The importance of understanding and explaining the associated classification results in the utilization of artificial intelligence (AI) in many different practical applications (e.g., cyber security and forensics) has contributed to the trend of moving away from black-box / opaque AI towards explainable AI (XAI). In this article, we propose the first interpretable autoencoder based on decision trees, which is designed to handle categorical data without the need to transform the data representation. Furthermore, our proposed interpretable autoencoder provides a natural explanation for experts in the application area. The experimental findings show that our proposed interpretable autoencoder is among the top-ranked anomaly detection algorithms, along with one-class Support Vector Machine (SVM) and Gaussian Mixture. More specifically, our proposal is on average 2% below the best Area Under the Curve (AUC) result and 3% over the other Average Precision scores, in comparison to One-class SVM, Isolation Forest, Local Outlier Factor, Elliptic Envelope, Gaussian Mixture Model, and eForest.

Supervised classification based on Contrast Patterns (CP) is a trending topic in the pattern recognition literature, partly because it contains an important family of both understandable and accurate classifiers. In this paper, we survey 105 articles and provide an in-depth review of CP-based supervised classification and its applications. Based on our review, we present a taxonomy of the existing application domains of CP-based supervised classification, and a scientometric study. We also discuss potential future research opportunities.

Abstract Reconstruction‐based one‐class classification has shown to be very effective in a number of domains. This approach works by attempting to capture the underlying structure of the normal class, typically, by means of clusters of objects. It has the main disadvantage, however, that one has to indicate the number of clusters in advance, for this yields an efficient way of computing a clustering. In this paper, we introduce a new algorithm, OCKRA++, which achieves a better performance, by enhancing a clustering‐based one‐class ensemble classifier (OCKRA) with a cluster validity index that is used to set the best number of clusters during the classifier's training process. We have thoroughly tested OCKRA++ in a particular domain, namely masquerade detection. For this purpose, we have used the Windows‐Users and ‐Intruder simulation Logs data set repository, which contains 70 different masquerade data sets. We have found that OCKRA++ is currently the algorithm that achieves the best area under the curve, with a significant difference, in masquerade detection using the file system navigation approach.

Latent palmprint identification is a crucial element for both law enforcement and integrated automated fingerprint identification systems because approximately 30% of the imprints found in a crime scene originate from a human’s palms. To find the person whom the palmprint belongs to, forensic experts use systems that automatically compare the imprints found, called latent, against thousands of potential palmprints. Identification systems rely on features obtained from the palmprint, and different feature representations to include discriminative information. However, there is no consensus as to which representation allows for a better matching between latent palmprints, and those with a known identity. Furthermore, evaluating the identification performance when matching palmprints obtained when using different representations has not been done fairly. The current manner of evaluating palmprint identification methods uses different datasets, performance measures, and does not allow to discern the contributions of the feature representation and the methods for matching the palmprints. In this study, we have reviewed those features used for latent palmprint identification, and also we propose an evaluation methodology that allows for a fair comparison of minutiae-based features. Using our methodology, we evaluated each representation performing more than 5 billion comparisons. Our experiments are done using a dataset that includes information about the matching minutiae according to an expert. We aim with our results to provide a baseline for new research in latent palmprint identification feature representations, allowing for a fair comparison of newly developed representations in the future, which would enhance the whole latent palmprint identification methods. For this purpose, we also publicly provide our dataset, methodology implementation, and the feature representations implementation tested in our experiments.

People often face risk-prone situations, that range from a mild event to a severe, life-threatening scenario. Risk situations stem from a number of different scenarios: a health condition, a hazard situation due to a natural disaster, a dangerous situation because one is being subject to a crime or physical violence, among others. The lack of a prompt response, calling for assistance, may severely worsen the consequences. In this paper, we propose a novel visualisation method to track and to identify, in real-time, when a person is under a risk-prone situation. Our visualisation model is capable of providing a decision maker a visual description of the physiological behaviour of an individual, or a group thereof; through it, the decision maker may infer whether further assistance is required, if a risky situation is in progress. Our visualisation is leveraged with a traffic light model of a one-class classifier. This combination allows us to train the decision maker into visualising correct and potential risky or abnormal behaviour.

We define personal risk detection as the timely identification of when someone is in the midst of a dangerous situation, for example, a health crisis or a car accident, events that may jeopardize a person’s physical integrity. We work under the hypothesis that a risk-prone situation produces sudden and significant deviations in standard physiological and behavioural user patterns. These changes can be captured by a group of sensors, such as the accelerometer, gyroscope, and heart rate. We introduce a dataset, called PRIDE, which provides a baseline for the development and the fair comparison of personal risk detection mechanisms. PRIDE contains information on 18 test subjects; for each subject, it includes partial information about the user’s behavioural and physiological patterns, as captured by Microsoft Band©. PRIDE test subject records include sensor readings of not only when a subject is carrying out ordinary daily life activities, but also when exposed to a stressful scenario, thereby simulating a dangerous or abnormal situation. We show how to use PRIDE to develop a personal risk detection mechanism; to accomplish this, we have tackled risk detection as a one-class classification problem. We have trained several classifiers based only on the daily behaviour of test subjects. Further, we tested the accuracy of the classifiers to detect anomalies that were not included in the training process of the classifiers. We used a number of one-class classifiers, namely: SVM, Parzen, and two versions of Parzen based on k-means. While there is still room for improvement, our results are encouraging: they support our hypothesis that abnormal behaviour can be automatically detected.

Most studies in masquerade detection focus mainly on the user action, ignoring the object upon which that action is performed. This may yield limited models, since, for example, command execution (an action) usually ends up in the transformation of a file (the object). The overall goal of this paper is to prove that the object is paramount to distinguishing a user from a masquerade. With this in mind, we have developed a new approach to masquerade detection, called file system navigation, and tested our ideas using the Windows-Users and Windows-Intruder simulations Logs Data set, (WUIL) which unlike other datasets of its kind includes close-to-real simulated attacks. We have shown that our approach makes it possible to capture computer behavior in an abstract way difficult to realize in a purely action-based approach. In this paper, we introduce an abstraction called locality, the tendency of programs to cluster references to memory. While temporal locality is applicable to both actions and objects, spatial locality is more suitable to objects, as it depends on a notion of position. We have successfully validated our working hypothesis: locality-based features better capture user behavior for masquerade detection. Particularly, results based on our approach report an Area Under the Curve (AUC) of the receiver operating characteristic curve value of 0.97 in average with 30% of users having an AUC equal to or above 0.99.

This paper presents a new public available database for latent palmprint identification. Latent palmprint identification is an important research area which includes scientific challenges as well as social interest. Latent palmprints appear frequently in criminal investigations so developing accurate identification systems is critical in solving these investigations. Latent palmprint identification includes several pattern recognition challenges such as matching, feature extraction, and image processing. The lack of public latent palmprint databases has limited advances in scientific state-of-the-art researches. The database presented in this paper comprises 380 latent palmprints from 100 palms acquired under realistic conditions. The database includes the minutiae (position and orientation) taken manually and automatically. Additionally new research opportunities based on this database are presented as well as the benchmarks obtained with different publicly available minutiae extractors and matchers. As an example of the possibilities of the database a comparison between automatic and manual minutiae extraction is included.