Unsupervised learning limitations: Many traditional anomaly detection techniques are unsupervised, meaning they do not depend on labeled data for training. This can make it challenging to distinguish between normal variations and actual fraud, resulting in higher false positive rates. Model sensitivity: A...
Wang C, Zhu H (2022) Wrongdoing Monitor: A Graph-Based Behavioral Anomaly Detection in Cyber Security. IEEE Trans Inf Forensics Secur 17:2703–2718 Article MATH Google Scholar Kim J, In Y, Yoon K, Lee J, Park C (2023) Class Label-aware Graph Anomaly Detection. In: CIKM. ACM; p. ...
This article focuses on dueling AI algorithms designed to investigate the trustworthiness of power systems’ cyber-physical security under various scenarios using the phasor measurement units (PMU) use case. Particularly in PMU operations, the focus is on areas that manage sensitive data vital to ...
Empty Cell[20]Cluster of high correlated sensorsExperimental data from hydraulic test equipmentDetect faults in several equipments, enable the analysis of physical meaning of anomalies Empty Cell[35]Fuzzy clustering technique Polynomial regressionReal data from wind generatorRemove incoherent data from wind...
Since the semantic meaning of each parameter is known after parsing, the values in each vector can be analyzed with methods that are appropriate for the respective value types, e.g., numeric or categorical. One special parameter of log events is the time stamp as it allows to put event ...
According to the attack method proposed in the literature13, this paper makes the following assumptions on the poisoning party. The malicious nodes are non-colluding, meaning their updates have limited impact on the global model. The data are distributed among clients in an I.I.D. manner, ...
Anomaly detection is the identification of data points, items, observations or events that do not conform to the expected pattern of a given group. These anomalies occur very infrequently but may signify a large and significant threat such as cyber intrusions or fraud. ...
Abstract:This paper presentsthe special meaning of DDoS Attack and some anomaly detection method to deal with it.And compared different methods with each other.Finally,discussed the existing problems and the future direction in this field. Keywords:DDoS attack, attack detection, network. 1.Introductio...
Score ranges are assumed non-linear and relative, their meaning established by weighting the whole dataset (or a dataset model). While this is perfectly true, algorithms also impose dynamics that decisively affect the meaning of outlierness scores. In this work, we aim to gain a better ...
Anomaly detection has been used in a wide variety of applications (Chandola et al. 2009; Patcha and Park 2007; Hodge and Austin 2004), such as network intrusion detection for cyber-security (Dokas et al. 2002; Yamanishi et al. 2004), fraud detection for credit cards (Aleskerov et al....