Until recently, researchers have utilized and applied various techniques for intrusion detection system (IDS), including DNA encoding and clustering that are widely used for this purpose. In addition to the other two major techniques for detection are anomaly and misuse detection, where anomaly detection is done based on user behavior, while misuse detection is done based on known attacks signatures. However, both techniques have some drawbacks, such as a high false alarm rate. Therefore, hybrid IDS takes advantage of combining the strength of both techniques to overcome their limitations. In this paper, a hybrid IDS is proposed based on the DNA encoding and clustering method. The proposed DNA encoding is done based on the UNSW-NB15 database by dividing the record's attributes into four groups, including State, Protocol, Service, and the rest of the features is Digits. Four DNA characters were used to represent each protocol attribute values. While two DNA characters are used to represent State, Service and Digits attributes values. Then, the clustering method is applied to classify the records into two clusters, either attack or normal. The current experiment results showed that the proposed system has achieved a good detection rate and accuracy results equal to 81.22% and 82.05% respectively. Also, the system achieved fast encoding and clustering time that equal 0.385 seconds and 0.00325 seconds respectively for each record.
The aim of the current study is to identify the level of goal conflict with twelfth-grade students in South Sharqiah/ Sultanate of Oman according to gender and specialization. The study used the descriptive method. A scale of (28) items was developed and divided into six dimensions: time pressure, goal achievement, limit of power, limit of budget, incompatible strategies, and unclear task. To validate the scale, it was piloted (40) students. The scale was administered to a sample of (402) students (209) males in the Governorate of South Sharqiah. The results showed that the conflict level was high in “unclear task”, and an average conflict level in “limit of power”. Other dimensions (goal achievement, time pressure, limit of powe
... Show MoreIn recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc
... Show More. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a
... Show MoreAssociation rules mining (ARM) is a fundamental and widely used data mining technique to achieve useful information about data. The traditional ARM algorithms are degrading computation efficiency by mining too many association rules which are not appropriate for a given user. Recent research in (ARM) is investigating the use of metaheuristic algorithms which are looking for only a subset of high-quality rules. In this paper, a modified discrete cuckoo search algorithm for association rules mining DCS-ARM is proposed for this purpose. The effectiveness of our algorithm is tested against a set of well-known transactional databases. Results indicate that the proposed algorithm outperforms the existing metaheuristic methods.