Including the experts knowledge about suspicious activities in the detection process can result in improved ad. Visualization techniques allow for the identification of geographical clusters, rule based expert systems enable the identification of patterns and maritime anomaly detection, while databases for statistical analysis help identify historical trends. Improving maritime anomaly detection and situation awareness through interactive visualization. Part of the lecture notes in computer science book series lncs. Datadriven detection and contextbased classification of. A huge innovation in data science over the past five years has been the ascendance of neural network models, rebranded as deep learning models, over symbolic, rulebased expert systems. In this article, we propose a rulebased method for data integrity assessment, with rules built from the system technical specifications and by domain experts, and formalised by a logicbased framework, resulting in the triggering of situation. On the other hand, maritime domain experts have the required knowledge and experience for finding maritime anomalies. Nowadays, machine learning based data analysis and mining techniques is a natural choice for this type of task. Realtime maritime traffic anomaly detection based on. May 19, 2015 we then developed an anomaly detection algorithm based on this model in which an indicator is used to evaluate suspicious behavior and scores trajectory behavior according to the defined outlying features. Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior.
A prototype for a rulebased expert system based on the maritime domain ontologies was developed by edlund et al. Grid size optimization for potential field based maritime anomaly detection. The planned and purposing vessel movement should generate highlycorrelated ais data, and this can be used for movement anomaly detection. Feature extraction for anomaly detection in maritime. Associated to any study, a normality must be established as the assessment of an anomalous thing is relative, and a distance must be chosen for distance computation. In essence we use ripple down rules to partition a domain, and add new partitions as new. The transit of goods occurs over the oceans that cover 23s of the planet and yet are inhabited by human beings.
Categorization of maritime anomalies for notification and alerting purpose. In this article, we propose a rule based method for data integrity assessment, with rules built from the system technical specifications and by domain experts, and formalised by a logic based framework, resulting in the triggering of situation. An activity has thus been undertaken to implement, within the ckef, a proofofconcept prototype of a rule based expert system to support the analysts regarding this aspect. Roy, j rulebased expert system for maritime anomaly detection. Visualization techniques allow for the identification of geographical clusters, rulebased expert systems enable the identification of patterns and maritime anomaly detection, while databases for statistical analysis help identify historical trends. Maritime domain awareness mda is the effective understanding of activities, events and threats in the maritime environment that could impact global safety, security, economic activity or the environment. Automated vessel anomaly detection is immensely important for preventing and reducing illegal activities e. By gradually adjusting the limits, the system will improve its ability to recognize conditions that identify risks for casting defects. Anomaly detection based on sensor data in petroleum. Anomaly detection and machine learning methods for network. Deepmind beating lee sedol at go, as well as the use of neural networks to solve important fundamental ai tasks such as image recognition, which is. This is achieved through the exploitation of techniques from the areas of machine learning and anomaly detection. Fastmaritime anomaly detection using kdtreegaussian processes.
Airborne behaviour monitoring using gaussian processes. Interactive visualization applications for maritime anomaly. We then developed an anomaly detection algorithm based on this model in which an indicator is used to evaluate suspicious behavior and scores trajectory behavior according to the defined outlying features. A comparative evaluation of anomaly detection algorithms for maritime vi deo surveillance bryan auslander 1, kalyan moy gupta 1. Jasinevicius and petrauskas 9 also used a rule based expert map.
Experiment results demonstrate that the proposed mtmad framework is capable of effectively detecting anomalies in maritime trajectories. Obtaining maritime anomaly data can be difficult or even impractical. The input to our overall anomaly detection system is a time series signature such as the current vs. Maritime domain operatorsanalysts have a mandate to be aware of all that is happening within their areas of responsibility.
Cambridge core institutional access books catalogue individuals cambridge english. Anomaly detection in oceans is a priority for governmental organizations. The definitions of rulebased system depend almost entirely on expert systems, which are system that mimic the reasoning of human expert in solving a. Feature extraction for anomaly detection in maritime trajectories. Anomaly detection in maritime data based on geometrical. The automated identification system of vessel movements receives a huge. In particular, we examine hierarchical task network htn and case based algorithms for plan recognition, which detect anomalies by generating expected behaviors for use as a basis for threat detection. However, we need to be wary of the pitfalls of rulebased anomaly pattern detection. Sep 17, 2009 ebusiness technologies ebtech introduction to rulebased applications adrian giurca, ebusiness technologies, craiova, march 2009 dr. Interactive visualization applications for maritime. Signature based detection systems such as snort have been widely deployed by enterprises for network security, but are limited by the scaling factors described above. Learning states and rules for time series anomaly detection.
In rule based expert systems, knowledge base is also called production memory as rules in the form of ifthen are called productions. For a rule and framebased integration, it composes of the following key features. In particular, we examine hierarchical task network htn and casebased algorithms for plan recognition, which detect anomalies by generating expected behaviors for use as a basis for threat detection. Therefore, we use a generative approach to vary and control the difficulty of anomaly detection tasks. Roy 8 proposed a rule based expert system implementing automated rule based reasoning in support of maritime anomaly detection. Network based anomaly detection algorithms depend only on data which is collected from network devices like firewalls, routers, intrusion prevention systems ips, etc. Anomaly detection is the identification of data points, items, observations or events that do not conform to the expected pattern of a given group. Realtime maritime traffic anomaly detection based on sensors. Object classes, slot attributes, inheritance relations. A framework for anomaly detection in maritime trajectory. To help governments with this task, since 2004, the international maritime organization imo requires automatic identi. Sciforum preprints scilit sciprofiles mdpi books encyclopedia.
The definitions of rule based system depend almost entirely on expert systems, which are system that mimic the reasoning of human expert in solving a knowledge intensive problem. Proceedings paper anomaly detection in the maritime domain. In proceedings of spiethe international society for optical engineering, usa. A complex event processing approach to detect abnormal. Knnlpe performs global densitybased anomaly detection. Anomaly detection using the knowledgebased temporal abstraction method asaf shabtai dept. Maritime anomaly detection using gaussian process active. On the other hand, a limited number of analyzed data points means realtime calculation and decision making. While the rule based approach is conceptually simple and. The system is able to identify a number of basic spatial and kinematical relations between objects, and then deduce different situations, e. Event detection anomaly detection lof time series marine systems.
A rule based system uses rules as the knowledge representation for knowledge coded into the system 4 1416171820. A prototype for a rule based expert system based on the maritime domain ontologies was developed by edlund et al. Maritime anomaly detection using gaussian process active learning. Theres a lot of hype and headline around this stuff just now. An activity has thus been undertaken to implement, within the ckef, a proofofconcept prototype of a rulebased expert system to support the analysts regarding this aspect. Improving maritime anomaly detection and situation. This allows encapsulating knowledge and expansion of the expert system done in a a easy way. We compare their performance with a behavior recognition algorithm on simulated riverine maritime traffic. Open data for anomaly detection in maritime surveillance. Knowledge elicitation from experts, rules formulation or just in general, ex.
This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. With over 30 years of cbrn detection experience, bruker has developed a unique capability in. Maritime security and anomaly detection bigdataocean. A rule based fuzzy expert system was illustrated by jasinevicius, r. Elements of a rule based expert system artificial intelligence. Seecoast applies rulebased and learningbased pattern recognition algorithms to alert illegal. Event detection in marine time series data springerlink. The abnormal vessel movement can be defined as an unreasoned movement deviation from the sea lanes, trajectory, speed or other traffic parameters. The primary concern of this thesis is to investigate automated methods of anomaly detection within vessel track data.
Anomaly detection using the knowledgebased temporal. An activity has thus been undertaken to implement, within the ckef, a proofofconcept prototype of a rulebased expert system to support the. The output of the overall system is a set of rules that implement state transition logic on an. False alarm reduction in maritime anomaly detection with. A fuzzy expert system introduced by jasinevicius and petrauskas 3 that takes into account the vessel type. A comparative evaluation of anomaly detection algorithms. A rulebased fuzzy expert system was illustrated by jasinevicius, r.
This quality makes pointbased anomaly detection techniques attractive for realtime tasks. Roy 8 proposed a rulebased expert system implementing automated rulebased reasoning in support of maritime anomaly detection. An exception is the work presented in 14, where a solution based on bayesian. In this respect, anomaly detection methods are needed to ensure an assessment of such systems. Knowledge based anomaly detection unsworks unsw sydney. The output of the overall system is a set of rules that implement state transition logic on an expert system, and are able to determine if other time series signatures deviate significantly. These anomalies occur very infrequently but may signify a large and significant threat such as cyber intrusions or fraud. Anomaly detection david aha, naval research laboratory, usa 10. Jasinevicius and petrauskas 9 also used a rulebased expert map. Rulebased anomaly pattern detection for detecting disease. Anomaly detection is an important part of datarelated studies and is often based on aforementioned data quality dimensions. Learning states and rules for time series anomaly detections.
Fastmaritime anomaly detection using kdtreegaussian. Topology preserving mapping for maritime anomaly detection. There has been an increasing interest for anomaly detection within the maritime domain in recent years. Designed, configured and tested to be used in the extreme. The input to our overall anomaly detection system is normal time series data like the graph at the top left corner of figure 1. Its applicability has been demonstrated in several publications, examining its scalability, modeling capabilities and detection performance. Detection of anomalies of hybrid ruleframebased expert.
Feature extraction for anomaly detection in maritime trajectories joel sundholm masters thesis at csc. Automatic identification system ais, anomaly detection, bayesian network, maritime environment, situational awareness, threat assessment, white shipping. Rulebased expert system for maritime anomaly detection. Airborne behaviour monitoring using gaussian processes with. Host based anomaly detection systems can include programs running on individual computers, which allows for more features to be added to the anomaly detection system. Science, princeton university, princeton, nj 08544 duf. The proposed potential field based method has been examined using a webbased anomaly detection system strand seafaring transport anomaly detection implemented for this study. Learning maritime traffic rules using potential fields. A comparative evaluation of anomaly detection algorithms for. Anomaly detection is heavily used in behavioral analysis and other forms of. Adrian giurca brandenbu slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Data integrity assessment for maritime anomaly detection.
Download citation rulebased expert system for maritime anomaly detection maritime domain operatorsanalysts have a mandate to be aware of all that is. Rulebased expert system for maritime anomaly detection nasaads. These limits are stored in a database for alloys and are used in the condition part of the rule based expert system. Study of automatic anomalous behaviour detection techniques for.
While the rulebased approach is conceptually simple and easy to implement, it. Laxhammar 6 uses a gaussian mixture model for maritime anomaly detection while johansson and falkman 7 use a bayesian network. Anomaly detection and machine learning methods for. Rule based expert system for maritime anomaly detection.