Anomaly Detection for an E-commerce Pricing System

Ramakrishnan, Jayapradha; Shaabani, Elham; Li, Chao

doi:10.1145/3292500.3330748

Cited by 23 publications

(7 citation statements)

References 26 publications

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“…Since its debut, PyOD has been used in various academic and commercial projects. Some examples of the use of PyOD include the detection of anomalies in a large-scale online pricing system at Walmart [ 74 ] and the development of an unsupervised outlier detection framework called DCSO, which has been demonstrated and evaluated for dynamically selecting the most competent base detectors [ 46 ].…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection

Komadina,

Kovačević,

Štengl

et al. 2024

Sensors

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Detecting anomalies in large networks is a major challenge. Nowadays, many studies rely on machine learning techniques to solve this problem. However, much of this research depends on synthetic or limited datasets and tends to use specialized machine learning methods to achieve good detection results. This study focuses on analyzing firewall logs from a large industrial control network and presents a novel method for generating anomalies that simulate real attacker actions within the network without the need for a dedicated testbed or installed security controls. To demonstrate that the proposed method is feasible and that the constructed logs behave as one would expect real-world logs to behave, different supervised and unsupervised learning models were compared using different feature subsets, feature construction methods, scaling methods, and aggregation levels. The experimental results show that unsupervised learning methods have difficulty in detecting the injected anomalies, suggesting that they can be seamlessly integrated into existing firewall logs. Conversely, the use of supervised learning methods showed significantly better performance compared to unsupervised approaches and a better suitability for use in real systems.

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Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection

Komadina,

Kovačević,

Štengl

et al. 2024

Sensors

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show abstract

“…Te selection of abnormal threshold is the key to the detection performance [27]. Referring to the paper [28], we frst obtain a set of test samples with known abnormal labels and obtain all abnormal scores. By drawing the relationship curve between F1-score (including precision rate and recall rate) and the threshold, we fnd that the threshold corresponding to the maximum value of the F1-score is the best threshold ϵ.…”

Section: Mw-cave Anomaly Detectionmentioning

confidence: 99%

Multiworking Conditions Anomaly Detection of Mechanical System Based on Conditional Variational Auto-Encoder

Lei

Dong

et al. 2023

Shock and Vibration

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Existing anomaly detection models of mechanical systems often face challenges for the equipment under multiple working conditions: the learning model under a single working condition is challenging to adapt to new working conditions, and centralized learning of multicondition samples leads to too low detection accuracy. A multiworking condition variational auto-encoder (MW-CVAE) is proposed to solve the problem. Based on the variational auto-encoder model, the working conditions of the equipment are regarded as the input. The anomaly evaluation threshold of each independent working condition is established by the centralized learning of normal multiworking condition samples. At the same time, it is found that the representation of each working condition sample in the space of the hidden layer forms a distribution close to the prior probability, providing a theoretical basis for the separation and evaluation of working conditions. By comparing and verifying the CWRU, JNU, and PU datasets, the new method significantly improves anomaly detection (the F1-score value is increased by 18-19%) and can be widely used in anomaly detection mechanical systems with various discrete working conditions.

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“…Yong Yang [18] introduced an approach to detect anomalies by analyzing the sequence of web access behaviors. In addition, Jagdish et al [19] designed an anomaly detection system in E-commerec systems based on features showing business characteristics such as price, goods, etc. Thes features are also adopted in this paper, but at a more general level and the extraction process of these features is implemented automatically.…”

Section: Related Work 21 Web Attack Detection Researchmentioning

confidence: 99%

An adaptive anomaly request detection framework based on dynamic web application profiles

Xuan

Nguyễn

Dinh

2020

IJECE

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Web application firewall is a highly effective application in protecting the application layer and database layer of websites from attack access. This paper proposes a new web application firewall deploying method based on Dynamic Web application profiling (DWAP) analysis technique. This is a method to deploy a firewall based on analyzing website access data. DWAP is improved to integrate deeply into the structure of the website to increase the compatibility of the anomaly detection system into each website, thereby improving the ability to detect abnormal requests. To improve the compatibility of the web application firewall with protected objects, the proposed system consists of two parts with the main tasks are: i) Detect abnormal access in web application (WA) access; ii) Semi-automatic update the attack data to the abnormal access detection system during WA access. This new method is applicable in real-time detection systems where updating of new attack data is essential since web attacks are increasingly complex and sophisticated.

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Anomaly Detection for an E-commerce Pricing System

Cited by 23 publications

References 26 publications

Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection

Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection

Multiworking Conditions Anomaly Detection of Mechanical System Based on Conditional Variational Auto-Encoder

An adaptive anomaly request detection framework based on dynamic web application profiles

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