Factors Influencing Employees’ Job Motivation: A Case Study of Small and Medium Enterprises in Dong Nai Province

The research explores the factors affecting employees' job motivation in small and medium enterprises in Dong Nai province from June 2019 to June 2020. The results of multiple linear regression analysis showed that there are five factors including the following elements: relationships in the organization; interesting job; Salary, bonus, and benefits; Training and promotion and assessment of job performance affects the working motivation of employees in small and medium-sized enterprises in Dong Nai province with the significance of 5%. In addBesidesstudy results were processed from SPSS 20.0, Amos software. Estimated parameters of the model by the least least-squared with a significance level of 1%. The results are also significant scientific evidence for researchers and policymakers at small and medium enterprises in Dong Nai province to apply research results to human resource development in the future. Based on the five-factor test results, the author proposed the priority governance implications that the author suggested in order of priority, respectively, interesting work, performance evaluation, organization, salary, bonus and welfare regime, and training and promotion.

An Anomaly Based Adaptive Fuzzy Framework for Detecting Network Intrusions

Anomaly based Intrusion detection systems have proved their worth by detecting zero age intrusions but suffers from large number of false alarms mainly because of imprecise definitions of their normal profile or detection models. Building accurate and precise normal profiles or detection models for intrusion detection is a complex process. It is because it involves highly dynamic network behavior, concept drift phenomenon and evolving intrusion patterns. To accommodate these network dynamics in intrusion de- tection models, we require extensive training data-sets. These data sets must contain a uniform distribution of theoretically possible intrusion patterns and normal network traffic instances. Deviation in training data-set with real time network data and skewed class distribution in training data set will result in a biased detection model. Concept drift phenomenon, huge network data, highly imbalance traffic distribution, addition of new applications and abstract boundaries between normal and abnormal behavior has limited the accuracy of generalized detection models or shortened their detection models useful life. Due to these limitations and complexities in building long term intrusion de- tection models, it is proposed in this thesis that instead of building a generalized profile responsible for detecting all the intrusions it is more helpful if short-term profiles are used to detect an intrusion or even a phase of an intrusion active in certain time space. These short term profiles are evolved by changing cost functions according to changed anomaly conditions, current network traffic patterns and security policies. The evolved profiles remain valid for a short period of time in which network dynamics can be as- sumed as piece-wise linear. In this thesis an anomaly based Adaptive SEmi-supervised Evolutionary Security (ASEES) fuzzy framework is proposed. It is based on adaptive distributed and cooperative fuzzy agents which use evolved short-term profiles. These profiles are evolved for different objectives to detect specific intrusions. Evolved pro- files are switched and activated according to current network and anomaly conditions, network security policies and based on forecasted attacks. The ASEES fuzzy framework is tested under two different attacks; DoS attack and viireconnaissance attack i.e. port scan. The results show good detection times and high detection rate due to similarity of the training and testing data-set. The results also shows a performance increase in using short term profiles along with generalize normal profiles for denial of service attacks.