The Effectiveness of Zero-Day Attacks Data Samples Generated via GANs on Deep Learning Classifiers.

Digitization of most of the services that people use in their everyday life has, among others, led to increased needs for cybersecurity. As digital tools increase day by day and new software and hardware launch out-of-the box, detection of known existing vulnerabilities, or zero-day as they are commonly known, becomes one of the most challenging situations for cybersecurity experts. Zero-day vulnerabilities, which can be found in almost every new launched software and/or hardware, can be exploited instantly by malicious actors with different motives, posing threats for end-users. In this context, this study proposes and describes a holistic methodology starting from the generation of zero-day-type, yet realistic, data in tabular format and concluding to the evaluation of a Neural Network zero-day attacks' detector which is trained with and without synthetic data. This methodology involves the design and employment of Generative Adversarial Networks (GANs) for synthetically generating a new and larger dataset of zero-day attacks data. The newly generated, by the Zero-Day GAN (ZDGAN), dataset is then used to train and evaluate a Neural Network classifier for zero-day attacks. The results show that the generation of zero-day attacks data in tabular format reaches an equilibrium after about 5000 iterations and produces data that are almost identical to the original data samples. Last but not least, it should be mentioned that the Neural Network model that was trained with the dataset containing the ZDGAN generated samples outperformed the same model when the later was trained with only the original dataset and achieved results of high validation accuracy and minimal validation loss.

FoGGAN: Generating Realistic Parkinson's Disease Freezing of Gait Data Using GANs.

Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson's Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier's performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter.

A Distributed Big Data Analytics Architecture for Vehicle Sensor Data.

The unceasingly increasing needs for data acquisition, storage and analysis in transportation systems have led to the adoption of new technologies and methods in order to provide efficient and reliable solutions. Both highways and vehicles, nowadays, host a vast variety of sensors collecting different types of highly fluctuating data such as speed, acceleration, direction, and so on. From the vast volume and variety of these data emerges the need for the employment of big data techniques and analytics in the context of state-of-the-art intelligent transportation systems (ITS). Moreover, the scalability needs of fleet and traffic management systems point to the direction of designing and deploying distributed architecture solutions that can be expanded in order to avoid technological and/or technical entrapments. Based on the needs and gaps detected in the literature as well as the available technologies for data gathering, storage and analysis for ITS, the aim of this study is to provide a distributed architecture platform to address these deficiencies. The architectural design of the system proposed, engages big data frameworks and tools (e.g., NoSQL Mongo DB, Apache Hadoop, etc.) as well as analytics tools (e.g., Apache Spark). The main contribution of this study is the introduction of a holistic platform that can be used for the needs of the ITS domain offering continuous collection, storage and data analysis capabilities. To achieve that, different modules of state-of-the-art methods and tools were utilized and combined in a unified platform that supports the entire cycle of data acquisition, storage and analysis in a single point. This leads to a complete solution for ITS applications which lifts the limitations imposed in legacy and current systems by the vast amounts of rapidly changing data, while offering a reliable system for acquisition, storage as well as timely analysis and reporting capabilities of these data.

A Quality Control Methodology for Heterogeneous Vehicular Data Streams.

The rapid evolution of sensors and communication technologies has led to the production and transfer of mass data streams from vehicles either inside their electronic units or to the outside world using the internet infrastructure. The "outside world", in most cases, consists of third-party applications, such as fleet or traffic management control centers, which utilize vehicular data for reporting and monitoring functionalities. Such applications, in most cases, in order to facilitate their needs, require the exchange and processing of vast amounts of data which can be handled by the so-called Big Data technologies. The purpose of this study is to present a hybrid platform suitable for data collection, storing and analysis enhanced with quality control actions. In particular, the collected data contain various formats originating from different vehicle sensors and are stored in the aforementioned platform in a continuous way. The stored data in this platform must be checked in order to determine and validate them in terms of quality. To do so, certain actions, such as missing values checks, format checks, range checks, etc., must be carried out. The results of the quality control functions are presented herein, and useful conclusions are drawn in order to avoid possible data quality problems which may occur in further analysis and use of the data, e.g., for training of artificial intelligence models.

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review.

The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper's goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.

Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data.

In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle's condition as well as the driver's behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (ICT) push the transportation domain towards a new more intelligent and efficient era. The reduction of CO2 emissions and the minimization of the environmental footprint is, undeniably, of utmost importance for the protection of the environment. In this light, it is widely acceptable that the driving behaviour is directly associated with the vehicle's fuel consumption and gas emissions. Thus, given the fact that, nowadays, vehicles are equipped with sensors that can collect a variety of data, such as speed, acceleration, fuel consumption, direction, etc. is more feasible than ever to put forward solutions which aim not only to monitor but also improve the drivers' behaviour from an environmental point of view. The approach presented in this paper describes a holistic integrated platform which combines well-known machine and deep learning algorithms together with open-source-based tools in order to gather, store, process, analyze and correlate different data flows originating from vehicles. Particularly, data streamed from different vehicles are processed and analyzed with the utilization of clustering techniques in order to classify the driver's behaviour as eco-friendly or not, followed by a comparative analysis of supervised machine and deep learning algorithms in the given labelled dataset.

Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0.

The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing interest from security researchers. Network traffic analysis and classification based on Machine Learning (ML) methodologies can play a vital role in tackling such threats. Towards this direction, this research work presents and evaluates different ML classifiers for network traffic classification, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF) and Stochastic Gradient Descent (SGD), as well as a hard voting and a soft voting ensemble model of these classifiers. In the context of this research work, three variations of the NSL-KDD dataset were utilized, i.e., initial dataset, undersampled dataset and oversampled dataset. The performance of the individual ML algorithms was evaluated in all three dataset variations and was compared to the performance of the voting ensemble methods. In most cases, both the hard and the soft voting models were found to perform better in terms of accuracy compared to the individual models.

Survey on Security Threats in Agricultural IoT and Smart Farming.

The agriculture sector has held a major role in human societies across the planet throughout history. The rapid evolution in Information and Communication Technologies (ICT) strongly affects the structure and the procedures of modern agriculture. Despite the advantages gained from this evolution, there are several existing as well as emerging security threats that can severely impact the agricultural domain. The present paper provides an overview of the main existing and potential threats for agriculture. Initially, the paper presents an overview of the evolution of ICT solutions and how these may be utilized and affect the agriculture sector. It then conducts an extensive literature review on the use of ICT in agriculture, as well as on the associated emerging threats and vulnerabilities. The authors highlight the main ICT innovations, techniques, benefits, threats and mitigation measures by studying the literature on them and by providing a concise discussion on the possible impacts these could have on the agri-sector.

Frontotemporal Dysfunction in Amyotrophic Lateral Sclerosis: A Discriminant Function Analysis.

BACKGROUND: There is growing evidence for extramotor dysfunction (EMd) in amyotrophic lateral sclerosis (ALS), with a reported prevalence of up to 52%. OBJECTIVE: In the present study, we explore the clinical utility of a brief neuropsychological battery for the investigation of cognitive, behavioral, and language deficits in patients with ALS. METHODS: Thirty-four consecutive ALS patients aged 44-89 years were tested with a brief neuropsychological battery, including executive, behavioral, and language measures. Patients were initially classified as EMd or non-EMd based on their scores on the frontal assessment battery (FAB). RESULTS: Between-group comparisons revealed significant differences in all measures (p < 0.01). Discriminant analysis resulted in a single canonical function, with all tests serving as significant predictors. This function agreed with the FAB in 13 of 17 patients screened as EMd and identified extramotor deficits in 2 additional patients. Overall sensitivity and specificity estimates against FAB were 88.2%. CONCLUSIONS: We stress the importance of discriminant function analysis in clinical neuropsychological assessment and argue that the proposed neuropsychological battery may be of clinical value, especially when the option of extensive and comprehensive neuropsychological testing is limited. The psychometric validity of an ALS-frontotemporal dementia diagnosis using neuropsychological tests is also discussed.

Volume matters: the influence of different botulinum toxin-A dilutions for sialorrhea in amyotrophic lateral sclerosis.

INTRODUCTION: We aimed to determine the effect of different botulinum toxin-A (BTX-A) dilutions on the treatment efficacy and side effects for amyotrophic lateral sclerosis (ALS) related sialorrhea. METHODS: Ten patients were enrolled in the study. BTX-A dilution for Group A was 100 U in 1 ml of saline, whereas the dilution for Group B was 100 U in 2 ml of saline. Both groups received 20 U of BTX-A in each parotid gland, and assessments were made by means of the Drooling Impact Scale, items 1 and 3 of the ALS functional rating scale, and visual analog scales for drooling and swallowing function. RESULTS: Although both groups exhibited a similar improvement in drooling, Group B had a mild but significant deterioration in bulbar function that was not evident in Group A. CONCLUSIONS: These results suggest that BTX-A has a safer profile when reconstituted with 1 ml instead of 2 ml of saline.

Prevalence of major depression in ALS: comparison of a semi-structured interview and four self-report measures.

This study aimed to compare prevalence estimates of current major depression obtained with a semi-structured interview and four frequently used self-report depression severity measures in a sample of amyotrophic lateral sclerosis (ALS) patients. Thirty-seven ALS patients (56.8% males) aged 37-80 years (mean 62.0 ± 10.7) without respiratory insufficiency or dementia were studied during hospitalization or on a follow-up visit. SCID-IV interview as well as self-report Hospital Anxiety and Depression Scale (HADS), ALS Depression Inventory (ADI), Center for Epidemiological Studies-Depression scale (CES-D) and Beck Depression Inventory (BDI-I) were administered. Kappa coefficients of diagnostic agreement between various instruments were calculated. Results showed that 37.8% of patients had a lifetime diagnosis of depression and in 13.5% depression followed ALS onset. Percentages of patients 'diagnosed' with current major depression were: 21.6% (SCID-IV), 16.7% (HADS-D ≥ 11), 16.2% (ADI ≥ 29), 25% (CES-D ≥ 24) and 24.3% (BDI-I ≥ 16). High kappa values were recorded between CES-D, BDI-I and SCID-IV as well as between HADS-D and ADI. CES-D, BDI-I and SCID-IV gave the highest prevalence estimates of current major depression in ALS patients and were in poor agreement with estimates based on HADS and ADI; it is suggested that this is possibly because the former give a far greater emphasis on physical symptoms of depression than the latter.

Interleukin-15 and interleukin-12 are elevated in serum and cerebrospinal fluid of patients with amyotrophic lateral sclerosis.

BACKGROUND: There is evidence that immunological factors may be involved in pathogenic mechanisms of amyotrophic lateral sclerosis (ALS). Interleukin (IL)-15 and IL-12 are proinflammatory cytokines produced by activated blood and glial cells. They promote T cell differentiation and proliferation. PATIENTS AND METHODS: We measured by ELISA serum and cerebrospinal fluid (CSF) levels of IL-15 and IL-12 in 21 patients with ALS and 19 patients with other noninflammatory neurological disorders (NIND) studied as a control group. IL-15 and IL-12 serum and CSF levels were also correlated with duration of the disease, the disability level determined using the revised ALS Functional Rating Scale and the clinical subtype of the disease onset in patients with ALS. RESULTS: IL-15 and IL-12 serum levels were higher in patients with ALS as compared with patients with NIND (p = 0.014 and p = 0.011, respectively). IL-15 and IL-12 CSF levels were also increased in patients with ALS (p = 0.011 and p = 0.005, respectively). IL-15 and IL-12 levels were not correlated with disease duration, disability scale or clinical subtype of the disease onset in ALS patients. CONCLUSIONS: Our findings suggest that these molecules may be involved in the pathogenic mechanisms acting as potential markers of immune activation in ALS.

Square-wave jerks and smooth pursuit impairment as subtle early signs of brain involvement in Langerhans' cell histiocytosis.

Central nervous system (CNS) involvement in Langerhans' cell histiocytosis (LCH) has been described as a progressive neurological disorder marked by motor and cognitive decline. Detailed analysis of ocular motor abnormalities is lacking. We report on a 60-year-old male with histologically confirmed LCH who developed oscillopsia and gait ataxia over a 1-year period. Eye movements recorded with infrared oculography revealed a high rate of square-wave jerks (SWJ) with frequencies of 41 min(-1) on average and amplitudes between 1 degrees and 7 degrees , as well as marked impairment of smooth tracking of sinusoidally moving targets. Furthermore, static posturography disclosed increased body sway, with an abnormally high sway path. The initial brain MRI was unremarkable. Due to the presumed cerebellar dysfunction we performed a second MRI 1 year later that disclosed deep cerebellar lesions compatible with LCH relapse within the CNS. The abnormal high SWJ rate and the impaired smooth pursuit performance correctly heralded later involvement of the cerebellum anticipating lesion appearance in the MRI.

Humoral immune activation in amyotrophic lateral sclerosis patients.

There is evidence that immunological factors may involved in the pathogenetic mechanisms of amyotrophic lateral sclerosis (ALS). Few studies to date have explored the status of the humoral immune response in patients with ALS. We examined the presence of humoral immune activation in ALS patients, serum immunoglobulins (IgG, IgA and IgM) levels were measured in 36 patients with ALS and 35 normal controls. Serum IgG, IgM and IgA levels were not significantly different in our ALS patients compared with the control group (P=ns). No correlations of serum IgG, IgM and IgA concentrations with duration, severity of the disease or the clinical form of onset (bulbar or spinal) were found in our ALS patients. Our results do not suggest a humoral immune activation in ALS patients. This does not exclude that immunological mechanisms may be involved in ALS pathogenesis.