Women’s leadership development in entrepreneurial business is critical to improving women’s participation in management and businesses in Bangladesh. Research shows that only seven percent of all business establishments in Bangladesh are women-owned and women-headed. This limited ownership and development of women’s leadership behavior is a clear gap to be filled. Thus, the study aims to identify women’s leadership behavioral factors (WLBFs) and examine the causal relationship between WLBFs and women’s leadership behavior practices (WLBPs) in line with path–goal leadership theory. We conducted causal research, applying systematic sampling techniques in selecting participants and conducting interviews with 366 women entrepreneurs from the Bangladesh Women Chamber of Commerce and Industries database under seven administrative divisional headquarters. We analyzed data through exploratory factor analysis and structural equation modeling techniques. The results show that the factors internal to women as entrepreneurs (including entrepreneurial attitude, intentions, and workplace learning culture), the factors external to women as entrepreneurs (such as training and education), and sociocultural factors are significantly related to the development of WLBPs. The external organizational behavior context was not significant. WLBPs help develop directive, supportive, participatory, and achievement-oriented leadership practices among women entrepreneurs in Bangladesh. This study suggests that policymakers, implementing managers, training service providers, and women entrepreneurs focus on entrepreneurial attitude, intention, education and skills development training, workplace learning culture, and sociocultural support among women entrepreneurs in Bangladesh.
Successful cyber-attacks are caused by the exploitation of some vulnerabilities in the software and/or hardware that exist in systems deployed in premises or the cloud. Although hundreds of vulnerabilities are discovered every year, only a small fraction of them actually become exploited, thereby there exists a severe class imbalance between the number of exploited and non-exploited vulnerabilities. The open source national vulnerability database, the largest repository to index and maintain all known vulnerabilities, assigns a unique identifier to each vulnerability. Each registered vulnerability also gets a severity score based on the impact it might inflict upon if compromised. Recent research works showed that the cvss score is not the only factor to select a vulnerability for exploitation, and other attributes in the national vulnerability database can be effectively utilized as predictive feature to predict the most exploitable vulnerabilities. Since cybersecurity management is highly resource savvy, organizations such as cloud systems will benefit when the most likely exploitable vulnerabilities that exist in their system software or hardware can be predicted with as much accuracy and reliability as possible, to best utilize the available resources to fix those first. Various existing research works have developed vulnerability exploitation prediction models by addressing the existing class imbalance based on algorithmic and artificial data resampling techniques but still suffer greatly from the overfitting problem to the major class rendering them practically unreliable. In this research, we have designed a novel cost function feature to address the existing class imbalance. We also have utilized the available large text corpus in the extracted dataset to develop a custom-trained word vector that can better capture the context of the local text data for utilization as an embedded layer in neural networks. Our developed vulnerability exploitation prediction models powered by a novel cost function and custom-trained word vector have achieved very high overall performance metrics for accuracy, precision, recall, F1-Score and AUC score with values of 0.92, 0.89, 0.98, 0.94 and 0.97, respectively, thereby outperforming any existing models while successfully overcoming the existing overfitting problem for class imbalance.
Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072.
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