In this work, we present a new and efficient algorithm to perform a short-term market trend forecast, based on the Artificial Organic Networks (AON) metaheuristic machine learning framework. Regarding this goal, we present the concept of Artificial Halocarbon Compounds (AHC) or AHC-algorithm as a bio-inspired supervised machine learning algorithm based on the AON framework. Through our research, we contrast the forecast acquired with the proposed AHC model, to previously reported outcomes using the Artificial Hydrocarbon Networks (AHN) in similar tasks. The AHN algorithm is the first formally defined topology based on the AON, making the AHN algorithm a vital benchmark to contemplate. After comparing the AHC-algorithm to the original AHN-algorithm, we found out that due to the high computational complexity of the latter, the new topology is more convenient when modeling more complex systems; being this characteristic the main contribution of the AHC-algorithm, allowing it to be a more adaptable, dynamic, and reconfigurable topology. Likewise, we compared the results of the AHC-algorithm against the outcomes derived from an ARIMA model; we also made a cross-reference contrast against results concerning the prediction of other stock market indices using former state-of-the-art machine learning methods. The proficiency of the AHC-algorithm is assessed by doing a forecast of the IPC Mexico index obtaining good results, achieving a computed R-square of 0.9919, and an $$8\times 10^{-4}$$ 8 × 10 - 4 mean relative error for the forecast.

Predicting mental health conditions from speech has been widely explored in recent years. Most studies rely on a single sample from each subject to detect indicators of a particular disorder. These studies ignore two important facts: certain mental disorders tend to co-exist, and their severity tends to vary over time. This work introduces a longitudinal dataset labeled with depression, anxiety, and stress scores using the DASS-21 self-report questionnaire, and describes a machine-learning pipeline to determine the severity of the three mental disorders using acoustic features extracted from speech samples of this dataset. Our initial findings suggest that healthy participants adhere more to the study procedure than participants who exhibit indicators of depression, anxiety, and stress and demonstrate that a one-dimensional convolutional neural network, trained on VGG-19 features, predicts the severity of depression, anxiety, and stress with high accuracy.

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.

Upon an intrusion, security staff must analyze the IT system that has been compromised, in order to determine how the attacker gained access to it, and what he did afterward. Usually, this analysis reveals that the attacker has run an exploit that takes advantage of a system vulnerability. Pinpointing, in a given log file, the execution of one such an exploit, if any, is very valuable for computer security. This is both because it speeds up the process of gathering evidence of the intrusion, and because it helps taking measures to prevent a further intrusion, e.g., by building and applying an appropriate attack signature for intrusion detection system maintenance. This problem, which we call postmortem intrusion detection, is fairly complex, given both the overwhelming length of a standard log file, and the difficulty of identifying exactly where the intrusion has occurred. In this paper, we propose a novel approach for postmortem intrusion detection, which factors out repetitive behavior, thus, speeding up the process of locating the execution of an exploit, if any. Central to our intrusion detection mechanism is a classifier, which separates abnormal behavior from normal one. This classifier is built upon a method that combines a hidden Markov model withk-means. Our experimental results establish that our method is able to spot the execution of an exploit, with a cumulative detection rate of over 90%. In addition, we propose an entropy-based approach that speeds up the construction of a profile for ordinary system behavior.