The rising number of malicious threats on computer networks and Internet services owing to a large number of attacks makes the network security be at incessant risk. One of the predominant network attacks that poses distressing threats to networks security are the brute force attacks. A brute force attack uses a trial and error algorithm to decode encrypted data such as passwords or Data Encryption Standard keys, through exhaustive effort (using brute force) rather than using intellectual strategies. Brute force attacks resemble legitimate network traffic, making it difficult to defend an organization that rely mainly on perimeter-based security solutions a major challenge. For stopping the occurrence of such attacks, several curable steps must be taken. This paper proposes an efficient mechanism for SSH-Brute force network attacks detection based on a supervised deep learning algorithm, Convolutional Neural Network. The model performance was compared with experimental results from 5 classical machine learning algorithms including Naive Bayes, Logistic Regression, Decision Tree, k-Nearest Neighbour, and Support Vector Machine. Four standard metrics namely, Accuracy, Precision, Recall, and the F-measure were used. Results show that the CNN-based model is superior to the traditional machine learning methods with 94.3% accuracy, a precision rate of 92.5%, recall rate of 97.8% and F1-score of 91.8% in terms of the ability to detect SSH-Brute force attacks.
Purpose: The study sought to determine factors influencing knowledge management practices in the commercial banks in KenyaMethodology: The study adopted a descriptive survey research design. The population of 44 commercial banks was identified. A sample of 17 banks was chosen using random sampling. A stratified approach was used to select respondents and a total of 85 respondents were surveyed from five departments in each of the 17 banks. Quantitative statistical techniques were used during the analysis to describe and analyze data. The results of the analysis were presented and interpreted in the form of descriptive statistics, as well as inferential statistics.Results: Regression result indicated that there exists a positive linear relationship between organizational Culture and Knowledge Management practices. Results indicate that there exists a positive linear relationship between organizational structure and Knowledge Management practices. This is evidenced by an odds ratio of 28.988. The relationship is significant as shown by a p value of 0.0113.Results indicated that there was a positive and significant correlation of 0.759 between Information technology and Knowledge management practices. Results indicate that there exists a positive linear relationship between Organization Leadership and Knowledge Management Practices. This is evidenced by a regression coefficient of 125.198. The relationship is significant as shown by a p value of 0.0058.Unique contribution to theory, practice and policy: The study recommends that commercial banks in Kenya should continue investing in leadership as doing so would improve their knowledge management practices. In addition, commercial banks should adopt more flexible structures that support knowledge acquisition, dissemination and storage. The study advocates that the cultural orientation of the organizations should be such that it supports the perception of knowledge management practices. Furthermore, commercial banks should continue investing in Information technology as doing so would improve the knowledge management practices.
Recommender systems have taken over user’s choice to choose the items/services they want from online markets, where lots of merchandise is traded. Collaborative filtering-based recommender systems uses user opinions and preferences. Determination of commonly used attributes that influence preferences used for prediction and subsequent recommendation of unknown or new items to users is a significant objective while developing recommender engines. In conventional systems, study of user behavior to know their dis/like over items would be carried-out. In this paper, presents feature selection methods to mine such preferences through selection of high influencing attributes of the items. In machine learning, feature selection is used as a data pre-processing method but extended its use on this work to achieve two objectives; removal of redundant, uninformative features and for selecting formative, relevant features based on the response variable. The latter objective, was suggested to identify and determine the frequent and shared features that would be preferred mostly by marketplace online users as they express their preferences. The dataset used for experimentation and determination was synthetic dataset. The Jupyter Notebook™ using python was used to run the experiments. Results showed that given a number of formative features, there were those selected, with high influence to the response variable. Evidence showed that different feature selection methods resulted with different feature scores, and intrinsic method had the best overall results with 85% model accuracy. Selected features were used as frequently preferred attributes that influence users’ preferences.
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