Tracking fishing vessels individually plays a pivotal role in fisheries monitoring, control, and surveillance. Fishing trip is the most appropriate granularity level to study routine fishing activity patterns. Since self-reported information about fishing vessel trips is notoriously unreliable, we propose here TripTracker, an unsupervised learning approach to partition raw trajectories of ships and boats engaging in fishing into trips and identify trip types. TripTracker first partitions a fishing trip into micro-activities, then uses cluster analysis to confirm the microactivity type. Next, it employs multiple Hidden Markov Models to partition the trip into segments, each of which representing a routine activity. Finally, TripTracker utilizes maritime contextual information to differentiate various fishing trip types, revealing actionable knowledge about vessel activities and their operations. Our experimental evaluation on a large real-world fishing vessel trajectory dataset, confirms TripTracker’s practicability and effectiveness for enhancing maritime domain awareness.

Unsupervised domain adaptation is the problem of transferring extracted knowledge from a labeled source domain to an unlabeled target domain. To achieve discriminative domain adaptation recent studies take advantage of target sample pseudo-labels to impose class-aware distribution alignment across the source and target domains. Still, they have some shortcomings such as making decisions based on inaccurate pseudo-labeled samples that mislead the adaptation process. In this paper, we propose a progressive deep feature alignment, called Norma, to tackle class-aware unsupervised domain adaptation for image classification by enforcing inter-class compactness and intra-class discrepancy through a hybrid learning process. To this end, Norma's optimization process is defined based on a novel triplet loss which not only addresses soft prototype alignment but also pushes away multiple negative centroids. Also, to extract maximum discriminative domain knowledge per iteration, we propose a joint positive and negative learning procedure along with an uncertainty-guided progressive pseudo-labeling on the basis of prototype-based clustering and conditional probability. Our experimental results on several benchmarks demonstrate that Norma outperforms the state-of-the-art methods.

The impact of marine life on the oceans of our planet is undeniable and overfishing is a serious threat to marine ecosystems worldwide. Maritime domain awareness calls for continuous monitoring and tracking of fisheries using data from maritime intelligence sources to detect illegal fishing activities. Marine traffic data from vessel tracking services is a promising source for identifying, locating, and capturing vessel information. Given the volume of such data, manual processing is impossible, raising an immediate need for autonomous and smart systems to follow the footprints of vessels and detect their activity types in near real-time. To achieve this goal, we propose FishNET, a simple yet effective convolutional neural network (CNN) model for vessel trajectory classification. The model is trained using a set of invariant spatiotemporal feature sequences extracted from the behavioral characteristics of vessel movements.

In this paper we propose a data-driven approach to detection and tracking of dark fishing in high-volume marine traffic datasets from vessel tracking services. Dark fishing refers to stealthy fishing operations by vessels trying to hide their illicit activities related to various forms of illegal fishing-one of the most serious threats to world fisheries and fish populations worldwide as well as to global food security. Our approach builds on profiling and ranking fishing vessels by analyzing their routine operations over extended time periods to uncover abnormal activity patterns associated with dark fishing. The focus is on vessel movement patterns rendered as a trajectory with defined starting and endpoints such as ports and known anchorage locations. Specifically, we analyze scenarios where the fishing pattern, with the fishing gear in the water, is obscured in a vessel's reported trip data. Our experimental evaluation, using a large dataset of fishing vessel trajectories from coastal waters of North America, shows the effectiveness and efficiency of the proposed method in differentiating between suspicious and normal fishing vessels irrespective of the vessel type.

Intelligent monitoring and control of critical infrastructure such as electric power grids, public water utilities and transportation systems produces massive volumes of time series data from heterogeneous sensor networks. Time Series Forecasting (TSF) is essential for system safety and security, and also for improving the efficiency and quality of service delivery. Being highly dependent on various external factors, the observed system behavior is usually stochastic, which makes the next value prediction a tricky and challenging task that usually needs customized methods. In this paper we propose a novel deep learning based framework for time series analysis and prediction by ensembling parametric and nonparametric methods. Our approach takes advantage of extracting features at different time scales, which improves accuracy without compromising reliability in comparison with the state-of-the-art methods. Our experimental evaluation using real-world SCADA data from a municipal water management system shows that our proposed method outperforms the baseline methods evaluated here.

Considering the fact that fully immunizing critical infrastructure such as water supply or power grid systems against physical and cyberattacks is not feasible, it is crucial for every public or private sector to invigorate the detective, predictive, and preventive mechanisms to minimize the risk of disruptions, resource loss or damage. This paper proposes a methodical approach to situation analysis and anomaly detection in SCADA-based water supply systems. We model normal system behavior as a hierarchy of hidden semi-Markov models, forming the basis for detecting contextual anomalies of interest in SCADA data. Our experimental evaluation on real-world water supply system data emphasizes the efficacy of our method by significantly outperforming baseline methods.

Automated vessel anomaly detection is immensely important for preventing and reducing illegal activities (e.g., drug dealing, human trafficking, etc.) and for effective emergency response and rescue in a country's territorial waters. A major limitation of previously proposed vessel anomaly detection techniques is the high rate of false alarms as these methods mainly consider vessel kinematic information which is generally obtained from AIS data. In many cases, an anomalous vessel in terms of kinematic data can be completely normal and legitimate if the "context" at the location and time (e.g., weather and sea conditions) of the vessel is factored in. In this paper, we propose a novel anomalous vessel detection framework that utilizes such contextual information to reduce false alarms through "contextual verification". We evaluate our proposed framework for vessel anomaly detection using massive amount of real-life AIS data sets obtained from U.S. Coast Guard. Though our study and developed prototype is based on the maritime domain the basic idea of using contextual information through "contextual verification" to filter false alarms can be applied to other domains as well.

Maritime domain awareness is critical for protecting sea lanes, ports, harbors, offshore structures like oil and gas rigs and other types of critical infrastructure against common threats and illegal activities. Typical examples range from smuggling of drugs and weapons, human trafficking and piracy all the way to terror attacks. Limited surveillance resources constrain maritime domain awareness and compromise full security coverage at all times. This situation calls for innovative intelligent systems for interactive situation analysis to assist marine authorities and security personal in their routine surveillance operations. In this article, we propose a novel situation analysis approach to analyze marine traffic data and differentiate various scenarios of vessel engagement for the purpose of detecting anomalies of interest for marine vessels that operate over some period of time in relative proximity to each other. We consider such scenarios as probabilistic processes and analyze complex vessel trajectories using machine learning to model common patterns. Specifically, we represent patterns as left-to-right Hidden Markov Models and classify them using Support Vector Machines. To differentiate suspicious activities from unobjectionable behavior, we explore fusion of data and information, including kinematic features, geospatial features, contextual information and maritime domain knowledge. Our experimental evaluation shows the effectiveness of the proposed approach using comprehensive real-world vessel tracking data from coastal waters of North America.

Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper we present CRIMETRACER, a personalized random walk based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behavior of known offenders within their activity space. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently commit opportunistic crimes and serial violent crimes by taking advantage of opportunities they encounter in places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CRIMETRACER outperforms all other methods used for location recommendation we evaluate here.

Crime reduction and prevention strategies are essential to increase public safety and reduce the crime costs to society. Law enforcement agencies have long realized the importance of analyzing co-offending networks---networks of offenders who have committed crimes together---for this purpose. Although network structure can contribute significantly to co-offence prediction, research in this area is very limited. Here we address this important problem by proposing a framework for co-offence prediction using supervised learning. Considering the available information about offenders, we introduce social, geographic, geo-social and similarity feature sets which are used for classifying potential negative and positive pairs of offenders. Similar to other social networks, co-offending networks also suffer from a highly skewed distribution of positive and negative pairs. To address the class imbalance problem, we identify three types of criminal cooperation opportunities which help to reduce the class imbalance ratio significantly, while keeping half of the co-offences. The proposed framework is evaluated on a large crime dataset for the Province of British Columbia, Canada. Our experimental evaluation of four different feature sets show that the novel geo-social features are the best predictors. Overall, we experimentally show the high effectiveness of the proposed co-offence prediction framework. We believe that our framework will not only allow law enforcement agencies to improve their crime reduction and prevention strategies, but also offers new criminological insights into criminal link formation between offenders.

In this paper, we analyze co-offending networks derived from a large real-world crime dataset for the purpose of identifying organized crime structures and their constituent entities. We focus on methodical and analytical aspects in using social network analysis methods and data mining techniques. The goal of our work is to promote computational co-offending network analysis as an effective means for extracting information about criminal organizations from large real-life crime datasets, specifically police-reported crime data. We contend that it would be virtually impossible to obtain such information by using traditional crime analysis methods. For our approach we provide an experimental evaluation with promising results.

Individuals frequently have routine daily activities that require commuting between several places, such as their home, work, shopping centres and recreational facilities. According to Crime Pattern Theory, offenders most likely commit opportunistic crimes, including serial and violent crimes, within their Activity Space, that is the space that they visit most frequently during the course of their daily routine activities, since they are aware of the opportunities and risks within these spaces. However, others within the social network of an offender can introduce the offender to new opportunities outside of his Activity Space, a phenomenon that Crime Pattern Theory does not address. This paper explores an important and interesting question about social networks: What is the relation between social and spatial distance of actors? We study this question in the context of crime and co-offending networks to better understand the impact of an offender's social network on the Activity Space of the offender. Our experiments on real-life crime data show that there is a strong correlation between social and spatial distance of offenders: offenders who are socially close are also spatially close.

A co-offending network is a network of offenders who have committed crimes together. Recently different researches have shown that there is a fairly strong concept of network among offenders. Analyzing these networks can help law enforcement agencies in designing more effective strategies for crime prevention and reduction. One of the important tasks in co-offending network analysis is central actors identification. In this paper, firstly we introduce a data model, called unified crime data model to bridge the conceptual gap between abstract crime data level and co-offending network mining level. Using this data model, we extract the co-offending network of five years real-world crime data. Then we apply different variations of centrality methods on the extracted network and discuss how key player identification and removal can help law enforcement agencies in policy making for crime reduction.

Law enforcement and intelligence agencies have long realized that analysis of co-offending networks, networks of offenders who have committed crimes together, is invaluable for crime investigation, crime reduction and prevention. Investigating crime can be a challenging and difficult task, especially in cases with many potential suspects and inconsistent witness accounts or inconsistencies between witness accounts and physical evidence. We present here a novel approach to crime suspect recommendation based on partial knowledge of offenders involved in a crime incident and a known co-offending network. To solve this problem, we propose a random walk based method for recommending the top-K potential suspects. By evaluating the proposed method on a large crime dataset for the Province of British Columbia, Canada, we show experimentally that this method outperforms baseline random walk and association rule-based methods. Additionally, results obtained for public domain data from experiments for co-author recommendation on a DBLP co-authorship network are consistent with those on the crime dataset. Compared to the crime dataset, the performance of all competitors is much better on the DBLP dataset, confirming that crime suspect recommendation is an inherently harder task.