Multi-task Feature Selection based Anomaly Detection
Network anomaly detection is still a vibrant research area. As the fast growth of network bandwidth and the tremendous traffic on the network, there arises an extremely challengeable question: How to efficiently and accurately detect the anomaly on multiple traffic? In multi-task learning, the traffic consisting of flows at different time periods is considered as a task. Multiple tasks at different time periods performed simultaneously to detect anomalies. In this paper, we apply the multi-task feature selection in network anomaly detection area which provides a powerful method to gather information from multiple traffic and detect anomalies on it simultaneously. In particular, the multi-task feature selection includes the well-known l1-norm based feature selection as a special case given only one task. Moreover, we show that the multi-task feature selection is more accurate by utilizing more information simultaneously than the l1-norm based method. At the evaluation stage, we preprocess the raw data trace from trans-Pacific backbone link between Japan and the United States, label with anomaly communities, and generate a 248-feature dataset. We show empirically that the multi-task feature selection outperforms independent l1-norm based feature selection on real traffic dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly Detectionfeature selectionMulti-Task LearningSimilar Papers 제목 키워드 기반
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
Self-supervised feature reconstruction methods have shown promising advances in industrial image anomaly detection and localization. Despite this progress, these methods still face challenges in synthesizing realistic an…
Anomaly Detectionfeature selectionFeature Selection for Fault Detection and Prediction based on Event Log Analysis
Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection, log parsing, feature extraction, and an…
Anomaly DetectionFault Detectionfeature selectionLog Parsing+1Quantum Autoencoders for Anomaly Detection in Cybersecurity
Anomaly detection in cybersecurity is a challenging task, where normal events far outnumber anomalous ones with new anomalies occurring frequently. Classical autoencoders have been used for anomaly detection, but struggl…
Anomaly DetectionM3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising
Existing industrial anomaly detection methods primarily concentrate on unsupervised learning with pristine RGB images. Yet, both RGB and 3D data are crucial for anomaly detection, and the datasets are seldom completely c…
Anomaly DetectionDenoisingModel Selection of Anomaly Detectors in the Absence of Labeled Validation Data
Anomaly detection is the task of identifying abnormal samples in large unlabeled datasets. While the advent of foundation models has produced powerful zero-shot anomaly detection methods, their deployment in practice is …
Anomaly DetectionModel SelectionUnsupervised Anomaly Detectionzero-shot anomaly detection