Malware or malicious software is an intrusive software that infects or performs harmful activities on a computer under attack. Malware has been a threat to individuals and organizations since the dawn of computers and the research community has been struggling to develop efficient methods to detect malware. In this work, we present a static malware detection system to detect Portable Executable (PE) malware in Windows environment and classify them as benign or malware with high accuracy. First, we collect a total of 27,920 Windows PE malware samples divided into six categories and create a new dataset by extracting four types of information including the list of imported DLLs and API functions called by these samples, values of 52 attributes from PE Header and 100 attributes of PE Section. We also amalgamate this information to create two integrated feature sets. Second, we apply seven machine learning models; gradient boosting, decision tree, random forest, support vector machine, K-nearest neighbor, naive Bayes, and nearest centroid, and three ensemble learning techniques including Majority Voting, Stack Generalization, and AdaBoost to classify the malware. Third, to further improve the performance of our malware detection system, we also deploy two dimensionality reduction techniques: Information Gain and Principal Component Analysis. We perform a number of experiments to test the performance and robustness of our system on both raw and selected features and show its supremacy over previous studies. By combining machine learning, ensemble learning and dimensionality reduction techniques, we construct a static malware detection system which achieves a detection rate of 99.5% and error rate of only 0.47%.

Every country requires efficient public transportation to reduce the reliance on motor vehicles, decrease harmful emissions and noise pollution, and, most importantly, provide accessible transportation for urban populations with limited resources. Yet, several challenges hinder the efficiency of the public transportation system, affecting factors like daily ridership, infrastructure, revenue, and operations. Therefore, this research investigated barriers and their impact on the daily ridership of rail transit services, especially in densely populated areas characterized by high automobile usage, inadequate pedestrian facilities, and limited connectivity to other transportation modes. To accomplish this goal, a self-administered questionnaire was developed, and a survey was conducted to collect responses from commuters at various public transport hubs. Structural equation modeling (SEM) was used to analyze data gathered from a sample of 1000 participants. The findings from the SEM model indicated a positive correlation between Instrumental Attractiveness (IA) and Facility Design and Operation (FDO) with daily travel. Conversely, Service and Information (SI) along with Environmental Comfort (EC) exhibited a negative correlation with daily travel. Private sector employees without personal vehicles and a monthly income below fifty thousand exhibited a strong preference for public transport. Individuals with higher incomes who owned vehicles showed lower preferences towards public transport usage. The main contributors to low ridership in public transport included poor connectivity with metro and feeder buses, lack of availability of discount tickets, insufficient seating at stations, and inadequate route map information. Gasoline prices have a notable impact on daily ridership too, as higher fuel prices tend to attract more travelers to use public transport. The ridership data demonstrate a positive trend in response to increased gasoline prices. The findings from this study are useful for transportation engineers, practitioners, planners, and policymakers.

This paper focuses on the perspectives of passengers who were railway users and how railways as a service can be uplifted with technological advancements through the introduction of information and communication technologies (ICTs). For this purpose, a questionnaire was designed comprised of six sections related to information on socio-economic-demographics, travel, station facilities, train facilities, customer care, and familiarity with and benefits of ICTs. A total of 800 respondents were recruited on trains and in railway stations to collect data through a random sampling technique. Data were analyzed through descriptive statistics, factor analysis, bivariate correlation analysis, and ordered logistic regression analysis. The three hypotheses tested showed that (i) there is a correlation between socio-demographic factors, train frequency, and satisfaction levels, (ii) satisfaction with station and train facilities and customer care impacts users’ travel likelihood with the train service, and (iii) users’ familiarity with perceived benefits of ICTs influences passengers’ travel likelihood with the train service. The results indicate that the users’ satisfaction with attributes of station facilities, train facilities, and customer care and perceptions about ICTs significantly influences their travel frequency with the train service. This study is useful for multiple stakeholders, especially for railway management authorities, to provide inclusive services to passengers and to plan for future transportation, which should be well-equipped with ICTs, well-integrated with other transport modes, and well-connected with optimum stops.

A sustainable information and communication flow (ICF) supports lifelines in floods, especially transport systems. A detailed insight into barriers regarding effective ICF through the implementation of information and communication technologies (ICTs) in the lifecycle of floods was given for evolved and evolving economies, i.e., York and Head-Marala, with sample sizes of 240 and 300 experts, respectively. All experts responded on an ethically approved questionnaire with further information notes that were used whilst discoursing the factors. ICTs were segregated into two groups, ranging from simple to advanced technologies. KMO and Bartlett’s tests confirmed high sampling adequacy with values ranging from 0.679 to 0.823 (≥0.5) with p-values ≤ 0.05. An amended version of Q-Methodology was used to identify nine factors in total. Each factor with an Eigenvalue ≥1 was retained, and all factors were highly reliable with values between 0.89 and 0.96. Factors were explained through communalities, factor loadings, pattern and structure matrices, and notes from experts. Results showed that under-evolved economies have limited technological resources and under-developed flood coping plans compared to evolved economies. Also, the unacknowledged possibility was uncovered that ICF can certainly be sustained if all possibly available ICTs are engaged through a thorough deployment plan of action. Authorities needed to make ICT engagement plans simple and efficient through effective coordination among different institutions. Though authorities were trained and equipped with modern tools, equipment, and technologies, dense and dependent populations overwhelmed the rescue capabilities. Other than VMS, social media pages, and radio, other ICTs were not tried and tested in floods for the exchange of transport–flood ICF. These findings are useful for stakeholders from all communities, transport planning institutions, and flood managers who are not fully benefiting from the extended use of ICTs to manage travel activities in floods.

Graph sampling allows mining a small representative subgraph from a big graph. Sampling algorithms deploy different strategies to replicate the properties of a given graph in the sampled graph. In this study, we provide a comprehensive empirical characterization of five graph sampling algorithms on six properties of a graph including degree, clustering coefficient, path length, global clustering coefficient, assorta-tivity, and modularity. We extract samples from fifteen graphs grouped into five categories including collaboration, social, citation, technological , and synthetic graphs. We provide both qualitative and quantitative results. We find that there is no single method that extracts true samples from a given graph with respect to the properties tested in this work. Our results show that the sampling algorithm that aggressively explores the neighborhood of a sampled node performs better than the others.