Trans RINA, Vol 157, Part A3, Intl J Maritime Eng, Jul-Sep 2015 ANOMALY DETECTION IN VESSEL TRACKING – A BAYESIAN NETWORKS (BNs)


APPROACH (DOI No: 10.3940/rina.ijme.2015.a3.316) D Handayani, Department of Computer Science, Faculty of


Information and Communication Technology,


International Islamic University Malaysia, W Sediono, Department of Mechatronics Engineering, Faculty of Engineering, International Islamic University Malaysia and A Shah, Department of Information Systems, Faculty of Information and Communication Technology, International Islamic University Malaysia


SUMMARY


The paper describes the supervised method approach to identifying vessel anomaly behaviour. The vessel anomaly behaviour is determined by learning from self-reporting maritime systems based on the Automatic Identification System (AIS). The AIS is a real world vessel reporting data system, which has been recently made compulsory by the International Convention for the Safety of Life and Sea (SOLAS) for vessels over 300 gross tons and most commercial vessels such as cargo ships, passenger vessels, tankers, etc. In this paper, we describe the use of Bayesian networks (BNs) approach to identify the behaviour of the vessel of interest. The BNs is a machine learning technique based on probabilistic theory that represents a set of random variables and their conditional independencies via directed acyclic graph (DAG). Previous studies showed that the BNs have important advantages compared to other machine learning techniques. Among them are that expert knowledge can be included in the BNs model, and that humans can understand and interpret the BNs model more readily. This work proves that the BNs technique is applicable to the identification of vessel anomaly behaviour.


1. INTRODUCTION


The number of threats facing vessels at sea, and the security of coastal countries, increases daily in the form of collision, illegal fishing, smuggling, pollution, and piracy. Some of these problems are caused by human actions and some arise from natural causes. For example, in the Straits of Malacca and Singapore, more than 150 vessels a day (over 70,000 vessels annually) transit this strategic and important international waterway. Thus, the International Chamber of Shipping (ICS) believes in placing safety and security as its priority for all nations across the globe [1].


With the advancement of technology in surveillance and the immediate need environment,


for automated solutions become an


better protection of the have


important issue. Such solutions can be applied for detecting anomaly behaviour in moving objects, such as road vehicles, planes and vessels.


Anomaly detection of a massive moving object is one of many techniques for improving environment security, especially in surveillance [2-4]. The pattern of the moving object can become very complex which makes the work more challenging.


One of the sources of vessel movement information is data from the Automatic Identification System (AIS). Maritime surveillance authorities used AIS data to reveal threats to security, for instance, smuggling, illegal trafficking, illegal fishing or other risks. With the amount of information retrieved, the need for an automated system to analyze the vessel behaviour increases.


Some previous work was devoted to research and


development in the area of anomaly detection. In 2011, Etienne Martineau [5] determined that the purposes of


©2015: The Royal Institution of Naval Architects anomaly detection are: for manpower optimization,


support in the decision making process, prediction and early notification, and maintaining a complete and continuous surface picture.


1.1 MANPOWER OPTIMIZATION


The number of vessels at sea grows every year. Aligned with this, the increasing number of pirate attacks has raised the performance expectations for security systems. It becomes more of an issue as countries reduce the number of coastal security staff.


To overcome this issue, it is desirable to combine the strengths of humans and computers. Human reasoning is superior to that of the machine but machines can process massive amount of data in a short period. Therefore, the proposed approach is to let the machine carry out routine and simple tasks leaving the complex problems to be solved by the operator (human) [6, 7]. The combination of human and machine can thus, improve overall performance.


1.2 SUPPORT OF DECISION PROCESSES


The computer is used to support the operator while monitoring the maritime traffic and reduce the operator’s cognitive load. This is to improve the performance of operators, not to fully replace them. The surveillance of large sea areas typically involves the analysis of vast amount


of data, and this cannot be managed and interpreted solely by humans [8].


For this reason, when the operator needs to make decisions, the system will aid the decision process by helping operators interpret the data [9].


A-145


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