use novel attack …