A simple and effective outlier detection algorithm for categorical data

Zhao, Xin; Liang, Jiye; Cao, Fuyuan

doi:10.1007/s13042-013-0202-4

Cited by 53 publications

(52 citation statements)

References 23 publications

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“…In our tests, we used the Python language version 3.9.7, along with the Scikit-Learn [37] library and the PyOD [38] library. We used specific functions of the Scikit-Learn library and the PyOD library, and a Python script was developed utilizing these functions in order to perform the training and testing of the four selected novelty detection algorithms.…”

Section: B Training Process Of Novelty Detection Algorithmsmentioning

confidence: 99%

Novelty Detection for Risk-based User Authentication on Mobile Devices

Παπαϊωάννου

Zachos

Μαντάς

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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User authentication acts as the first line of defense verifying the identity of a mobile user, often as a prerequisite to allow access to resources in a mobile device. For several decades, user authentication was based on the ''something the user knows'', known also as knowledge-based user authentication. Recent studies state that although knowledge-based user authentication has been the most popular for authenticating an individual, nowadays it is no more considered secure and convenient for the mobile user as it is imposing several limitations. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Toward this direction, user authentication based on the ''something the user is'' has caught the attention. This category includes authentication methods which make use of human physical characteristics (also referred to as physiological biometrics), or involuntary actions (also referred to as behavioral biometrics). In particular, risk-based user authentication based on behavioral biometrics appears to have the potential to increase mobile authentication security without sacrificing usability. In this context, we, firstly, present an overview of user authentication on mobile devices and discuss risk-based user authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Afterwards, a set of novelty detection algorithms for risk estimation is tested and evaluated to identify the most appropriate ones for risk-based user authentication on mobile devices.

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Section: B Training Process Of Novelty Detection Algorithmsmentioning

confidence: 99%

Novelty Detection for Risk-based User Authentication on Mobile Devices

Παπαϊωάννου

Zachos

Μαντάς

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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“…A common-neighbor-based distance function was developed by Li, Lee, and Lang (2007) to measure the proximity of a pair of data points in distance-based outlier detection method for highdimensional categorical data. A weighted density that takes into account the density and uncertainty of each categorical variable is defined by Zhao, Liang, and Cao (2014).…”

Section: Related Workmentioning

confidence: 99%

Automation of Cleaning and Ensembles for Outliers Detection in Questionnaire Data

Uher

Dráždilová²,

Platoš³

et al. 2022

SSRN Journal

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“…In the literature, there are many techniques for event verification and filtration [13] [15]. One of the most important ones is using outlier detection-based approaches [55]. In this paper, we use local density cluster-based outlier factor (LDCOF) [56], an extension of clustering-based local outlier factor (CBLOF) [57].…”

Section: Event Filtration Componentmentioning

confidence: 99%

Towards event aggregation for reducing the volume of logged events during IKC stages of APT attacks

Ramaki,

Ghaemi-Bafghi,

Rasoolzadegan

2021

Preprint

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Nowadays, targeted attacks like Advanced Persistent Threats (APTs) has become one of the major concern of many enterprise networks. As a common approach to counter these attacks, security staff deploy a variety of security and non-security sensors at different lines of defense (Network, Host, and Application) to track the attacker's behaviors during their kill chain. However, one of the drawbacks of this approach is the huge amount of events raised by heterogeneous security and non-security sensors which makes it difficult to analyze logged events for later processing i.e. event correlation for timely detection of APT attacks. Till now, some research papers have been published on event aggregation for reducing the volume of logged low-level events. However, most research works have been provided a method to aggregate the events of a single-type and homogeneous event source i.e. NIDS. In addition, their main focus is only on the degree to which the event volume is reduced, while the amount of security information lost during the event aggregation process is also very important. In this paper, we propose a three-phase event aggregation method to reduce the volume of logged heterogeneous events during APT attacks considering the lowest rate of loss of security information. To this aim, at first, low-level events of the sensors are clustered into some similar event groups and then, after filtering noisy event clusters, the remained clusters are summarized based on an Attribute-Oriented Induction (AOI) method in a controllable manner to reduce the unimportant or duplicated events. The method has been evaluated on the three publicly available datasets: SotM34, Bryant, and LANL. The experimental results show that the method is efficient enough in event aggregation and can reduce events volume up to 99.7% with an acceptable level of information loss ratio (ILR).Keywords Security event management • Event aggregation • Advanced persistent threat • Intrusion kill chain • Heterogeneous event logs.

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A simple and effective outlier detection algorithm for categorical data

Cited by 53 publications

References 23 publications

Novelty Detection for Risk-based User Authentication on Mobile Devices

Novelty Detection for Risk-based User Authentication on Mobile Devices

Automation of Cleaning and Ensembles for Outliers Detection in Questionnaire Data

Towards event aggregation for reducing the volume of logged events during IKC stages of APT attacks

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