Detecting Anomalous Maritime Container Itineraries for Anti-fraud and Supply Chain Security

Camossi, Elena; Dimitrova, Tatyana; Tsois, Aris

doi:10.1109/eisic.2012.39

Cited by 17 publications

(9 citation statements)

References 14 publications

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“…Detecting anomalous container itineraries are important for implementing anti-fraud measures and ensuring supply chain security. Container Status Messages are used to successfully discover irregular container shipments, by using a simple SVM classifier (Camossi et al, 2012).…”

Section: Inventory and Logisticsmentioning

confidence: 99%

AI-enabled Enterprise Information Systems for Manufacturing

Zdravković

Panetto

Weichhart

2021

Enterprise Information Systems

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Research work presented in this paper considers the contemporary Enterprise Information Systems (EIS) functional architecture and carries out the literature review of AI applications integrated in Customer Relationship Management, Supply Chain Management, Inventory and logistics, Production Planning and Scheduling, Finance and accounting, Product Lifecycle Management and Human Resources, with special attention to the manufacturing enterprises. As a review outcome, the enhanced capabilities are identified in each of those processes and proposed as AI services. AI-enablement is related to the capabilities of the enterprise to implement improved decision making or automation by using (a) Machine Learning (ML) models or (b) logicbased systems. It is a process of the enterprise transformation leading to the convergence of the 4 major disruptive technologies, namely Industrial Internet of Things (or Cyber-Physical Systems), Agent-based Distributed Systems, Cloud Computing and Artificial Intelligence (non-symbolic AI like Machine Learning, Neural Networks; symbolic AI like Reasoning and Logic/Model-based AI).

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Section: Inventory and Logisticsmentioning

confidence: 99%

AI-enabled Enterprise Information Systems for Manufacturing

Zdravković

Panetto

Weichhart

2021

Enterprise Information Systems

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“…They conducted an experiment to verify the proposed anomaly detection algorithm using actual taxi operation records in China. Camossi et al [48] suggested an algorithm for detecting the transportation of suspicious containers in the transportation schedules by learning general container transportation schedules using SVM. Then they conducted an experiment to verify the algorithm by using the container transportation records for a period of 3 years.…”

Section: Anomaly Detection Beyond the Business Processmentioning

confidence: 99%

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

2019

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Featured Application: The proposed framework is capable of analyzing the flow of numerous objects in real time on the basis of a new process anomaly detection approach that considers the manufacturing process of an autonomous and adaptive smart factory.Abstract: Factories of the future are foreseen to evolve into smart factories with autonomous and adaptive manufacturing processes. However, the increasing complexity of the network of manufacturing processes is expected to complicate the rapid detection of process anomalies in real time. This paper proposes an architecture framework and method for the implementation of the Scalable On-line Anomaly Detection System (SOADS), which can detect process anomalies via real-time processing and analyze large amounts of process execution data in the context of autonomous and adaptive manufacturing processes. The design of this system architecture framework entailed the derivation of standard subsequence patterns using the PrefixSpan algorithm, a sequential pattern algorithm. The anomalies of the real-time event streams and derived subsequence patterns were scored using the Smith-Waterman algorithm, a sequence alignment algorithm. The excellence of the proposed system was verified by measuring the time for deriving subsequence patterns and by obtaining the anomaly scoring time from large event logs. The proposed system succeeded in large-scale data processing and analysis, one of the requirements for a smart factory, by using Apache Spark streaming and Apache Hbase, and is expected to become the basis of anomaly detection systems of smart factories.

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“…To be able to apply proposals like [6,19] to use CTI in Route-based Risk Indicators (RRIs), there is the need to develop algorithms to obtain this information from commonly existing data sources. In [2], CSMs were used to infer CTI records following a decision-tree like process. In [17] basic information on the container trip (origin, first port, last port and destination) is extracted from bill of lading documents [9].…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately, this information is often noisy, incomplete and non-standardized, requiring elaborated algorithms to remove the noise and improve the quality of the information. The works in [2][3][4]18] focus on how CSM records can be processed in order to extract useful information on the routes of the goods and assist the Customs risk management processes. The key element proposed on them to describe the route of the goods is the Container-Trip Information (CTI) [4,18].…”

Section: Introductionmentioning

confidence: 99%

A Quality Assessment Framework for Large Datasets of Container-Trips Information

Makridis

Fidalgo-Merino

Cotelo-Lema

et al. 2016

Computer Information Systems and Industrial Management

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Part 10: MiscellanousInternational audienceCustoms worldwide are facing the challenge of supervising huge volumes of containerized trade arriving to their country with resources allowing them to inspect only a minimal fraction of it. Risk assessment procedures can support them on the selection of the containers to inspect. The Container-Trip information (CTI) is an important element for that evaluation, but is usually not available with the needed quality. Therefore, the quality of the computed CTI records from any data sources that may use (e.g. Container Status Messages), needs to be assessed. This paper presents a quality assessment framework that combines quantitative and qualitative domain specific metrics to evaluate the quality of large datasets of CTI records and to provide a more complete feedback on which aspects need to be revised to improve the quality of the output data. The experimental results show the robustness of the framework in highlighting the weak points on the datasets and in identifying efficiently cases of potentially wrong CTI records

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Detecting Anomalous Maritime Container Itineraries for Anti-fraud and Supply Chain Security

Cited by 17 publications

References 14 publications

AI-enabled Enterprise Information Systems for Manufacturing

AI-enabled Enterprise Information Systems for Manufacturing

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

A Quality Assessment Framework for Large Datasets of Container-Trips Information

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