Enhancing profiles for anomaly detection using time granularities

被引：0

作者：

Li, Yingjiu ^{[1
]}

Wu, Ningning ^{[1
]}

Sean Wang, X. ^{[1
]}

Jajodia, Sushil ^{[1
]}

机构：

[1] Ctr. for Secure Information Systems, George Mason University, Fairfax, VA 22030, United States

来源：

Journal of Computer Security | 2002年 / 10卷 / 1-2期

关键词：

Algorithms - Data reduction - Network protocols - Security of data;

D O I：

10.3233/JCS-2002-101-206

中图分类号：

学科分类号：

摘要：

Recently, association rules have been used to generate profiles of normal behavior for anomaly detection. However, the time factor (especially in terms of multiple time granularities) has not been utilized extensively in generation of these profiles. In reality, user behavior during different time intervals may be very different. For example, the normal number and duration of FTP connections may vary from working hours to midnight, from business day to weekend or holiday. Furthermore, these variations may depend on the day of the month or the week. This paper proposes to build profiles using temporal association rules in terms of multiple time granularities, and describes algorithms to discover these profiles. Because multiple time granularities are used for the profile generation, the proposed method is more flexible and precise than previous methods that use fixed partition of time intervals. Finally, the paper describes an experiment and its preliminary result on TCP-dump data.

引用

页码：137 / 157

共 50 条

[21] Anomaly Detection in Time Series Data Using Reversible Instance Normalized Anomaly Transformer
Baidya, Ranjai
Jeong, Heon
SENSORS, 2023, 23 (22)
[22] Enhancing Sound-Based Anomaly Detection Using Deep Denoising Autoencoder
Kim, Seong-Mok
Soo Kim, Yong
IEEE ACCESS, 2024, 12 : 84323 - 84332
[23] Design and evaluation of a dynamic anomaly detection scheme using the age of user profiles
Bae, Ihn-Han
Lee, Hwa-Ju
Lee, Kyung-Sook
FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 3, PROCEEDINGS, 2007, : 136 - 140
[24] Time series forecasting and anomaly detection using deep learning
Iqbal, Amjad
Amin, Rashid
COMPUTERS & CHEMICAL ENGINEERING, 2024, 182
[25] Calendars, time granularities, and automata
Dal Lago, U
Montanari, A
ADVANCES IN SPATIAL AND TEMPORAL DATABASES, PROCEEDINGS, 2001, 2121 : 279 - 298
[26] Adaptive Threshold for Anomaly Detection Using Time Series Segmentation
Dani, Mohamed-Cherif
Jollois, Francois-Xavier
Nadif, Mohamed
Freixo, Cassiano
NEURAL INFORMATION PROCESSING, PT III, 2015, 9491 : 82 - 89
[27] A Novel Hierarchical Detection Method for Enhancing Anomaly Detection Efficiency
Kim, Eunhye
Kim, Sehun
2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (CICN), 2015, : 1018 - 1022
[28] Time of Day Anomaly Detection
Price-Williams, Matthew
Turcotte, Melissa
Heard, Nick
2018 EUROPEAN INTELLIGENCE AND SECURITY INFORMATICS CONFERENCE (EISIC), 2018, : 1 - 6
[29] Enhancing Anomaly Detection in Multivariate Time Series with Stacked Transformer Encoders and Adaptive Positional Embeddings
Sowmya, Kella
Ramesh, K.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2024,
[30] Enhancing business cycle analysis by integrating anomaly detection and components decomposition of time series data
Kyo, Koki
STATISTICAL METHODS AND APPLICATIONS, 2025,

← 1 2 3 4 5 →