IEEE

Deep Learning-Based Autonomous Anomaly Detection for Security in SDN-IoT Networks

Abstract: Converging Software-Defined Networking (SDN) and the Internet of Things (IoT) has directed innovative network architectures and applications. However, this fusion exposes security ...
GitHub

Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

07.2025: Dinomaly has been integrated in Intel open-edge Anomalib in v2.1.0. Great thanks to the contributors for the nice reproduction and integration. Anomalib is a comprehensive library for ...
GitHub

A professional Python project for detecting anomalies in historical stock prices using advanced statistical and machine learning methods. Designed for recruiters, data ...

Professional, modular analysis of historical stock prices using multiple anomaly detection methods (Z-Score, Isolation Forest, DBSCAN, Prophet, Rolling Quantile). Includes multi-ticker comparison, ...
Scientific Research Publishing

Sharma, A., Sahay, R. and Gupta, M. (2020) Deep Learning-Based Intrusion Detection Systems: A Comparative Analysis. Computers & Security, 97, Article 101857.

ABSTRACT: The accelerating sophistication of cyberattacks poses unprecedented challenges for national security, critical infrastructures, and global digital resilience. Traditional signature-based ...
Hacker

An Overview of Log-Based Anomaly Detection Techniques

5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?
IEEE

Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey

Abstract: The rapid expansion of data from diverse sources has made anomaly detection (AD) increasingly essential for identifying unexpected observations that may signal system failures, security ...
Scientific Research Publishing

Munir, M., Siddiqui, S.A., Dengel, A. and Ahmed, S. (2019) DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series. IEEE Access, 7, 1991-2005.

ABSTRACT: The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in ...