Data mining algorithms have essential methods and rules that can contribute in detecting and preventing various types of network attacks. These methods are utilized with the intrusion detection systems that can be designed and developed preserve the information in organizations from damage. Specifically, the data mining technique allows users to effectively distinguish between normal and malicious traffic with good accuracy. In this paper, a methodology for revealing and detecting (DDOS) network attack was suggested using DM algorithms. The utilized methodology is divided especially into four parts, each part has its own rules, as the following: First one is the pre-processing which consists of three sub-steps: (i) encoding, (ii) log2, and (iii) PCA. Encoding is used by converting the original nominal packets into numeric features. Standardization of data was performed using logarithmic algorithm. Finally the PCA technique is applied eight times for several different features to reduce the dimensions of the dataset. The second stage is an anomaly detection model, (RF) algorithm is implemented for the extraction of data patterns while classification the types of the given features in training step, (NB) algorithm was also used in classifying the data to compare the results of its classification with the results of using the classifier (RF). In the third stage, the outcomes were tested by implementing the already trained datasets. In the fourth stage, the proposed system performance evaluation metrics were collected such as the rates of accuracy, false alarm, detection, precision, and F.measure. MIX dataset were utilized to train and test the proposed model which resulted from merging two datasets (PORTMAP+LDAP), which are used from the CICDDOS2019 datasets, each consisting of several types of attack packets, and benign packets. Several metrics were utilized in the evaluation of the proposed system. The best outcomes were obtained for detection by using the log2 algorithm and PCA technique in the preprocessing step and using (RF)classifier to classify the dataset. the accuracy when using MIX dataset was 99.9764%, the detection rate was 100%, false alarm rate ≍ 0, and the F.measure was 99.9% when PCA = 25.

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The data grid technique evolved largely in sharing the data in multiple geographical stations across different sites to improve the data access and increases the speed of transmission data. The performance and the availability of the resources is taken into account, when a total of sites holding a copy of files, there is a considerable benefit in selecting the best set of replica sites to be cooperated for increasing data transfer job. In this paper, new selecrtion strategy is proposed to reduce the total transfer time of required files. Pincer-Search algorithm is used to explore the common characteristics of sites to select uncongested replica sites.

Johnson's rule is a scheduling method for the sequence of jobs. Its primary goal is to find the perfect sequence of functions to reduce the amount of idle time, and it also reduces the total time required to complete all functions. It is a suitable method for scheduling the purposes of two functions in a specific time-dependent sequence for both functions and where the time factor is the only parameter used in this way. Therefore, it is not suitable for scheduling work for computers network, where there are many factors affecting the completion time such as CPU speed, memory, bandwidth, and size of data. In this research, Johnson's method will adopt by adding many factors that affect the completion time of the work so that it becomes suitable for the site’s job scheduling purposes to reduce the waiting and idle time for a group of jobs.

Load balancing is a critical aspect of managing server resources efficiently and ensuring optimal performance in distributed systems. The Weighted Round Robin (WRR) algorithm is commonly used to allocate incoming requests among servers based on their assigned weights. However, static weights may not reflect the changing demands of servers, leading to imbalanced workloads. To address this issue, this study proposes a dynamic mechanism for assigning weights to servers in the WRR algorithm based on the data rate and incorporates the Least Connection approach for the best result. The dynamic mechanism takes into account the real-time data rate of each server, representing its current load. Servers with higher data rates are assigned higher weights to attract a larger share of incoming requests, while those with lower data rates receive lower weights to manage their loads effectively. This dynamic weight assignment allows the algorithm to adapt to varying workloads and achieve better load balancing across servers. To further refine the distribution of requests, the Least Connection approach is employed to handle tie-breaking situations and for more fairness in distributing the loads. The proposed algorithm is a hybrid of data rate and the Least Connection, it is evaluated through simulations and real-world experiments. The results demonstrate its superiority in achieving improved load balance compared to traditional static-weight WRR algorithms. By dynamically adjusting weights based on data rate and employing the Least Connection approach, the algorithm optimizes server resource usage, minimizes response times, and enhances overall system performance in distributed environments.