ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning.

In recent years, cybersecurity has been strengthened through the adoption of processes, mechanisms and rapid sources of indicators of compromise in critical areas. Among the most latent challenges are the detection, classification and eradication of malware and Denial of Service Cyber-Attacks (DoS). The literature has presented different ways to obtain and evaluate malware- and DoS-cyber-attack-related instances, either from a technical point of view or by offering ready-to-use datasets. However, acquiring fresh, up-to-date samples requires an arduous process of exploration, sandbox configuration and mass storage, which may ultimately result in an unbalanced or under-represented set. Synthetic sample generation has shown that the cost associated with setting up controlled environments and time spent on sample evaluation can be reduced. Nevertheless, the process is performed when the observations already belong to a characterized set, totally detached from a real environment. In order to solve the aforementioned, this work proposes a methodology for the generation of synthetic samples of malicious Portable Executable binaries and DoS cyber-attacks. The task is performed via a Reinforcement Learning engine, which learns from a baseline of different malware families and DoS cyber-attack network properties, resulting in new, mutated and highly functional samples. Experimental results demonstrate the high adaptability of the outputs as new input datasets for different Machine Learning algorithms.

FASSVid: Fast and Accurate Semantic Segmentation for Video Sequences.

Most of the methods for real-time semantic segmentation do not take into account temporal information when working with video sequences. This is counter-intuitive in real-world scenarios where the main application of such methods is, precisely, being able to process frame sequences as quickly and accurately as possible. In this paper, we address this problem by exploiting the temporal information provided by previous frames of the video stream. Our method leverages a previous input frame as well as the previous output of the network to enhance the prediction accuracy of the current input frame. We develop a module that obtains feature maps rich in change information. Additionally, we incorporate the previous output of the network into all the decoder stages as a way of increasing the attention given to relevant features. Finally, to properly train and evaluate our methods, we introduce CityscapesVid, a dataset specifically designed to benchmark semantic video segmentation networks. Our proposed network, entitled FASSVid improves the mIoU accuracy performance over a standard non-sequential baseline model. Moreover, FASSVid obtains state-of-the-art inference speed and competitive mIoU results compared to other state-of-the-art lightweight networks, with significantly lower number of computations. Specifically, we obtain 71% of mIoU in our CityscapesVid dataset, running at 114.9 FPS on a single NVIDIA GTX 1080Ti and 31 FPS on the NVIDIA Jetson Nano embedded board with images of size 1024×2048 and 512×1024, respectively.

Efficient Face Recognition System for Operating in Unconstrained Environments.

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together.

A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT.

At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in depth, several defensive layers are required to protect information assets. Within the context of IoT cyber-attacks, it is fundamental to continuously adapt new detection mechanisms for growing IoT threats, specifically for those becoming more sophisticated within mesh networks, such as identity theft and cloning. Therefore, current applications, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Security Information and Event Management Systems (SIEM), are becoming inadequate for accurately handling novel security incidents, due to their signature-based detection procedures using the matching and flagging of anomalous patterns. This project focuses on a seldom-investigated identity attack-the Clone ID attack-directed at the Routing Protocol for Low Power and Lossy Networks (RPL), the underlying technology for most IoT devices. Hence, a robust Artificial Intelligence-based protection framework is proposed, in order to tackle major identity impersonation attacks, which classical applications are prone to misidentifying. On this basis, unsupervised pre-training techniques are employed to select key characteristics from RPL network samples. Then, a Dense Neural Network (DNN) is trained to maximize deep feature engineering, with the aim of improving classification results to protect against malicious counterfeiting attempts.

Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach.

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average-dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.

Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation.

In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico's 2017 Earthquake is presented, and the data extracted during and after the event are reported.

Efficacy of Tianeptine 25-50 mg in Elderly Patients With Recurrent Major Depressive Disorder: An 8-Week Placebo- and Escitalopram-Controlled Study.

OBJECTIVE: The present placebo-controlled study evaluated the efficacy and safety of 8 weeks of treatment with tianeptine 25-50 mg/d in elderly patients suffering from major depressive disorder (MDD) according to DSM-IV-TR. Escitalopram 5-10 mg/d was used as an active comparator. METHODS: Elderly outpatients aged at least 65 years with a primary diagnosis of moderate to severe episode of recurrent MDD were recruited by psychiatrists in 44 clinical centers in 10 countries from October 2013 to January 2016. Patients were randomly assigned to receive tianeptine (n = 105), placebo (n = 107), or escitalopram (n = 99) for 8 weeks. The primary outcome measure was the 17-item Hamilton Depression Rating Scale (HDRS17) total score. RESULTS: Tianeptine improved depressive symptoms, as evaluated by the HDRS17 total score in terms of absolute change from baseline (week 0) to week 8 (placebo-tianeptine difference [SE] of 3.84 [0.85] points, P < .001, using a last-observation-carried-forward approach) and response to treatment (tianeptine: 46.7%; placebo: 34.0%, estimate [SE] = 12.70% [6.70], P = .06). A sensitivity analysis using a mixed model for repeated measures confirmed the main results on HDRS total s​core. The placebo-tianeptine difference (SE) was 0.66 (0.15) for Clinical Global Impressions-Severity of Illness (95% CI, 0.37 to 0.96; P < .001) and 0.57 (0.14) for Clinical Global Impressions- Improvement (95% CI, 0.30 to 0.83; P < .001). Positive results were also obtained with the active control escitalopram (HDRS17 total score placebo-escitalopram difference of 4.09 ± 0.86 points, P < .001), therefore validating the sensitivity of the studied population. Tianeptine was well tolerated, with only minimal differences in tolerability from placebo. CONCLUSIONS: The present study provides robust evidence that an 8-week treatment period with tianeptine 25-50 mg is efficacious and well tolerated in depressed patients aged 65 years or older. TRIAL REGISTRATION: EudraCT identifier: 2012-005612-26​.

Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization.

In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization.

Cervical necrotizing fasciitis of nonodontogenic origin: case report and review of literature.

Cervical necrotizing fasciitis (CNF) is a potentially fatal infection characterized by generalized necrosis of the cervical fascia that progresses rapidly. The incidence of this entity corresponds to 2.6% of all infections of the head and neck. The most frequent primary origin is dental infection, although other causes exist that should be evaluated.Delay in the diagnosis of this entity may lead to rapid progression and fatal outcome. Patients often present immunosuppression or systemic diseases that predispose them to this pathology. Cervical necrotizing fasciitis is associated with mortality rates of 7% to 20% depending on the extension of the cervical lesion. The highest rates correspond to cases that progress to mediastinitis or septic shock, which are the main and most frequent complications. Early detection and adequate emergency treatment are critical in the management of these patients and may reduce morbimortality and improve survival. The emergency services should be prepared to manage such cases efficiently, through a multidisciplinary treatment by coordinating emergency surgery with critical support and clinical stabilization of patients.We present a case of CNF of non odontogenic origin managed in our hospital.

Linfadenitis necrotizante submaxilar en la enfermedad por arañazo de gato / Submandibular necrotising lymphadenitis in cat-scratch

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Aspectos clinicopatológicos del cáncer de vesícula biliar / Clinicopathological aspects of cancer of the gallbladder

Se analizan 94 observaciones de cáncer de vesícula, asociados en todos los casos a litiasis biliar o colesterolosis. Se observó que la frecuencia de aparición del cáncer aumenta en forma directamente proporcional a la edad del paciente y a la antigüedad de la litiasis. Es importante destacar que en más del 80% de los casos se hallaron en la mucosa adyacente a los tumores focos de hiperplasia, hiperplasia atípica, displasia y carcinoma in situ. Se señala la importancia de un estudio histológico realizado en base a numerosos cortes para una correcta estadificación. La clasificación propuesta por Nevin tiene gran valor pronóstico. Se considera que la evolución de los pacientes depende más del estadio del tumor, que de la amplitud de la resección realizada

Aspectos clinicopatológicos del cáncer de vesícula biliar / Clinicopathological aspects of cancer of the gallbladder

Se analizan 94 observaciones de cáncer de vesícula, asociados en todos los casos a litiasis biliar o colesterolosis. Se observó que la frecuencia de aparición del cáncer aumenta en forma directamente proporcional a la edad del paciente y a la antig³edad de la litiasis. Es importante destacar que en más del 80% de los casos se hallaron en la mucosa adyacente a los tumores focos de hiperplasia, hiperplasia atípica, displasia y carcinoma in situ. Se señala la importancia de un estudio histológico realizado en base a numerosos cortes para una correcta estadificación. La clasificación propuesta por Nevin tiene gran valor pronóstico. Se considera que la evolución de los pacientes depende más del estadio del tumor, que de la amplitud de la resección realizada (AU)