Malware detection based on graph attention networks for intelligent transportation systems

Intelligent Transportation Systems (ITS) aim to make transportation smarter, safer, reliable, and environmentally friendly without detrimentally affecting the service quality. ITS can face security issues due to their complex, dynamic, and non-linear properties. One of the most critical security problems is attacks that damage the infrastructure of the entire ITS. Attackers can inject malware code that triggers dangerous actions such as information theft and unwanted system moves. The main objective of this study is to improve the performance of malware detection models using Graph Attention Networks. To detect malware attacks addressing ITS, a Graph Attention Network (GAN)-based framework is proposed in this study. The inputs to this framework are the Application Programming Interface (API)-call graphs obtained from malware and benign Android apk files. During the graph creation, network metrics and the Node2Vec model are utilized to generate the node features. A GAN-based model is combined with different types of node features during the experiments and the performance is compared against Graph Convolutional Network (GCN). Experimental results demonstrated that the integration of the GAN and Node2Vec models provides the best performance in terms of F-measure and accuracy parameters and, also, the use of an attention mechanism in GAN improves the performance. Furthermore, node features generated with Node2Vec resulted in a 3% increase in classification accuracy compared to the features generated with network metrics. 2021 by the authors. Licensee MDPI, Basel, Switzerland.Scopus2-s2.0-8511720183

Energy load forecasting using a dual-stage attention-based recurrent neural network

Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures are crucial for service providers. Electrical load forecasting can be modeled as a time series prediction problem. One solution is to capture spatial correlations, spatial-temporal relations, and time-dependency of such temporal networks in the time series. Previously, different machine learning methods have been used for time series prediction tasks; however, there is still a need for new research to improve the performance of short-term load forecasting models. In this article, we propose a novel deep learning model to predict electric load consumption using Dual-Stage Attention-Based Recurrent Neural Networks in which the attention mechanism is used in both encoder and decoder stages. The encoder attention layer identifies important features from the input vector, whereas the decoder attention layer is used to overcome the limitations of using a fixed context vector and provides a much longer memory capacity. The proposed model improves the performance for short-term load forecasting (STLF) in terms of the Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE) scores. To evaluate the predictive performance of the proposed model, the UCI household electric power consumption (HEPC) dataset has been used during the experiments. Experimental results demonstrate that the proposed approach outperforms the previously adopted techniques. 2021 by the authors. Licensee MDPI, Basel, Switzerland.Scopus2-s2.0-8511828214

A hybrid DNN-LSTM model for detecting phishing URLs

Phishing is an attack targeting to imitate the official websites of corporations such as banks, e-commerce, financial institutions, and governmental institutions. Phishing websites aim to access and retrieve users' important information such as personal identification, social security number, password, e-mail, credit card, and other account information. Several anti-phishing techniques have been developed to cope with the increasing number of phishing attacks so far. Machine learning and particularly, deep learning algorithms are nowadays the most crucial techniques used to detect and prevent phishing attacks because of their strong learning abilities on massive datasets and their state-of-the-art results in many classification problems. Previously, two types of feature extraction techniques [i.e., character embedding-based and manual natural language processing (NLP) feature extraction] were used in isolation. However, researchers did not consolidate these features and therefore, the performance was not remarkable. Unlike previous works, our study presented an approach that utilizes both feature extraction techniques. We discussed how to combine these feature extraction techniques to fully utilize from the available data. This paper proposes hybrid deep learning models based on long short-term memory and deep neural network algorithms for detecting phishing uniform resource locator and evaluates the performance of the models on phishing datasets. The proposed hybrid deep learning models utilize both character embedding and NLP features, thereby simultaneously exploiting deep connections between characters and revealing NLP-based high-level connections. Experimental results showed that the proposed models achieve superior performance than the other phishing detection models in terms of accuracy metric. 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Scopus2-s2.0-8511207050

Analysis of cyber security knowledge gaps based on cyber security body of knowledge

Due to the increasing number of cyber incidents and overwhelming skills shortage, it is required to evaluate the knowledge gap between cyber security education and industrial needs. As such, the objective of this study is to identify the knowledge gaps in cyber security graduates who join the cyber security workforce. We designed and performed an opinion survey by using the Cyber Security Knowledge Areas (KAs) specified in the Cyber Security Body of Knowledge (CyBOK) that comprises 19 KAs. Our data was gathered from practitioners who work in cyber security organizations. The knowledge gap was measured and evaluated by acknowledging the assumption for employing sequent data as nominal data and improved it by deploying chi-squared test. Analyses demonstrate that there is a gap that can be utilized to enhance the quality of education. According to acquired final results, three key KAs with the highest knowledge gap are Web and Mobile Security, Security Operations and Incident Management. Also, Cyber-Physical Systems (CPS), Software Lifecycles, and Vulnerabilities are the knowledge areas with largest difference in perception of importance between less and more experienced personnel. We discuss several suggestions to improve the cyber security curriculum in order to minimize the knowledge gaps. There is an expanding demand for executive cyber security personnel in industry. High-quality university education is required to improve the qualification of upcoming workforce. The capability and capacity of the national cyber security workforce is crucial for nations and security organizations. A wide range of skills, namely technical skills, implementation skills, management skills, and soft skills are required in new cyber security graduates. The use of each CyBOK KA in the industry was measured in response to the extent of learning in university environments. This is the first study conducted in this field, it is considered that this research can inspire the way for further researches. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Scopus2-s2.0-8513535559

Genetic polymorphism in the serotonin transporter gene-linked polymorphic region and response to serotonin reuptake inhibitors in patients with premature ejaculation

OBJECTIVES: Serotonin plays a central role in ejaculation and selective serotonin reuptake inhibitors have been successfully used to treat premature ejaculation. Here, we evaluated the relationship between a polymorphism in the serotonin transporter gene-linked polymorphic region (5-HTTLPR) and the response of patients with premature ejaculation to SSRI medication. METHODS: Sixty-nine premature ejaculation patients were treated with 20 mg/d paroxetine for three months. The Intravaginal Ejaculatory Latency Time and International Index of Erectile Function scores were compared with baseline values. The patients were scored as having responded to therapy when a 2-fold or greater increase was observed in Intravaginal Ejaculatory Latency Time compared with baseline values after three months. Three genotypes of 5-HTTLPR were studied: LL, LS and SS. The appropriateness of the allele frequencies in 5-HTTLPR were analyzed according to Hardy-Weinberg equilibrium using the χ2-test. RESULTS: The short (S) allele of 5-HTTLPR was significantly more frequent in responders than in nonresponders (

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P <.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes

Clinical complexity and impact of the ABC (Atrial fibrillation Better Care) pathway in patients with atrial fibrillation: a report from the ESC-EHRA EURObservational Research Programme in AF General Long-Term Registry

Background: Clinical complexity is increasingly prevalent among patients with atrial fibrillation (AF). The ‘Atrial fibrillation Better Care’ (ABC) pathway approach has been proposed to streamline a more holistic and integrated approach to AF care; however, there are limited data on its usefulness among clinically complex patients. We aim to determine the impact of ABC pathway in a contemporary cohort of clinically complex AF patients. Methods: From the ESC-EHRA EORP-AF General Long-Term Registry, we analysed clinically complex AF patients, defined as the presence of frailty, multimorbidity and/or polypharmacy. A K-medoids cluster analysis was performed to identify different groups of clinical complexity. The impact of an ABC-adherent approach on major outcomes was analysed through Cox-regression analyses and delay of event (DoE) analyses. Results: Among 9966 AF patients included, 8289 (83.1%) were clinically complex. Adherence to the ABC pathway in the clinically complex group reduced the risk of all-cause death (adjusted HR [aHR]: 0.72, 95%CI 0.58–0.91), major adverse cardiovascular events (MACEs; aHR: 0.68, 95%CI 0.52–0.87) and composite outcome (aHR: 0.70, 95%CI: 0.58–0.85). Adherence to the ABC pathway was associated with a significant reduction in the risk of death (aHR: 0.74, 95%CI 0.56–0.98) and composite outcome (aHR: 0.76, 95%CI 0.60–0.96) also in the high-complexity cluster; similar trends were observed for MACEs. In DoE analyses, an ABC-adherent approach resulted in significant gains in event-free survival for all the outcomes investigated in clinically complex patients. Based on absolute risk reduction at 1 year of follow-up, the number needed to treat for ABC pathway adherence was 24 for all-cause death, 31 for MACEs and 20 for the composite outcome. Conclusions: An ABC-adherent approach reduces the risk of major outcomes in clinically complex AF patients. Ensuring adherence to the ABC pathway is essential to improve clinical outcomes among clinically complex AF patients